Method and apparatus for evaluating muscle degeneration and storage medium

文档序号：928501 发布日期：2021-03-05 浏览：2次中文

阅读说明：本技术 肌肉衰减症评估方法、评估装置以及存储介质 (Method and apparatus for evaluating muscle degeneration and storage medium ) 是由樋山贵洋佐藤佳州相原贵拓和田健吾滨塚太一松村吉浩于 2020-08-19 设计创作，主要内容包括：提供一种肌肉衰减症评估方法、评估装置以及存储介质。本发明提供的肌肉衰减症评估方法,是基于试验对象的步行动作评估肌肉衰减症的肌肉衰减症评估装置的肌肉衰减症评估方法,获取与试验对象的步行有关的步行数据；根据步行数据,检测试验对象的一条腿的膝关节在一条腿的立脚期的角度、一条腿的膝关节在一条腿的脚悬空期的角度、一条腿的足尖部在立脚期在垂直方向的位移、一条腿的足尖部在脚悬空期在垂直方向的位移、一条腿的脚踝关节在立脚期的角度以及一条腿的脚踝关节在脚悬空期的角度的至少其中之一；利用至少一个步行参数,判断试验对象是否为肌肉衰减症。根据该构成,可以简单且高精度地评估肌肉衰减症。(Provided are a method and an apparatus for evaluating sarcopenia, and a storage medium. A method for evaluating muscular dystrophy, which is a method for evaluating muscular dystrophy based on walking movement of a test subject by a muscular dystrophy evaluation device, acquires walking data relating to walking of the test subject; detecting at least one of an angle of a knee joint of one leg of a test object in a foot standing period of the one leg, an angle of the knee joint of the one leg in a foot suspension period of the one leg, a displacement of a toe part of the one leg in a vertical direction in the foot standing period, a displacement of the toe part of the one leg in the vertical direction in the foot suspension period, an angle of an ankle joint of the one leg in the foot standing period, and an angle of the ankle joint of the one leg in the foot suspension period, according to walking data; and judging whether the test object is the muscular attenuation disease or not by using at least one walking parameter. According to this configuration, muscle degeneration can be evaluated easily and with high accuracy.)

1. A method for evaluating muscular dystrophy, which is a method for evaluating muscular dystrophy based on walking movement of a test subject by a muscular dystrophy evaluation device, comprising the steps of:

acquiring walking data relating to the walking of the test subject;

detecting at least one of an angle of a knee joint of one leg of the test subject during a foothold period of the one leg, an angle of the knee joint of the one leg during a foothold period of the one leg, a displacement of a toe portion of the one leg in a vertical direction during the foothold period, a displacement of the toe portion of the one leg in a vertical direction during the foothold period, an angle of an ankle joint of the one leg during the foothold period, and an angle of the ankle joint of the one leg during the foothold period, based on the walking data;

Determining whether the test subject is sarcopenia using at least one of the angle of the knee joint in the stance phase, the angle of the knee joint in the foot suspension phase, the displacement of the toe portion in the vertical direction in the stance phase, the displacement of the toe portion in the vertical direction in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase.

2. The method of evaluating sarcopenia according to claim 1,

in the detecting, time-series data of the angle of the knee joint during a predetermined period of the foot suspension period is detected,

in the determination, it is determined whether the test subject is the sarcopenia or not using an average value of the time-series data of the angle of the knee joint.

3. The method of evaluating sarcopenia according to claim 2,

when the period from the time when one foot of the test subject lands on the ground to the time when the foot lands on the ground again is expressed as one walking cycle, and the one walking cycle is expressed by 1% to 100%,

The predetermined period is a period of 61% to 100% of the one walking cycle.

4. The method of evaluating sarcopenia according to claim 1,

detecting time-series data of the angle of the knee joint during a predetermined period of the stance phase in the detection,

in the determination, it is determined whether the test subject is the sarcopenia or not using an average value of the time-series data of the angle of the knee joint.

5. The method of evaluating sarcopenia according to claim 4,

the predetermined period is a period of 50% to 60% of the one walking cycle.

6. The method of evaluating sarcopenia according to claim 1,

in the detecting, time-series data of the displacement of the toe portion in a vertical direction during a prescribed period of the footing period is detected,

in the determination, it is determined whether the test subject is the sarcopenia or not using an average value of the time-series data of the displacement of the toe in the vertical direction.

7. The method of evaluating sarcopenia according to claim 6,

the predetermined period is a period of 1% to 60% of the one walking cycle.

8. The method of evaluating sarcopenia according to claim 1,

in the detecting, time-series data of the displacement of the toe portion in a vertical direction during a prescribed period of the foot suspension period is detected,

in the determination, it is determined whether the test subject is the sarcopenia or not using an average value of the time-series data of the displacement of the toe in the vertical direction.

9. The method of evaluating sarcopenia according to claim 8,

the predetermined period is a period of 65% to 70% of the one walking cycle.

10. The method of evaluating sarcopenia according to claim 1,

in the detecting, time-series data of a first angle of the ankle joint in a first period of the foothold period and time-series data of a second angle of the ankle joint in a second period of the foot suspension period are detected,

in the determining, it is determined whether the test subject is the sarcopenia using an average value of the time-series data of the first angle of the ankle joint and an average value of the time-series data of the second angle of the ankle joint.

11. The method of evaluating sarcopenia according to claim 1,

in the detecting, time-series data of the displacement of the toe in a vertical direction during a first period of the stance phase, time-series data of the angle of the knee joint during a second period of the stance phase, time-series data of the angle of the knee joint during a third period of the foot suspension phase, time-series data of the angle of the knee joint during a fourth period of the foot suspension phase are detected,

in the determination, it is determined whether the test subject is the sarcopenia using an average value of the time-series data of the displacement of the toe in the vertical direction in the first period and an average value of the time-series data of the angle of the knee joint in the second period, the third period, and the fourth period.

12. The method of evaluating sarcopenia according to claim 1,

in the detecting, time-series data of the displacement of the toe portion in the vertical direction during a first period of the footfall, time-series data of the angle of the ankle joint during a second period of the footfall, time-series data of the angle of the ankle joint during a third period of the footfall, time-series data of the angle of the ankle joint during a fourth period of the foot suspension period, time-series data of the angle of the ankle joint during a fifth period of the foot suspension period are detected,

in the determining, whether the test subject is the muscular attenuation is determined using an average value of time-series data of the displacement of the toe portion in the vertical direction in the first period and an average value of time-series data of the angle of the ankle joint in the second period, the third period, the fourth period, and the fifth period.

13. The method of evaluating sarcopenia according to claim 1,

in the detecting, time-series data of the angle of the knee joint during a first period of the stance phase, time-series data of the angle of the knee joint during a second period of the foot suspension phase, time-series data of the angle of the knee joint during a third period of the foot suspension phase, time-series data of the angle of the ankle joint during a fourth period of the stance phase, time-series data of the angle of the ankle joint during a fifth period of the foot suspension phase, time-series data of the angle of the ankle joint during a sixth period of the foot suspension phase are detected,

In the determining, whether the test subject is the sarcopenia is determined using an average value of the time-series data of the angles of the knee joint in the first period, the second period, and the third period, and an average value of the time-series data of the angles of the ankle joint in the fourth period, the fifth period, and the sixth period.

14. The method of evaluating sarcopenia according to claim 1,

in the detecting, time-series data of the displacement of the toe portion in a vertical direction during a first period of the stance phase, time-series data of the displacement of the toe portion in a vertical direction during a second period of the stance phase, time-series data of the displacement of the toe portion in a vertical direction during a third period of the foot suspension phase, time-series data of the angle of the knee joint during a fourth period of the stance phase, time-series data of the angle of the knee joint during a fifth period of the stance phase and the foot suspension phase, time-series data of the angle of the ankle joint during a sixth period of the stance phase, time-series data of the angle of the ankle joint during the stance phase and a seventh period of the foot suspension phase are detected,

In the determining, whether the test subject is the sarcopenia is determined using an average value of the time-series data of the displacements of the toe in the vertical direction in the first period, the second period, and the third period, an average value of the time-series data of the angles of the knee joint in the fourth period and the fifth period, and an average value of the time-series data of the angles of the ankle joint in the sixth period and the seventh period.

15. The method of evaluating sarcopenia according to claim 1,

further, it is determined whether or not the test subject is a population ready for sarcopenia which is likely to become sarcopenia in the future, using at least one of the angle of the knee joint in the stance phase, the angle of the knee joint in the foot suspension phase, the displacement of the toe portion in the vertical direction in the stance phase, the displacement of the toe portion in the vertical direction in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase.

16. The method of evaluating sarcopenia according to claim 1,

In the determining, it is determined that the test object is the sarcopenia if an angle of the knee joint in the stance phase is greater than a threshold value, if an angle of the knee joint in the foot suspension phase is greater than a threshold value, if the displacement of the toe portion in the vertical direction in the stance phase is greater than a threshold value, if the displacement of the toe portion in the foot suspension phase in the vertical direction is greater than a threshold value, if an angle of the ankle joint in the stance phase is greater than a threshold value, or if an angle of the ankle joint in the foot suspension phase is greater than a threshold value.

17. The method of evaluating sarcopenia according to claim 1,

in the determination, whether the test object is the sarcopenia is determined by inputting at least one of the detected angle of the knee joint in the stance phase, the angle of the knee joint in the foot suspension phase, the displacement of the toe part in the vertical direction in the stance phase, the displacement of the toe part in the vertical direction in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase, to a prediction model, wherein the prediction model takes as input at least one of the angle of the knee joint in the stance phase, the angle of the knee joint in the foot suspension phase, the displacement of the toe part in the stance phase in the vertical direction, the displacement of the toe part in the foot suspension phase in the vertical direction, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase And a prediction model generated using as an output value whether or not the test subject is the sarcopenia.

18. A muscular attenuation syndrome evaluation device for evaluating muscular attenuation syndrome based on walking motion of a test subject, comprising:

an acquisition unit that acquires walking data relating to the walking of the test subject;

a detection unit configured to detect, based on the walking data, at least one of an angle of a knee joint of one leg of the test subject in a stance phase of the one leg, an angle of the knee joint of the one leg in a foot suspension phase of the one leg, a displacement of a toe portion of the one leg in a vertical direction in the stance phase, a displacement of the toe portion of the one leg in the vertical direction in the foot suspension phase, an angle of an ankle joint of the one leg in the stance phase, and an angle of the ankle joint of the one leg in the foot suspension phase; and the number of the first and second groups,

a determination unit that determines whether the test subject is sarcopenia using at least one of the angle of the knee joint in the stance phase, the angle of the knee joint in the foot suspension phase, the displacement of the toe portion in the vertical direction in the stance phase, the displacement of the toe portion in the vertical direction in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase.

19. A non-transitory computer-readable storage medium storing a sarcopenia evaluation program for evaluating sarcopenia based on a walking motion of a test subject, the program causing a computer to function as:

acquiring walking data relating to the walking of the test subject;

determining whether the test subject is sarcopenia using at least one of the angle of the knee joint in the stance phase, the angle of the knee joint in the foot suspension phase, the displacement of the toe portion in the vertical direction in the stance phase, the displacement of the toe portion in the vertical direction in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase.

Technical Field

The present invention relates to a technique for evaluating sarcopenia based on walking movements of a test subject.

Background

In recent years, techniques for easily estimating body functions have been developed in order to grasp the state of health of the elderly. Especially in the elderly, muscle mass decreases and fat mass increases with age. A state showing a decrease in skeletal muscle mass with age is generally called sarcopenia (sarcopenia). Muscular attenuation is said to be closely related to falls, fractures, bedridden and weakness. For this reason, it is necessary to find the elderly suffering from sarcopenia as early as possible and take countermeasures.

Conventionally, techniques for evaluating cognitive functions or motor functions based on parameters measured from walking performed on a daily basis have been proposed.

For example, a method of evaluating the incidence of an elderly disorder (risk of an elderly disorder) based on a walking parameter measured by walking behavior is disclosed in japanese patent laid-open publication No. 2013-255786.

Further, for example, japanese patent laid-open publication No. 2018-114319 discloses an evaluation method in which the front-rear acceleration, the left-right acceleration, and the up-down acceleration of the test subject during movement are measured by an acceleration sensor attached to the waist of the test subject, and the movement ability is evaluated based on the changes with time of the front-rear acceleration, the left-right acceleration, and the up-down acceleration.

However, the above-described conventional techniques have difficulty in evaluating sarcopenia easily and with high accuracy, and further improvement is required.

Disclosure of Invention

The present invention has been made to solve the above-described problems, and an object of the present invention is to provide a technique for evaluating sarcopenia easily and with high accuracy.

A method for evaluating muscular dystrophy, which is a method for evaluating muscular dystrophy based on walking movement of a test subject, includes the steps of acquiring walking data relating to walking of the test subject; detecting at least one of an angle of a knee joint of one leg of the test subject during a foothold period of the one leg, an angle of the knee joint of the one leg during a foothold period of the one leg, a displacement of a toe portion of the one leg in a vertical direction during the foothold period, a displacement of the toe portion of the one leg in a vertical direction during the foothold period, an angle of an ankle joint of the one leg during the foothold period, and an angle of the ankle joint of the one leg during the foothold period, based on the walking data; determining whether the test subject is sarcopenia using at least one of the angle of the knee joint in the stance phase, the angle of the knee joint in the foot suspension phase, the displacement of the toe portion in the vertical direction in the stance phase, the displacement of the toe portion in the vertical direction in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase.

Drawings

Fig. 1 is a block diagram showing a configuration of a muscle wasting evaluation system according to an embodiment of the present invention.

Fig. 2 is a schematic diagram for explaining the process of extracting bone data from two-dimensional image data according to the present embodiment.

Fig. 3 is a schematic diagram for explaining a walking cycle according to the present embodiment.

Fig. 4 is a flowchart for explaining the muscle wasting evaluation process using the walking motion of the test subject in the present embodiment.

Fig. 5 is a flowchart for explaining the sarcopenia determination process at step S4 in fig. 4.

Fig. 6 is a flowchart for explaining another example of the sarcopenia determination processing of step S4 of fig. 4.

Fig. 7 is a schematic diagram showing the change in angle of the knee joint of one leg in one walking cycle in the present embodiment.

Fig. 8 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia by the prediction model of the present embodiment.

Fig. 9 is a schematic diagram showing ROC curves obtained from the results of determination of healthy subjects and muscle degeneration preliminary population (pre-sarcopenia) by the prediction model according to the present embodiment.

Fig. 10 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia using the prediction model according to the first modification of the present embodiment.

Fig. 11 is a schematic diagram showing an ROC curve obtained from the results of determining healthy subjects and a population ready for sarcopenia using the prediction model according to the first modification of the present embodiment.

Fig. 12 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia using the prediction model of the second modification of the present embodiment.

Fig. 13 is a schematic diagram showing an ROC curve obtained from the results of determining healthy subjects and a population ready for sarcopenia using the prediction model of the second modification of the present embodiment.

Fig. 14 is a schematic diagram showing an average of time-series data of an angle of a knee joint of one leg of test subjects suffering from muscular dystrophy, and an average of time-series data of an angle of a knee joint of one leg of test subjects suffering from healthy persons, in a second modification of the present embodiment.

Fig. 15 is a schematic diagram showing the displacement of the toe portion of one leg in the vertical direction in one walking cycle in the third modification of the present embodiment.

Fig. 16 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia using the prediction model according to the third modification of the present embodiment.

Fig. 17 is a schematic diagram showing an ROC curve obtained from the results of determining healthy subjects and a population ready for sarcopenia using the prediction model of the third modification of the present embodiment.

Fig. 18 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia using the prediction model according to the fourth modification of the present embodiment.

Fig. 19 is a schematic diagram showing an ROC curve obtained from the results of determining healthy subjects and a population ready for sarcopenia using the prediction model of the fourth modification example of the present embodiment.

Fig. 20 is a schematic diagram showing the displacement of the toe portion of one leg in the vertical direction in one walking cycle in the fifth modification of the present embodiment.

Fig. 21 is a schematic diagram showing an ROC curve obtained from the results of determining whether a population is a sarcopenia or a sarcopenia ready population using the prediction model in the fifth modification of the present embodiment.

Fig. 22 is a schematic diagram showing an average of time-series data of vertical displacements of the toe of one leg of test subjects who are populations ready for sarcopenia and muscle degeneration, and an average of time-series data of vertical displacements of the toe of one leg of test subjects who are healthy subjects, in a fifth modification of the present embodiment.

Fig. 23 is a schematic diagram showing a change in angle of an ankle joint of one leg in one walking cycle in a sixth modification of the present embodiment.

Fig. 24 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia using the prediction model according to the sixth modification example of the present embodiment.

Fig. 25 is a schematic diagram showing an ROC curve obtained from the results of determining healthy subjects and a population ready for sarcopenia using the prediction model according to the sixth modification example of the present embodiment.

Fig. 26 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia using the prediction model of the seventh modification example of the present embodiment.

Fig. 27 is a schematic diagram showing an ROC curve obtained from the results of determining healthy subjects and a population ready for sarcopenia using the prediction model of the seventh modification example of the present embodiment.

Fig. 28 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia using the prediction model of the eighth modification of the present embodiment.

Fig. 29 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia using the prediction model of the ninth modification of the present embodiment.

Fig. 30 is a schematic diagram showing an average of the span (stride distances) of one leg of the test subjects suffering from muscular dystrophy and an average of the span of one leg of the test subjects suffering from healthy persons in the ninth modification of the present embodiment.

Fig. 31 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia using the prediction model of the tenth modification of the present embodiment.

Fig. 32 is a schematic diagram showing an ROC curve obtained from the results of determining healthy subjects and a population ready for sarcopenia using the prediction model of the eleventh modification example of the present embodiment.

Fig. 33 is a schematic diagram showing a change in angle of a knee joint of one leg in one walking cycle in a twelfth modification of the present embodiment.

Fig. 34 is a schematic diagram showing an ROC curve obtained from the results of determining whether a population is a sarcopenia or a sarcopenia ready population using the prediction model of the twelfth modification example of the present embodiment.

Fig. 35 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia using the prediction model according to the thirteenth modification example of the present embodiment.

Fig. 36 is a schematic diagram showing an ROC curve obtained from the results of determining healthy subjects and a population ready for sarcopenia using the prediction model of the fourteenth modification example of the present embodiment.

Fig. 37 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia using the prediction model of the fifteenth modification example of the present embodiment.

Fig. 38 is a schematic diagram showing an ROC curve obtained from the results of determining healthy subjects and a population ready for sarcopenia using the prediction model according to the sixteenth modification example of the present embodiment.

Fig. 39 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia using the prediction model of the seventeenth modification of the present embodiment.

Fig. 40 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and muscle weakening preparatory population using the prediction model according to the eighteenth modification of the present embodiment.

Fig. 41 is a schematic diagram showing an example of the evaluation result screen displayed in the present embodiment.

Detailed Description

(basic knowledge of the invention)

The measurement of walking parameters disclosed in japanese patent laid-open publication No. 2013 and 255786 uses a sheet type pressure sensor (sheet type pressure sensor) or a three-dimensional motion analysis system. The sheet type pressure sensor measures pressure distribution during walking and measures walking parameters based on the pressure distribution. The three-dimensional motion analysis system acquires image information obtained by imaging a tag attached to a foot from a plurality of cameras, and measures a walking parameter by analyzing a motion from the image information. However, it takes time and trouble to provide such a chip pressure sensor or a three-dimensional motion analysis system. Therefore, it is difficult to easily evaluate the risk of the aged disorder with the technique disclosed in japanese patent laid-open publication No. 2013 and 255786.

Further, as the walking parameters disclosed in japanese patent laid-open publication No. 2013-255786, two or more selected from the group consisting of a tempo, a stride, a walking ratio, a stride length, a step interval, a walking angle, a toe angle, a left-right difference in stride, a left-right difference in step interval, a left-right difference in walking angle, and a left-right difference in biped support period are used. The walking angle is an angle formed by a straight line connecting the left heel, the right heel and the other heel, and the traveling direction. The toe angle is the angle formed by the line connecting the heel and the toe with the direction of travel. Further, the technique of japanese patent laid-open publication No. 2013-255786 evaluates the risk of an aged disorder selected from at least knee pain, lumbago, urinary incontinence, dementia, and sarcopenia. However, japanese patent laid-open publication No. 2013-255786 does not disclose the evaluation of the risk of the elderly disorder by using walking parameters other than the above, and the evaluation accuracy of the risk of the elderly disorder can be further improved by using other walking parameters.

The mobility evaluation device disclosed in japanese patent laid-open publication No. 2018-114319 evaluates at least one of the front-back balance, the weight shift, and the left-right balance of the test subject during the movement, based on the front-back acceleration, the left-right acceleration, and the up-down acceleration of the test subject during the movement. However, japanese patent laid-open publication No. 2018-114319 does not disclose that the evaluation of sarcopenia is performed using parameters other than the above, and the evaluation accuracy of sarcopenia can be further improved by using other walking parameters.

In order to solve the above problems, a method for evaluating muscular dystrophy according to an aspect of the present invention is a method for evaluating muscular dystrophy by a muscular dystrophy evaluation device that evaluates muscular dystrophy based on walking movement of a test subject, including the steps of acquiring walking data relating to walking of the test subject; detecting at least one of an angle of a knee joint of one leg of the test subject during a foothold period of the one leg, an angle of the knee joint of the one leg during a foothold period of the one leg, a displacement of a toe portion of the one leg in a vertical direction during the foothold period, a displacement of the toe portion of the one leg in a vertical direction during the foothold period, an angle of an ankle joint of the one leg during the foothold period, and an angle of the ankle joint of the one leg during the foothold period, based on the walking data; determining whether the test subject is sarcopenia using at least one of the angle of the knee joint in the stance phase, the angle of the knee joint in the foot suspension phase, the displacement of the toe portion in the vertical direction in the stance phase, the displacement of the toe portion in the vertical direction in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase.

According to this configuration, at least one of the angle of the knee joint of one leg in the stance phase of one leg, the angle of the knee joint of one leg in the foot suspension phase of one leg, the displacement of the toe portion of one leg in the vertical direction in the stance phase, the displacement of the toe portion of one leg in the vertical direction in the foot suspension phase, the angle of the ankle joint of one leg in the stance phase, and the angle of the ankle joint of one leg in the foot suspension phase is used as the parameter relating to the muscular attenuation of the test subject. Walking movements of a subject suffering from sarcopenia tend to differ from walking movements of a subject not suffering from sarcopenia. In this way, by determining whether or not the test subject is sarcopenia using the parameter relating to sarcopenia of the test subject who is walking, it is possible to evaluate sarcopenia of the test subject with high accuracy.

In addition, at least one of the angle of the knee joint of one leg of the test subject walking in the foot standing period of one leg, the angle of the knee joint of one leg in the foot floating period of one leg, the displacement of the toe portion of one leg in the vertical direction in the foot standing period, the displacement of the toe portion of one leg in the vertical direction in the foot floating period, the angle of the ankle joint of one leg in the foot standing period, and the angle of the ankle joint of one leg in the foot floating period can be easily detected from image data obtained by capturing an image of the test subject walking, for example, and a large-sized device is not necessary. Therefore, according to the present configuration, the muscular attenuation of the test subject can be easily evaluated.

In the method for evaluating sarcopenia, the detection may detect time-series data of an angle of the knee joint during a predetermined period of the foot suspension period, and the determination may determine whether or not the test object is sarcopenia using an average value of the time-series data of the angle of the knee joint.

The angle of the knee joint of one leg of the walking test subject during a predetermined period of the foot suspension period of one leg was significantly different between the test subject suffering from sarcopenia and the test subject not suffering from sarcopenia. Therefore, according to the present configuration, by using the average value of the time-series data of the angle of the knee joint of one leg of the test subject walking during the predetermined period of the foot suspension period of one leg, the muscular attenuation of the test subject can be reliably evaluated.

In the method for evaluating sarcopenia, a period from when one foot of the test subject landed to when the other foot landed again may be expressed as one walking cycle, and when the one walking cycle is expressed by 1% to 100%, the predetermined period may be 61% to 100% of the one walking cycle.

According to this configuration, the period from the landing of one foot of the test subject to the landing of the other foot is expressed as one walking cycle, and one walking cycle is expressed by 1% to 100%. In this case, the muscle degeneration of the test subject can be reliably evaluated by using the average value of the time-series data of the angle of the knee joint of one leg during the period of 61% to 100% of one walking cycle.

In the method for evaluating sarcopenia, the time-series data of the angle of the knee joint during the predetermined period of the stance phase may be detected in the detection, and the average value of the time-series data of the angle of the knee joint may be used in the determination to determine whether or not the test object is sarcopenia.

The angle of the knee joint of one leg of a walking test subject in a predetermined period of the stance phase of one leg is significantly different between the test subject suffering from sarcopenia and the test subject not suffering from sarcopenia. Therefore, according to the present configuration, by using the average value of the time-series data of the angle of the knee joint of one leg of the test subject who is walking during the predetermined period of the stance phase of one leg, the muscular attenuation of the test subject can be reliably evaluated.

In the method for evaluating sarcopenia, the time-series data of the displacement of the toe in the vertical direction during the predetermined period of the foothold may be detected, and the average value of the time-series data of the displacement of the toe in the vertical direction may be used to determine whether or not the test object is sarcopenia.

The displacement of the toe of one leg of a walking test subject in the vertical direction during a predetermined period of the stance phase of one leg is significantly different between the test subject suffering from sarcopenia and the test subject not suffering from sarcopenia. Therefore, according to the present configuration, by using the average value of the time-series data of the displacement in the vertical direction of the toe of one leg of the test subject who is walking during the predetermined period of the stance phase of one leg, the muscular attenuation disorder of the test subject can be evaluated reliably.

According to this configuration, the period from the landing of one foot of the test subject to the landing of the other foot is expressed as one walking cycle, and one walking cycle is expressed by 1% to 100%. At this time, the muscular attenuation of the test subject can be reliably evaluated by using the average value of the time-series data of the displacement in the vertical direction of the toe of one leg during 50% to 60% of one walking cycle.

In the method for evaluating sarcopenia, the time-series data of the displacement of the toe in the vertical direction during the predetermined period of the foot suspension period may be detected in the detection, and the determination may be made to determine whether or not the test object is sarcopenia using an average value of the time-series data of the displacement of the toe in the vertical direction.

The displacement of the toe of one leg of a walking test subject in the vertical direction during a prescribed period of the foot suspension period of one leg was significantly different between the test subject suffering from sarcopenia and the test subject not suffering from sarcopenia. Therefore, according to the present configuration, the muscular attenuation of the test subject can be reliably evaluated by using the average value of the time-series data of the vertical displacement of the toe of one leg of the test subject who is walking during the predetermined period of the foot suspension period of one leg.

According to this configuration, the period from the landing of one foot of the test subject to the landing of the other foot is expressed as one walking cycle, and one walking cycle is expressed by 1% to 100%. At this time, the muscular attenuation of the test subject can be reliably evaluated by using the average value of the time-series data of the displacement in the vertical direction of the toe of one leg during 65% to 70% of one walking cycle.

In the method for evaluating sarcopenia, the detection may detect time-series data of a first angle of the ankle joint in a first period of the footfall period and time-series data of a second angle of the ankle joint in a second period of the foot suspension period, and the determination may determine whether the test object is sarcopenia using an average value of the time-series data of the first angle of the ankle joint and an average value of the time-series data of the second angle of the ankle joint.

According to this configuration, by combining the average value of the time-series data of the first angle in the first period of the foot standing period of one leg with the average value of the time-series data of the second angle in the second period of the foot floating period of one leg, the muscular attenuation can be evaluated with higher accuracy than when the average values are used alone.

According to this configuration, by combining the average value of the time-series data of the displacement in the vertical direction in the first period of the stance phase of one leg with the average value of the time-series data of the angle of the knee joint of one leg in the second period of the stance phase of one leg with the average value of the time-series data of the angle of the knee joint of one leg in the third period of the foot suspension phase of one leg with the average value of the time-series data of the angle of the knee joint of one leg in the fourth period of the foot suspension phase of one leg with the average value of the time-series data, the muscular attenuation can be evaluated with higher accuracy than when the average values are used alone.

In the muscle wasting assessment method, the detection may detect time-series data of the displacement of the toe portion in the vertical direction in the first period of the footfall period, time-series data of the angle of the ankle joint in the second period of the footfall period, time-series data of the angle of the ankle joint in the third period of the footfall period, time-series data of the angle of the ankle joint in the fourth period of the foot suspension period, and time-series data of the angle of the ankle joint in the fifth period of the foot suspension period, and the determination may use an average value of the time-series data of the displacement of the toe portion in the vertical direction in the first period, an average value of the time-series data of the angle of the ankle joint in the second period, the third period, the fourth period, and the fifth period, determining whether the subject is the sarcopenia.

According to this configuration, by combining the average value of the time-series data of the displacement in the vertical direction in the first period of the foot standing period of one leg by the toe portion of one leg, the average value of the time-series data of the angle in the second period of the foot standing period of one leg by the ankle joint of one leg, the average value of the time-series data of the angle in the third period of the foot standing period of one leg by the ankle joint of one leg, the average value of the time-series data of the angle in the fourth period of the foot floating period of one leg by the ankle joint of one leg, and the average value of the time-series data of the angle in the fifth period of the foot floating period of one leg by the ankle joint of one leg, muscle degeneration can be evaluated with higher accuracy than by using the average values alone.

In the muscle wasting assessment method, the time-series data of the angle of the knee joint in the first period of the foothold period, the time-series data of the angle of the knee joint in the second period of the foothold period, the time-series data of the angle of the knee joint in the third period of the foothold period, the time-series data of the angle of the ankle joint in the fourth period of the foothold period, the time-series data of the angle of the ankle joint in the fifth period of the foothold period, and the time-series data of the angle of the ankle joint in the sixth period of the foothold period may be detected, and the average value of the time-series data of the angles of the knee joint in the first period, the second period, and the third period may be used for the determination, And determining whether the test subject is the sarcopenia or not by averaging the time-series data of the angles of the ankle joint in the fourth period, the fifth period, and the sixth period.

According to this configuration, by using the average value of the time-series data of the angle of the knee joint of one leg in the first period of the stance phase of one leg, the average value of the time-series data of the angle of the knee joint of one leg in the second period of the foot suspension phase of one leg, and the average value of the time-series data of the angle of the knee joint of one leg in the third period of the foot suspension phase of one leg, the average value of the time-series data of the angle of the ankle joint of one leg in the fourth period of the foot standing period of one leg, the average value of the time-series data of the angle of the ankle joint of one leg in the fifth period of the foot floating period of one leg, and the average value of the time-series data of the angle of the ankle joint of one leg in the sixth period of the foot floating period of one leg can evaluate sarcopenia with higher accuracy than when the average values are used alone.

In the muscle wasting assessment method, the detection may be performed by detecting time-series data of the displacement of the toe portion in the vertical direction during a first period of the stance phase, time-series data of the displacement of the toe portion in the vertical direction during a second period of the stance phase, time-series data of the displacement of the toe portion in the vertical direction during a third period of the foot suspension phase, time-series data of the angle of the knee joint during a fourth period of the stance phase, time-series data of the angle of the knee joint during the stance phase and a fifth period of the foot suspension phase, time-series data of the angle of the ankle joint during a sixth period of the stance phase, time-series data of the angle of the ankle joint during the stance phase and a seventh period of the foot suspension phase, in the determining, whether the test subject is the sarcopenia is determined using an average value of the time-series data of the displacements of the toe in the vertical direction in the first period, the second period, and the third period, an average value of the time-series data of the angles of the knee joint in the fourth period and the fifth period, and an average value of the time-series data of the angles of the ankle joint in the sixth period and the seventh period.

According to this configuration, by using an average value of time-series data of a displacement in the vertical direction of the toe portion of one leg in the first period of the stance period of one leg, an average value of time-series data of a displacement in the vertical direction of the toe portion of one leg in the second period of the stance period of one leg, an average value of time-series data of a displacement in the vertical direction of the toe portion of one leg in the third period of the foot suspension period of one leg, an average value of time-series data of an angle of the knee joint of one leg in the fourth period of the stance period of one leg, an average value of time-series data of an angle of the knee joint of one leg in the stance period of one leg and the fifth period of the foot suspension period, an average value of time-series data of an angle of the ankle joint of one leg in the sixth period of one leg, and an average value of time-series data of an angle of the ankle joint of one leg in the stance period of one leg and the seventh period of the foot suspension period, the sarcopenia can be evaluated with higher accuracy than using the above average values alone.

In the method for evaluating sarcopenia, it may be determined whether the test subject is a population ready for sarcopenia which is likely to become sarcopenia in the future, using at least one of the angle of the knee joint in the stance phase, the angle of the knee joint in the foot suspension phase, the displacement of the toe portion in the vertical direction in the stance phase, the displacement of the toe portion in the vertical direction in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase.

According to this configuration, at least one of the angle of the knee joint of one leg in the stance phase of one leg, the angle of the knee joint of one leg in the foot suspension phase of one leg, the displacement of the toe portion of one leg in the vertical direction in the stance phase, the displacement of the toe portion of one leg in the vertical direction in the foot suspension phase, the angle of the ankle joint of one leg in the stance phase, and the angle of the ankle joint of one leg in the foot suspension phase is used as the parameter relating to the muscular attenuation of the test subject. In the future, there is a tendency that the walking motion of a test subject who is a pre-population for muscular dystrophy may be different from the walking motion of a test subject who is not a pre-population for muscular dystrophy. In this way, by determining whether or not the test subject is a muscle degeneration preliminary population using the parameter relating to muscle degeneration of the test subject who is walking, it is possible to evaluate muscle degeneration of the test subject with high accuracy.

In the method for evaluating sarcopenia, the determination may be made such that the test object is determined to be sarcopenia if the angle of the knee joint in the stance phase is greater than a threshold value, if the angle of the knee joint in the foot suspension phase is greater than a threshold value, if the displacement of the toe portion in the stance phase in the vertical direction is greater than a threshold value, if the displacement of the toe portion in the foot suspension phase in the vertical direction is greater than a threshold value, if the angle of the ankle joint in the stance phase is greater than a threshold value, or if the angle of the ankle joint in the foot suspension phase is greater than a threshold value.

According to this configuration, the test object can be determined to be sarcopenia when the angle of the knee joint of one leg in the foot standing period of one leg is greater than the threshold, when the displacement of the toe portion of one leg in the vertical direction in the foot standing period of one leg is greater than the threshold, when the angle of the ankle joint of one leg in the foot standing period of one leg is greater than the threshold, or when the angle of the ankle joint of one leg in the foot standing period of one leg is greater than the threshold.

Therefore, whether the test object is muscle degeneration can be easily determined by comparing the angle of the knee joint of one leg in the stance phase of one leg, the angle of the knee joint of one leg in the foot suspension phase of one leg, the displacement of the toe portion of one leg in the vertical direction in the stance phase of one leg, the displacement of the toe portion of one leg in the vertical direction in the foot suspension phase of one leg, the angle of the ankle joint of one leg in the stance phase of one leg, or the angle of the ankle joint of one leg in the foot suspension phase of one leg with the threshold value.

In the method for evaluating muscle wasting, the determination may be made by inputting at least one of the detected angle of the knee joint in the stance phase, the angle of the knee joint in the foot-free phase, the displacement of the toe portion in the vertical direction in the stance phase, the displacement of the toe portion in the vertical direction in the foot-free phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot-free phase into a prediction model, and the prediction model may be the angle of the knee joint in the stance phase, the angle of the knee joint in the foot-free phase, the displacement of the toe portion in the vertical direction in the stance phase, and the displacement of the toe portion in the vertical direction in the foot-free phase, And a prediction model which is generated by using at least one of the angle of the ankle joint in the footfall period and the angle of the ankle joint in the foot suspension period as an input value and using whether the test object is the sarcopenia or not as an output value.

According to this configuration, the prediction model is generated using as an input value at least one of an angle of a knee joint of one leg in a foot standing period of one leg, an angle of a knee joint of one leg in a foot suspension period of one leg, a displacement of a toe portion of one leg in a vertical direction in the foot standing period of one leg, a displacement of the toe portion of one leg in the vertical direction in the foot suspension period of one leg, an angle of an ankle joint of one leg in the foot standing period of one leg, and an angle of the ankle joint of one leg in the foot suspension period of one leg, and whether or not the test object is sarcopenia as an output value. Then, at least one of the detected angle of the knee joint of one leg in the foot standing period of one leg, the detected angle of the knee joint of one leg in the foot floating period of one leg, the detected displacement of the toe portion of one leg in the vertical direction in the foot standing period of one leg, the detected displacement of the toe portion of one leg in the vertical direction in the foot floating period of one leg, the detected angle of the ankle joint of one leg in the foot standing period of one leg, and the detected angle of the ankle joint of one leg in the foot floating period of one leg is input to the prediction model, thereby determining whether the test object is sarcopenia. Therefore, by storing the prediction model in advance, it is possible to easily determine whether or not the test object is sarcopenia.

The muscle degeneration evaluation device according to another aspect of the present invention is a muscle degeneration evaluation device for evaluating muscle degeneration based on walking movement of a test subject, and includes an acquisition unit for acquiring walking data relating to walking of the test subject; a detection unit configured to detect, based on the walking data, at least one of an angle of a knee joint of one leg of the test subject in a stance phase of the one leg, an angle of the knee joint of the one leg in a foot suspension phase of the one leg, a displacement of a toe portion of the one leg in a vertical direction in the stance phase, a displacement of the toe portion of the one leg in the vertical direction in the foot suspension phase, an angle of an ankle joint of the one leg in the stance phase, and an angle of the ankle joint of the one leg in the foot suspension phase; a determination unit that determines whether the test subject is sarcopenia using at least one of the angle of the knee joint in the stance phase, the angle of the knee joint in the foot suspension phase, the displacement of the toe portion in the vertical direction in the stance phase, the displacement of the toe portion in the vertical direction in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase.

A storage medium according to another aspect of the present invention is a non-transitory computer-readable storage medium storing a muscular dystrophy evaluation program that evaluates muscular dystrophy based on walking behavior of a test subject, the muscular dystrophy evaluation program causing a computer to function to acquire walking data relating to walking of the test subject; detecting at least one of an angle of a knee joint of one leg of the test subject during a foothold period of the one leg, an angle of the knee joint of the one leg during a foothold period of the one leg, a displacement of a toe portion of the one leg in a vertical direction during the foothold period, a displacement of the toe portion of the one leg in a vertical direction during the foothold period, an angle of an ankle joint of the one leg during the foothold period, and an angle of the ankle joint of the one leg during the foothold period, based on the walking data; determining whether the test subject is sarcopenia using at least one of the angle of the knee joint in the stance phase, the angle of the knee joint in the foot suspension phase, the displacement of the toe portion in the vertical direction in the stance phase, the displacement of the toe portion in the vertical direction in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase.

Embodiments of the present invention will be described below with reference to the drawings. The embodiments described below are specific examples of the present invention, and are not intended to limit the technical scope of the present invention.

(embodiment mode)

The muscle attenuation evaluation system according to the present embodiment will be described below with reference to fig. 1.

Fig. 1 is a block diagram showing a configuration of a muscle attenuation evaluating system according to an embodiment of the present invention.

The sarcopenia evaluation system shown in fig. 1 includes a sarcopenia evaluation device 1, a camera 2, and a display unit 3.

The camera 2 picks up an image of a walking test subject. The camera 2 outputs moving image data representing the walking test subject to the sarcopenia evaluation apparatus 1. The camera 2 is connected to the sarcopenia evaluation apparatus 1 by wire or wirelessly.

The muscle degeneration evaluation device 1 includes a processor 11 and a memory 12.

The processor 11 is, for example, a CPU (central processing unit), and includes a data acquisition unit 111, a walking parameter detection unit 112, a muscle degeneration determination unit 113, and an evaluation result presentation unit 114.

The memory 12 is a storage device such as a ram (random Access memory), a hdd (hard Disk drive), a ssd (solid State drive), or a flash memory, which can store various information.

The data acquisition unit 111 acquires walking data on the walking of the test subject. The walking data is, for example, moving image data obtained by imaging a walking test subject. The data acquisition unit 111 acquires moving image data output by the camera 2.

The walking parameter detection unit 112 extracts bone data representing the bone of the test subject from the moving image data acquired by the data acquisition unit 111. The bone data is expressed by coordinates of a plurality of characteristic points representing joints of the test object and the like and a straight line connecting the characteristic points. The walking parameter detection unit 112 may use software (e.g., opendose or 3D dose-base) that detects coordinates of a feature point of a person from two-dimensional image data.

Here, a process of extracting bone data from two-dimensional image data will be described.

Fig. 2 is a schematic diagram for explaining the process of extracting bone data from two-dimensional image data according to the present embodiment.

The walking parameter detection unit 112 extracts the skeleton data 21 from the two-dimensional image data 20 including the image of the test subject 200 walking. The skeleton data 21 includes a feature point 201 representing the head, a feature point 202 representing the center of the shoulders, a feature point 203 representing the right shoulder, a feature point 204 representing the right elbow, a feature point 205 representing the right hand, a feature point 206 representing the left shoulder, a feature point 207 representing the left elbow, a feature point 208 representing the left hand, a feature point 209 representing the waist, a feature point 210 representing the right femoral joint, a feature point 211 representing the right knee joint, a feature point 212 representing the right ankle joint, a feature point 213 representing the right toe portion, a feature point 214 representing the left femoral joint, a feature point 215 representing the left knee joint, a feature point 216 representing the left ankle joint, and a feature point 217 representing the left toe portion.

The moving image data is composed of a plurality of two-dimensional image data. The walking parameter detection unit 112 extracts time-series skeleton data from each of a plurality of two-dimensional image data constituting moving image data. The walking parameter detection unit 112 may extract the skeleton data from the two-dimensional image data of all frames, or may extract the skeleton data from the two-dimensional image data of each predetermined frame. In the present embodiment, muscle degeneration is evaluated mainly based on the movement of the lower limbs of the test subject during walking. Therefore, the walking parameter detecting unit 112 may extract only the skeletal data of the lower limb of the test subject.

Then, the walking parameter detecting unit 112 extracts the bone data corresponding to one walking cycle of the test subject from the time-series bone data. Wherein the bone data is extracted from the dynamic image data. The walking motion of a person is a periodic motion.

Here, the walking cycle of the test subject will be described.

Fig. 3 is a schematic diagram for explaining a walking cycle according to the present embodiment.

As shown in fig. 3, the period from when one foot of the test subject lands on the ground to when the foot again lands on the ground is shown as one walking cycle. One walking cycle shown in fig. 3 is a period from when the right foot of the test subject lands on the ground to when the right foot again lands on the ground. Also, one walking cycle is normalized to 1% to 100%. The period of 1% to 60% of one walking cycle is referred to as a stance period in which one foot (e.g., the right foot) is falling on the ground, and the period of 61% to 100% of one walking cycle is referred to as a foot-over period in which one foot (e.g., the right foot) is leaving the ground. One gait cycle includes a stance phase and a foot suspension phase. One walking cycle may be a period from the landing of the left foot of the test subject to the landing of the left foot again.

The walking parameter detecting unit 112 detects at least one of an angle of a knee joint of one leg in the foot standing period, an angle of a knee joint of one leg in the foot suspension period, a displacement of a toe portion of one leg in the vertical direction in the foot standing period, a displacement of a toe portion of one leg in the vertical direction in the foot suspension period, an angle of an ankle joint of one leg in the foot standing period, and an angle of an ankle joint of one leg in the foot suspension period, from the walking data.

In the present embodiment, the walking parameter detecting unit 112 detects the angle of the knee joint of one leg of the test subject in the foot suspension period from the walking data. The walking parameter detection unit 112 detects the angle of the knee joint of one leg of the test subject in the foot suspension period from the time-series skeletal data corresponding to one extracted walking cycle. As shown in fig. 2, the angle γ of the knee joint is an angle formed between a straight line connecting the feature point 211 representing the right knee joint and the feature point 210 representing the right femoral joint and a straight line connecting the feature point 211 representing the right knee joint and the feature point 212 representing the right ankle joint on an arrow-shaped plane (virtual plane).

In particular, the walking parameter detecting unit 112 detects time-series data of the angle of the knee joint of one leg in the foot suspension period for a predetermined period in the foot suspension period of one leg. Specifically, the predetermined period is a period of 61% to 100% of one walking cycle. The walking parameter detection unit 112 calculates an average value of time-series data of angles of the knee joint of one leg during the foot suspension period of one leg as a walking parameter.

In addition, the detection of the angle of the knee joint of one leg in the stance phase, the displacement of the toe portion of one leg in the vertical direction in the foot suspension phase, the angle of the ankle joint of one leg in the stance phase, and the angle of the ankle joint of one leg in the foot suspension phase will be described in the modification of the present embodiment.

The muscle attenuation syndrome determination unit 113 determines whether or not the test subject is muscle attenuation syndrome, using at least one of an angle of a knee joint of one leg in a foot standing period, an angle of a knee joint of one leg in a foot suspension period, a displacement of a toe portion of one leg in a vertical direction in the foot standing period, a displacement of a toe portion of one leg in a vertical direction in the foot suspension period, an angle of an ankle joint of one leg in the foot standing period, and an angle of an ankle joint of one leg in the foot suspension period.

In the present embodiment, the sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia using an average value of the time-series data of the angle of the knee joint of one leg in the foot suspension period.

The muscle degeneration determination unit 113 determines whether the test subject is muscle degeneration by inputting at least one of the angle of the knee joint of one leg in the stance phase, the angle of the knee joint of one leg in the foot suspension phase, the displacement of the toe portion of one leg in the vertical direction in the stance phase, the displacement of the toe portion of one leg in the vertical direction in the foot suspension phase, the angle of the ankle joint of one leg in the stance phase, and the angle of the ankle joint of one leg in the foot suspension phase, to a prediction model that takes as output values at least one of the angle of the knee joint of one leg in the stance phase, the angle of the knee joint of one leg in the foot suspension phase, the displacement of the toe portion of one leg in the vertical direction in the stance phase, the displacement of the toe portion of one leg in the foot suspension phase, the angle of the ankle joint of one leg in the stance phase, and the angle of the ankle joint of one leg in the foot suspension phase, whether the test subject is sarcopenia is generated as an output value.

In the present embodiment, the muscle degeneration determination unit 113 determines whether or not the test object is muscle degeneration by inputting the angle of the knee joint of one leg detected by the walking parameter detection unit 112 in the foot suspension phase into a prediction model generated with the angle of the knee joint of one leg in the foot suspension phase as an input value and the test object as an output value.

The determination of the muscular attenuation disorder of the test subject by using the angle of the knee joint of one leg in the stance phase, the displacement of the toe portion of one leg in the vertical direction in the foot suspension phase, the angle of the ankle joint of one leg in the stance phase, and the angle of the ankle joint of one leg in the foot suspension phase will be described in the modification of the present embodiment.

In the present embodiment, the sarcopenia determination unit 113 determines not only whether or not the test subject is sarcopenia. The sarcopenia determination unit 113 further determines whether or not the test object is a muscle sarcopenia preparation population that is likely to become sarcopenia in the future, using at least one of an angle of the knee joint of one leg in the stance phase, an angle of the knee joint of one leg in the foot suspension phase, a displacement of the toe portion of one leg in the vertical direction in the stance phase, an angle of the ankle joint of one leg in the stance phase, and an angle of the ankle joint of one leg in the foot suspension phase. That is, the sarcopenia determination unit 113 determines whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person.

Sarcopenia means a state in which the muscle mass of the test subject is decreased and the strength or physical strength of the muscle is decreased. The muscle decay disease preparation population indicates a state in which the muscle mass of the test subject is decreased, although the strength and physical performance of the muscle are not decreased. The muscle mass is obtained, for example, by measuring the muscle mass of the limb bones of the test subject. When the muscle mass of the bones of the limbs is lower than the threshold value, it can be judged that the muscle mass is decreasing. Also, the strength of the muscle is obtained, for example, by measuring the grip strength of the test subject. In the case where the grip strength is lower than the threshold value, it can be judged that the strength of the muscle is decreasing. Furthermore, physical performance can be obtained, for example, by measuring the walking speed of the test subject. When the walking speed is less than the threshold value, it can be determined that physical performance is decreasing.

The memory 12 stores in advance a prediction model generated by using as input values the angle of the knee joint of one leg in the foot suspension period and using as output values either the muscle degeneration, the muscle degeneration preparatory population, or the healthy person as the test object. The prediction model is a regression model (regression model) in which a target variable is any one of a muscle degeneration, a muscle degeneration preparatory population, and a healthy person, and time-series data of an angle of a knee joint of one leg in a foot suspension period in one walking cycle during a predetermined period is used as an explanatory variable. The prediction model outputs one of a value (for example, 2) indicating that the test subject is sarcopenia, a value (for example, 1) indicating that the test subject is a sarcopenia ready population, and a value (for example, 0) indicating that the test subject is not sarcopenia and that the sarcopenia ready population is a healthy person.

In particular, the sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preparation population, and the healthy subjects is the test subject, using the average value of the time-series data of the angle of the knee joint of one leg in the foot suspension period. Specifically, the sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preliminary population, and the healthy subject is the test subject, using an average value of the time-series data of the angles of the knee joints of one leg during the period of 61% to 100% of one walking cycle.

In addition, the prediction model may be generated by machine learning. Examples of machine learning include teacher learning in which a relationship between input and output is learned using teacher data to which a label (output information) is given to input information, teakless learning in which no label is constructed only from input, half-teacher learning in which a label may be present or absent, and reinforcement learning in which an action of maximizing reward by trial and error learning is performed. Further, as a specific method of machine learning, there are, for example, a neural network (including deep learning using a multilayer neural network), a genetic algorithm, a decision tree, a bayesian network, a Support Vector Machine (SVM), or the like. In the machine learning of the present invention, any one of the above-listed specific examples may be used.

The prediction model may output a value indicating that the test subject is likely to be sarcopenia. The value indicating that the test subject is likely to be sarcopenia is expressed by, for example, 0.0 to 2.0. In this case, for example, the sarcopenia determination unit 113 may determine that the test subject is a healthy person when the value indicating that the test subject is likely to be sarcopenia is 0.5 or less, determine that the test subject is a sarcopenia preparation group when the value indicating that the test subject is likely to be sarcopenia is greater than 0.5 and 1.5 or less, and determine that the test subject is sarcopenia when the value indicating that the test subject is likely to be sarcopenia is greater than 1.5.

The memory 12 may store a first prediction model for determining whether or not the test subject is sarcopenia and a second prediction model for determining whether or not the test subject is a sarcopenia preparation population. In this case, the muscular attenuation syndrome determining unit 113 inputs at least one of an angle of the knee joint of one leg in the stance phase, an angle of the knee joint of one leg in the foot suspension phase, a displacement of the toe portion of one leg in the vertical direction in the stance phase, a displacement of the toe portion of one leg in the vertical direction in the foot suspension phase, an angle of the ankle joint of one leg in the stance phase, and an angle of the ankle joint of one leg in the foot suspension phase to the first prediction model, and determines whether or not the test object is muscular attenuation syndrome. When determining that the test subject is not sarcopenia, the sarcopenia determination unit 113 inputs at least one of the angle of the knee joint of one leg in the stance phase, the angle of the knee joint of one leg in the foot suspension phase, the displacement of the toe portion of one leg in the vertical direction in the stance phase, the displacement of the toe portion of one leg in the vertical direction in the foot suspension phase, the angle of the ankle joint of one leg in the stance phase, and the angle of the ankle joint of one leg in the foot suspension phase to the second prediction model, and determines whether the test subject is a sarcopenia preparation population. The sarcopenia determination unit 113 determines that the test subject is a healthy subject when determining that the test subject is not a sarcopenia preparation group.

The evaluation result presentation unit 114 presents the evaluation result of sarcopenia determined by the sarcopenia determination unit 113. The evaluation result presentation unit 114 outputs the evaluation result determined by the sarcopenia determination unit 113 to the display unit 3. The evaluation result is at least one of information indicating whether or not the test subject determined by the sarcopenia determination unit 113 is sarcopenia and an evaluation message. The evaluation result presentation unit 114 may present an evaluation result indicating whether the test object determined by the sarcopenia determination unit 113 is sarcopenia, a sarcopenia preparation group, or a healthy person.

The display unit 3 displays the evaluation result output from the evaluation result presentation unit 114. The display unit 3 is, for example, a liquid crystal display panel or a light emitting element.

In addition, the display unit 3 may display a graph showing the transition of the value indicating that the test subject is likely to be sarcopenia in order to compare the value indicating that the test subject determined this time is likely to be sarcopenia with the value indicating that the test subject in the past is likely to be sarcopenia. In addition, a value indicating that the test object in the past is likely to be sarcopenia is stored in the memory 12 and can be read from the memory 12.

The muscle degeneration evaluation device 1 may include a camera 2 and a display unit 3. The sarcopenia evaluation apparatus 1 may further include a display unit 3. The sarcopenia evaluation apparatus 1 may also be a personal computer or a server.

Next, the muscle degeneration evaluation process according to the present embodiment will be described with reference to fig. 4.

Fig. 4 is a flowchart for explaining the muscle wasting evaluation process using the walking motion of the test subject in the present embodiment. The flowchart shown in fig. 4 represents the steps of evaluating sarcopenia using the sarcopenia evaluation apparatus 1.

The test subject walks in front of the camera 2. The camera 2 photographs a test subject who is walking. The camera 2 transmits the moving image data of the test subject walking to the muscular attenuation disease evaluation apparatus 1.

First, in step S1, the data acquisition unit 111 acquires moving image data transmitted from the camera 2.

Next, in step S2, the walking parameter detecting unit 112 extracts time-series skeleton data from the dynamic image data.

Next, in step S3, the walking parameter detecting unit 112 detects walking parameters for determining sarcopenia from the time-series skeleton data. Here, the walking parameter in the present embodiment is an average value of time-series data of angles of the knee joint of one leg in the foot suspension period during a predetermined period of the foot suspension period of one leg in one walking cycle. The predetermined period is, for example, a period of 61% to 100% of one walking cycle. In addition, the determination method regarding the walking parameters will be described later.

Next, in step S4, the sarcopenia determination unit 113 executes a sarcopenia determination process of determining whether the test subject is sarcopenia, a sarcopenia preparation group, or a healthy subject using the walking parameters. In addition, the process for determining sarcopenia will be described later.

Next, in step S5, the evaluation result presentation unit 114 outputs the result of the muscle decay determined by the muscle decay determination unit 113 to the display unit 3. The evaluation result of sarcopenia indicates whether the test object is sarcopenia, a sarcopenia preparation population and a healthy person. The evaluation result presentation unit 114 may output not only any of the sarcopenia, the sarcopenia preliminary population, and the healthy person to the display unit 3, but also an evaluation message corresponding to the sarcopenia, the sarcopenia preliminary population, or the healthy person to the display unit 3. The display unit 3 displays the evaluation result of sarcopenia output from the evaluation result presentation unit 114.

Here, the muscle-wasting determination process in step S4 in fig. 4 will be described.

Fig. 5 is a flowchart for explaining the sarcopenia determination process at step S4 in fig. 4.

First, in step S11, the sarcopenia determination unit 113 reads out the prediction model from the memory 12.

Next, in step S12, the sarcopenia determination unit 113 inputs the walking parameters detected by the walking parameter detection unit 112 to the prediction model. The walking parameter in the present embodiment is an average value of time-series data of angles of the knee joint of one leg of the test subject during 61% to 100% of one walking cycle. The sarcopenia determination unit 113 inputs the average value of the time-series data of the angle of the knee joint of one leg of the test subject during the period of 61% to 100% of one walking cycle to the prediction model.

Next, in step S13, the sarcopenia determination unit 113 obtains the result of determining sarcopenia from the prediction model. The sarcopenia determination unit 113 obtains, as a determination result, which of the sarcopenia, the sarcopenia preparation population, and the healthy subject is the test subject from the prediction model.

In the sarcopenia determination process according to the present embodiment, the walking parameters are input to the prediction model generated in advance to determine whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person, but the present invention is not limited to this. In another example of the muscle wasting determination processing according to the present embodiment, the determination as to whether the test object is a muscle wasting disease, a muscle wasting disease preparation group, or a healthy person may be made by comparing the walking parameter with a threshold value stored in advance.

In this case, the memory 12 stores in advance a first threshold value for determining whether the test subject is sarcopenia and a second threshold value for determining whether the test subject is a sarcopenia preparation population. The second threshold is below the first threshold.

The muscle degeneration determination unit 113 may determine that the test object is muscle degeneration when the angle of the knee joint of one leg in the stance phase is greater than the first threshold value, when the angle of the knee joint of one leg in the foot suspension phase is greater than the first threshold value, when the displacement of the toe portion of one leg in the vertical direction in the stance phase is greater than the first threshold value, when the displacement of the toe portion of one leg in the vertical direction in the foot suspension phase is greater than the first threshold value, when the angle of the ankle joint of one leg in the stance phase is greater than the first threshold value, or when the angle of the ankle joint of one leg in the foot suspension phase is greater than the first threshold value.

In the present embodiment, the sarcopenia determination unit 113 may determine that the test subject is sarcopenia when the angle of the knee joint of one leg in the foot suspension period is greater than the first threshold value. The sarcopenia determination unit 113 determines whether or not an average value of the time-series data of the angles of the knee joint of one leg of the test subject during 61% to 100% of one walking cycle is greater than a first threshold value. The sarcopenia determination unit 113 determines that the test subject is sarcopenia when the average value of the time-series data of the angles of the knee joint of one leg of the test subject during the period of 61% to 100% of one walking cycle is greater than the first threshold value.

On the other hand, when the angle of the knee joint of one leg in the stance phase is equal to or less than the first threshold value, the muscular dystrophy determining unit 113 may determine whether the angle of the knee joint of one leg in the stance phase is greater than the second threshold value. In addition, when the angle of the knee joint of one leg in the foot suspension period is equal to or less than the first threshold value, the muscular attenuation syndrome determination unit 113 may determine whether or not the angle of the knee joint of one leg in the foot suspension period is greater than the second threshold value. In addition, when the displacement of the toe portion of one leg in the vertical direction during the stance phase is equal to or less than the first threshold value, the muscular dystrophy determining unit 113 may determine whether the displacement of the toe portion of one leg in the vertical direction during the stance phase is greater than the second threshold value. In addition, when the displacement of the toe of one leg in the vertical direction during the foot-suspended period is equal to or less than the first threshold value, the muscular attenuation syndrome determining unit 113 may determine whether the displacement of the toe of one leg in the vertical direction during the foot-suspended period is greater than the second threshold value. In addition, when the angle of the ankle joint of one leg in the stance phase is equal to or less than the first threshold, the muscular dystrophy determining unit 113 may determine whether or not the angle of the ankle joint of one leg in the stance phase is greater than the second threshold. In addition, when the angle of the ankle joint of one leg in the foot suspension period is equal to or less than the first threshold, the muscular attenuation syndrome determination unit 113 may determine whether or not the angle of the ankle joint of one leg in the foot suspension period is greater than the second threshold.

The sarcopenia determination unit 113 may determine that the test object is a muscle attenuation preparation crowd when the angle of the knee joint of one leg in the stance phase is greater than the second threshold, when the angle of the knee joint of one leg in the foot suspension phase is greater than the second threshold, when the displacement of the toe portion of one leg in the vertical direction in the stance phase is greater than the second threshold, when the displacement of the toe portion of one leg in the vertical direction in the foot suspension phase is greater than the second threshold, when the angle of the ankle joint of one leg in the stance phase is greater than the second threshold, or when the angle of the ankle joint of one leg in the foot suspension phase is greater than the second threshold.

In the present embodiment, the sarcopenia determination unit 113 may determine that the test subject is a sarcopenia preparation group when the angle of the knee joint of one leg in the foot suspension period is greater than the second threshold value. The sarcopenia determination unit 113 determines whether or not the average value of the time-series data of the angles of the knee joint of one leg of the test subject during the period of 61% to 100% of one walking cycle is greater than the second threshold value. The sarcopenia determination unit 113 determines that the test subject is a sarcopenia preparation group when the average value of the time series data of the angles of the knee joint of one leg of the test subject during the period of 61% to 100% of one walking cycle is greater than the second threshold value. On the other hand, the sarcopenia determination unit 113 determines that the test subject is not a sarcopenia preparation group, that is, the test subject is a healthy person, when the average value of the time-series data of the angles of the knee joint of one leg of the test subject in the period of 61% to 100% of one walking cycle is equal to or less than the second threshold value.

Fig. 6 is a flowchart for explaining another example of the sarcopenia determination process of step S4 in fig. 4.

First, in step S21, the muscle degeneration determination unit 113 reads the first threshold value and the second threshold value from the memory 12.

Next, in step S22, the muscle wasting determination unit 113 determines whether or not the walking parameter detected by the walking parameter detection unit 112 is greater than a first threshold value. The walking parameter in the present embodiment is an average value of time-series data of angles of the knee joint of one leg of the test subject during 61% to 100% of one walking cycle. The sarcopenia determination unit 113 determines whether or not the average value of the time-series data of the angles of the knee joint of one leg of the test subject during the period of 61% to 100% of one walking cycle is greater than a first threshold value.

Here, when it is determined that the walking parameter is greater than the first threshold value (yes in step S22), the sarcopenia determination unit 113 determines that the test subject is sarcopenia in step S23.

On the other hand, when determining that the walking parameter is equal to or less than the first threshold value (no in step S22), in step S24, the muscle wasting determination unit 113 determines whether or not the walking parameter detected by the walking parameter detection unit 112 is greater than the second threshold value. The walking parameter in the present embodiment is an average value of time-series data of angles of the knee joint of one leg of the test subject during 61% to 100% of one walking cycle. The sarcopenia determination unit 113 determines whether or not the average value of the time-series data of the angles of the knee joint of one leg of the test subject during the period of 61% to 100% of one walking cycle is greater than the second threshold value.

If it is determined that the walking parameter is greater than the second threshold value (yes at step S24), the sarcopenia determination unit 113 determines that the test subject is a sarcopenia preparation group at step S25.

On the other hand, when determining that the walking parameter is equal to or less than the second threshold value (no in step S24), the sarcopenia determination unit 113 determines that the test subject is not a sarcopenia ready population, that is, the test subject is a healthy subject in step S26.

As described above, in the present embodiment, the angle of the knee joint of one leg of the subject who is walking in the foot suspension period is a parameter related to the muscular attenuation of the subject. Walking movements of the subjects with sarcopenia tend to be different from walking movements of subjects who are not sarcopenia. Therefore, since the muscle degeneration of the test subject is determined using the parameter related to the muscle degeneration of the test subject during walking, the muscle degeneration of the test subject can be evaluated with high accuracy.

Furthermore, since the knee joint of one leg of the walking test subject can be easily detected from the image data obtained by imaging the walking test subject, for example, at the angle of the foot suspension period, a large-sized device is not required. Therefore, according to this configuration, the muscular dystrophy of the test subject can be easily evaluated.

The walking parameters and the prediction model according to the present embodiment can be determined by experiments. The following describes a method of determining walking parameters and a prediction model according to the present embodiment.

The total number of subjects participating in the experiment was 65. All subjects were women. Various criteria for determining sarcopenia exist up to now. The judgment standard of the sarcopenia adopted here is that the skeletal muscle mass of the four limbs is less than 5.8 (kg/m)²) And has a grip strength of less than 19.3(kg) or a skeletal muscle mass of less than 5.8 (kg/m)²) And the walking speed is less than 1.19 (m/s). The limb skeletal muscle mass is a square value obtained by dividing the total muscle mass of both arms and both feet by the height. The muscle mass of the four limbs is less than 5.8 (kg/m)²) And the grip strength is less than 19.3(kg) or the muscle mass of the bones of the limbs is less than 5.8 (kg/m)²) And the walking speed is less than 1.19(m/s), the test object is judged to be the sarcopenia. The reference value used for the above determination is for female subjects, and for male subjects, the skeletal muscle mass in the four limbs is less than 7.0 (kg/m)²) And the grip strength is lower than 30.3(kg) or the muscle mass of the four limbs is lower than 7.0 (kg/m)²) And when the walking speed is less than 1.27(m/s), the test subject is judged to be sarcopenia.

When only the muscle mass of the four limbs is lower than the reference value, the test subject is judged as a muscle degeneration preparation population. That is, the muscle mass of the bones of only the four limbs is less than 5.8 (kg/m)²) In the case of (1), the test subject is judged as a muscle wasting disease preparation population.

The above criteria for determining sarcopenia are merely examples and are not limited to the above numerical values.

As a result of the determination, among the test subjects, there were 9 subjects who were sarcopenia, 30 subjects who were sarcopenia ready groups, and 26 subjects who were healthy persons. In the experiment, the test subjects walked in front of the camera. The test subjects walking are photographed by a camera, and skeletal data of each test subject is extracted from the dynamic image data. Then, time-series data of the angle of the knee joint of one leg of each test subject was detected from the extracted bone data.

Fig. 7 is a schematic diagram showing the change in angle of the knee joint of one leg in one walking cycle in the present embodiment. In fig. 7, the vertical axis represents the angle of the knee joint, and the horizontal axis represents one gait cycle after normalization. In fig. 7, the broken line represents the average waveform of the angle of the knee joint of one leg of the test subjects of healthy persons, the one-dot chain line represents the average waveform of the angle of the knee joint of one leg of the test subjects of the pre-population for muscular attenuation, and the solid line represents the average waveform of the angle of the knee joint of one leg of the test subjects of muscular attenuation.

In the experiment, one gait cycle after normalization was divided into ten intervals, and the average value of the angles of the knee joint of one leg in one interval or two or more consecutive intervals was calculated for each test subject. Then, a prediction model is created in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and the average value of the angles of the knee joint of one leg during 61% to 100% of one walking cycle is used as an explanatory variable. The predictive model was evaluated by cross-validation. Leave-one-out cross-validation is adopted as cross-validation. Then, an ROC (receiver Operating characteristic) curve of the prediction model determined as a healthy person and a muscle degeneration is calculated, and an ROC curve of the prediction model determined as a healthy person and a muscle degeneration preliminary population is calculated. Then, auc (area Under cut) values of the two ROC curves of the prediction model were calculated.

Fig. 8 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and sarcopenia by the prediction model of the present embodiment.

The prediction model of the present embodiment is created by using as an explanatory variable an average value of angles of the knee joint of one leg during a period of 61% to 100% of one walking cycle, which is a target variable of whether a test subject is sarcopenia, a sarcopenia preparation population, or a healthy subject. In fig. 8, the vertical axis represents the True Positive Rate (True Positive Rate), and the horizontal axis represents the False Positive Rate (False Positive Rate). The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in fig. 8 is a curve that depicts the true positive rate and the false positive rate of a prediction model created with the average value of the angles of the knee joint of one leg during 61% to 100% of one walking cycle as an explanatory variable. The AUC value of the ROC curve shown in fig. 8 is 0.699. The AUC value is the area of the lower half of the ROC curve. It is believed that the greater the AUC value (closer to 1) the higher the performance of the prediction model.

Fig. 9 is a schematic diagram showing an ROC curve obtained from the results of determination of healthy subjects and muscle degeneration preliminary population using the prediction model of the present embodiment. In fig. 9, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia preparation population as the sarcopenia preparation population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as the sarcopenia preparation population.

The ROC curve shown in fig. 9 is a curve that depicts the true positive rate and the false positive rate of a prediction model created with the average value of the angles of the knee joint of one leg during 61% to 100% of one walking cycle as an explanatory variable. The AUC value of the ROC curve shown in fig. 9 is 0.604.

In the present embodiment, the average value of the angles of the knee joints of one leg during the period of 61% to 100% of one walking cycle is determined as the walking parameter. A prediction model created using as an explanatory variable the average value of the angles of the knee joints of one leg during the period of 61% to 100% of one walking cycle is determined as the prediction model used by the sarcopenia determination unit 113.

The memory 12 stores in advance a prediction model generated by using as an input value an average value of time-series data of angles of a knee joint of one leg during a period of 61% to 100% of one walking cycle and using as an output value which of the sarcopenia, the sarcopenia preparation population, and the healthy person is the test object. The walking parameter detection unit 112 detects time-series data of the angle of the knee joint of one leg during a period of 61% to 100% of one walking cycle. The sarcopenia determination unit 113 inputs an average value of time series data of angles of the knee joint of one leg during a period of 61% to 100% of one walking cycle to the prediction model, and obtains a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person from the prediction model.

In addition, in a period of 61% to 100% of one walking cycle shown in fig. 7, the average waveform of the angle of the knee joint of one leg of the test subjects as the population ready for the muscular dystrophy is larger than the average waveform of the angle of the knee joint of one leg of the test subjects as the population ready for the muscular dystrophy. Therefore, a value between an average of time-series data of angles of the knee joint of one leg of the test subjects obtained through the experiment during the period of 61% to 100% of one walking cycle and an average of time-series data of angles of the knee joint of one leg of the test subjects of the preliminary population for muscular dystrophy during the period of 61% to 100% of one walking cycle may be stored in the memory 12 as the first threshold. The sarcopenia determination unit 113 may determine whether or not the test subject is sarcopenia by comparing an average value of the time-series data of the angles of the knee joint of one leg of the test subject during the period of 61% to 100% of one walking cycle with a first threshold value stored in advance.

In addition, in the period of 61% to 100% of one walking cycle shown in fig. 7, the average waveform of the angle of the knee joint of one leg of the test subjects as the population ready for muscular attenuation is larger than the average waveform of the angle of the knee joint of one leg of the test subjects as the healthy subjects. Therefore, the value between the average of the time-series data of the angle of the knee joint of one leg of the test subjects obtained through the experiment as the muscle degeneration preparation population during the period of 61% to 100% of one walking cycle and the average of the time-series data of the angle of the knee joint of one leg of the test subjects as the healthy subjects during the period of 61% to 100% of one walking cycle may be stored in the memory 12 as the second threshold. The sarcopenia determination unit 113 may determine whether or not the test subject is a sarcopenia preparation group by comparing an average value of the time-series data of the angles of the knee joint of one leg of the test subject during the period of 61% to 100% of one walking cycle with a second threshold value stored in advance.

In the present embodiment, the walking parameter is an average value of time-series data of angles of the knee joints of one leg during a period of 61% to 100% of one walking cycle, but the present invention is not limited to this. Various examples of the walking parameters in the present embodiment will be described below.

First, the walking parameters of the first modification of the present embodiment will be described.

The walking parameter in the first modification of the present embodiment may be an average value of time-series data of angles of the knee joint of one leg during a predetermined period of the stance phase of one leg of the test subject.

In the first modification of the present embodiment, as in the above-described experiment, time-series data of the angle of the knee joint of each of a plurality of test subjects is detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of the sarcopenia preparatory population, and the test subject of the healthy person. Then, a prediction model is created in which the target variable is any one of sarcopenia, a muscle degeneration preliminary population, and a healthy subject, and the average value of time-series data of the angle of the knee joint of one leg during a predetermined period of the stance phase is used as an explanatory variable. The predetermined period is a period of 1% to 60% of one walking cycle. The predictive model was evaluated by cross validation. Leave-one-out cross-validation is adopted as cross-validation. Then, an ROC curve of the prediction model is calculated. Further, AUC values of ROC curves of the prediction models were calculated.

The prediction model of the first modification of the present embodiment is created by using as an objective variable which one of the muscle degeneration, the muscle degeneration preparatory population, and the healthy person is the test object, and using as an explanatory variable the average value of the angles of the knee joint of one leg during 1% to 60% of one walking cycle. In fig. 10, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in fig. 10 is a curve that depicts the true positive rate and the false positive rate of a prediction model that is created using, as explanatory variables, the average value of the angles of the knee joint of one leg during 1% to 60% of one walking cycle. The AUC value of the ROC curve shown in fig. 10 was 0.586.

In fig. 11, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia preparation population as the sarcopenia preparation population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as the sarcopenia preparation population.

The ROC curve shown in fig. 11 is a curve that depicts the true positive rate and the false positive rate of a prediction model that is created using, as explanatory variables, the average value of the angles of the knee joint of one leg during 1% to 60% of one walking cycle. The AUC value of the ROC curve shown in fig. 11 was 0.537.

In the first modification of the present embodiment, the average value of the angles of the knee joints of one leg during 1% to 60% of one walking cycle is determined as the walking parameter. Then, a prediction model created using as an explanatory variable the average value of the angles of the knee joints of one leg during 1% to 60% of one walking cycle is determined as the prediction model used by the muscle degeneration determination unit 113.

The walking parameter detection unit 112 detects, from the walking data, the angle of the knee joint of one leg during a predetermined period of the stance phase of one leg of the test subject. The walking parameter detection unit 112 detects the angle of the knee joint of one leg in a predetermined period of the stance phase from the time-series skeletal data corresponding to one extracted walking cycle. In particular, the walking parameter detecting unit 112 detects time-series data of the angle of the knee joint in a predetermined period of the stance phase of one leg. Specifically, the predetermined period is a period of 1% to 60% of one walking cycle. The walking parameter detection unit 112 detects time-series data of the angle of the knee joint of one leg during a period of 1% to 60% of one walking cycle. The walking parameter detection unit 112 calculates an average value of time-series data of angles of the knee joint of one leg during a period of 1% to 60% of one walking cycle.

The memory 12 stores a prediction model generated by using as an input value an angle of a knee joint of one leg during 1% to 60% of one walking cycle and using as an output value which of the muscular dystrophy, the muscle degeneration preparatory population, and the healthy person is the test object. The prediction model is a regression model in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and time series data of the angle of the knee joint of one leg during 1% to 60% of one walking cycle is used as an explanatory variable. In particular, the memory 12 stores in advance a prediction model generated by using as an input value an average value of time-series data of angles of the knee joint of one leg during a period of 1% to 60% of one walking cycle and using as an output value a test object which is one of sarcopenia, a muscle degeneration preliminary population, and a healthy person.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of time-series data of angles of the knee joint of one leg during a predetermined period of the stance period. The predetermined period is a period of 1% to 60% of one walking cycle. The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia by inputting the average value of the time-series data of the angle of the knee joint of one leg during the predetermined period of the stance period detected by the walking parameter detection unit 112 to a prediction model which is generated by taking the average value of the time-series data of the angle of the knee joint of one leg during the predetermined period of the stance period as an input value and taking whether or not the test subject is sarcopenia as an output value.

The sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia ready population, and the healthy subject is the test subject, using an average value of the time-series data of the angle of the knee joint during the predetermined period of the stance phase of one leg. The predetermined period is a period of 1% to 60% of one walking cycle. Specifically, the sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preparation group, and the healthy person is the test object, using the average value of the time-series data of the angles of the knee joints of one leg during 1% to 60% of one walking cycle. The sarcopenia determination unit 113 inputs an average value of time series data of angles of the knee joint of one leg during 1% to 60% of one walking cycle to the prediction model, and obtains a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person from the prediction model.

Next, walking parameters of a second modification of the present embodiment will be described.

The walking parameter in the second modification of the present embodiment may be an average value of time-series data of angles of the knee joint of one leg during 50% to 60% of one walking cycle.

In the second modification of the present embodiment, time-series data of the angle of the knee joint of one leg of each of a plurality of test subjects is detected, as in the above-described experiment. Then, a prediction model is created in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and the average value of the angles of the knee joint of one leg during 50% to 60% of one walking cycle is used as an explanatory variable. The predictive model was evaluated by cross-validation. As the cross validation, leave-one-out cross validation was employed. Then, an ROC curve of the prediction model is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model of the second modification of the present embodiment is created by using as an objective variable which one of the muscle degeneration, the muscle degeneration preparatory population, and the healthy person is the test object, and using as an explanatory variable the average value of the angles of the knee joint of one leg during 50% to 60% of one walking cycle. In fig. 12, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in fig. 12 is a curve depicting the true positive rate and the false positive rate of a prediction model created using, as explanatory variables, the average value of the angles of the knee joint of one leg during 50% to 60% of one walking cycle. The AUC value of the ROC curve shown in fig. 12 is 0.6786.

In fig. 13, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia preparation population as the sarcopenia preparation population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as the sarcopenia preparation population.

The ROC curve shown in fig. 13 is a curve depicting the true positive rate and the false positive rate of a prediction model created using, as explanatory variables, the average value of the angles of the knee joint of one leg during 50% to 60% of one walking cycle. The AUC value of the ROC curve shown in fig. 13 is 0.6135.

In a second modification of the present embodiment, the average value of the angles of the knee joints of one leg during 50% to 60% of one walking cycle is determined as the walking parameter. A prediction model created using as an explanatory variable the average value of the angles of the knee joints of one leg during 50% to 60% of one walking cycle is determined as the prediction model used by the muscle degeneration determination unit 113.

The walking parameter detection unit 112 detects time-series data of the angle of the knee joint during a predetermined period of the stance phase of one leg. Specifically, the predetermined period is a period of 50% to 60% of one walking cycle. The walking parameter detection unit 112 detects time-series data of the angle of the knee joint of one leg during 50% to 60% of one walking cycle. The walking parameter detecting unit 112 calculates an average value of time-series data of angles of the knee joint of one leg during 50% to 60% of one walking cycle.

The memory 12 stores in advance a prediction model generated by using as an input value an angle of a knee joint of one leg during a period of 50% to 60% of one walking cycle and using as an output value any of a person to be tested, a person who is a person who. The prediction model is a regression model in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and time series data of the angle of the knee joint of one leg during 50% to 60% of one walking cycle is used as an explanatory variable. In particular, the memory 12 stores in advance a prediction model generated by using as an input value an average value of time-series data of angles of knee joints of one leg during a period of 50% to 60% of one walking cycle and using as an output value a test object which is one of sarcopenia, a muscle degeneration preliminary population, and a healthy person.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of time-series data of angles of the knee joint of one leg during a predetermined period of the stance period. The predetermined period is a period of 50% to 60% of one walking cycle. The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia by inputting the average value of the time-series data of the angle of the knee joint of one leg during the predetermined period of the stance phase detected by the walking parameter detection unit 112 into a prediction model which is generated by taking the average value of the time-series data of the angle of the knee joint of one leg during the predetermined period of the stance phase as an input value and taking whether or not the test subject is sarcopenia as an output value.

The sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia ready population, and the healthy subject is the test subject, using an average value of the time-series data of the angle of the knee joint during the predetermined period of the stance phase of one leg. The predetermined period is a period of 50% to 60% of one walking cycle. Specifically, the sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preparation group, and the healthy person is the test object, using the average value of the time series data of the angles of the knee joints of one leg during 50% to 60% of one walking cycle. The sarcopenia determination unit 113 inputs an average value of time series data of angles of the knee joint of one leg during 50% to 60% of one walking cycle to the prediction model, and obtains a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person from the prediction model.

As shown in fig. 14, the average of the time-series data of the angles of the knee joints of one leg of the test subjects suffering from muscular dystrophy during the period of 50% to 60% of one walking cycle was 15.3 degrees, the average of the time-series data of the angles of the knee joints of one leg of the test subjects suffering from muscular dystrophy pre-population during the period of 50% to 60% of one walking cycle was 12.4 degrees, and the average of the time-series data of the angles of the knee joints of one leg of the test subjects suffering from healthy person during the period of 50% to 60% of one walking cycle was 9.3 degrees.

In this way, the average of the time-series data of the angle of the knee joint of one leg of the test subjects of the sarcopenia is larger than the average of the time-series data of the angle of the knee joint of one leg of the test subjects of the pre-population of sarcopenia during the period of 50% to 60% of one walking cycle. Therefore, a value between an average of time-series data of angles of the knee joint of one leg of the test subjects obtained through the experiment during 50% to 60% of one walking cycle and an average of time-series data of angles of the knee joint of one leg of the test subjects of the muscle degeneration preparatory population during 50% to 60% of one walking cycle can be stored in the memory 12 as the first threshold. The sarcopenia determination unit 113 may determine whether or not the test subject is sarcopenia by comparing an average value of the time-series data of the angles of the knee joint of one leg of the test subject during 50% to 60% of one walking cycle with a first threshold value stored in advance.

In addition, the average of the time-series data of the angle of the knee joint of one leg of the test subjects of the muscle degeneration preliminary population is larger than the average of the time-series data of the angle of the knee joint of one leg of the test subjects of the healthy subjects during 50% to 60% of one walking cycle. For this reason, the value between the average of the time-series data of the angle of the knee joint of one leg of the test subjects obtained by the experiment as the muscle degeneration preparation population during the period of 50% to 60% of one walking cycle and the average of the time-series data of the angle of the knee joint of one leg of the healthy subject during the period of 50% to 60% of one walking cycle may be stored as the second threshold value in the memory 12. The sarcopenia determination unit 113 may determine whether or not the test subject is a sarcopenia preparation group by comparing an average value of the time-series data of the angles of the knee joint of one leg of the test subject during 50% to 60% of one walking cycle with a second threshold value stored in advance.

Next, a walking parameter of a third modification of the present embodiment will be described.

The walking parameter in the third modification of the present embodiment may be an average value of time-series data of the vertical displacement of the toe of one leg during a predetermined period of the stance phase of one leg.

Fig. 15 is a schematic diagram showing the vertical displacement of the toe of one leg in one walking cycle in the third modification of the present embodiment. In fig. 15, the vertical axis represents the displacement of the toe in the vertical direction, and the horizontal axis represents one walking cycle after normalization. In fig. 15, the broken line shows the average waveform of the vertical displacement of the toe of one leg of healthy people, the alternate long and short dash line shows the average waveform of the vertical displacement of the toe of one leg of test subjects who are candidates for the muscular attenuation syndrome, and the solid line shows the average waveform of the vertical displacement of the toe of one leg of test subjects who are candidates for the muscular attenuation syndrome.

In the third modification of the present embodiment, as in the above-described experiment, time series data of the vertical displacement of the toe portion of one leg of each of a plurality of test subjects is detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of a sarcopenia ready population, and the test subject of a healthy person. As shown in fig. 2, the displacement β of the toe in the vertical direction is a displacement in the vertical direction representing the characteristic point 213 of the toe.

In the experiment, one walking cycle after normalization was divided into ten sections, and the average value of the displacement in the vertical direction of the toe of one leg in one section or two or more continuous sections was calculated for each test object. Then, a prediction model is created in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and the average value of the vertical displacement of the toe of one leg during 1% to 60% of one walking cycle is used as an explanatory variable. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of the prediction model for which healthy subjects and sarcopenia are determined and an ROC curve of the prediction model for which healthy subjects and sarcopenia ready population are determined are calculated. Further, AUC values of two ROC curves of the prediction model are calculated respectively.

The prediction model of the third modification of the present embodiment is created using as an explanatory variable an average value of the vertical displacement of the toe of one leg during 1% to 60% of one walking cycle, with which the test object is either sarcopenia or a sarcopenia pre-population, or a healthy subject. In fig. 16, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in fig. 16 is a curve depicting the true positive rate and the false positive rate of a prediction model created using as explanatory variables the average value of the displacement in the vertical direction of the toe of one leg during 1% to 60% of one walking cycle. The AUC value of the ROC curve shown in fig. 16 was 0.636.

In fig. 17, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia preparation population as the sarcopenia preparation population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as the sarcopenia preparation population.

The ROC curve shown in fig. 17 is a curve depicting the true positive rate and the false positive rate of a prediction model created using as explanatory variables the average value of the displacement in the vertical direction of the toe of one leg during 1% to 60% of one walking cycle. The AUC value of the ROC curve shown in fig. 17 is 0.560.

In a third modification of the present embodiment, the average value of the vertical displacement of the toe of one leg during 1% to 60% of one walking cycle is determined as the walking parameter. Then, a prediction model created using as an explanatory variable the average value of the vertical displacement of the toe of one leg during 1% to 60% of one walking cycle is determined as the prediction model used by the muscle-wasting disease determination unit 113.

The walking parameter detecting unit 112 detects the vertical displacement of the toe of one leg of the test subject based on the walking data. The walking parameter detecting unit 112 detects a vertical displacement of the toe of one leg of the test subject from the extracted time-series skeleton data corresponding to one walking cycle. In particular, the walking parameter detecting unit 112 detects time-series data of the vertical displacement of the toe of one leg during a predetermined period of the stance phase of one leg. Specifically, the predetermined period is a period of 1% to 60% of one walking cycle. The walking parameter detecting unit 112 detects time-series data of the vertical displacement of the toe of one leg during 1% to 60% of one walking cycle. The walking parameter detecting unit 112 calculates an average value of time-series data of the vertical displacement of the toe of one leg during 1% to 60% of one walking cycle.

In the third modification of the present embodiment, since one walking cycle is a period from the time when the right foot of the test subject lands on the ground to the time when the right foot again lands on the ground, the walking parameter detecting unit 112 detects the displacement β of the toe portion of the right foot in the vertical direction when the right foot is in the stance period. When one walking cycle is a period from the landing of the left foot of the test subject to the landing of the left foot again, the walking parameter detector 112 may detect the vertical displacement β of the toe of the left foot when the left foot is in the stance period.

The memory 12 stores in advance a prediction model generated by using, as an input value, a displacement of the toe of one leg in the vertical direction during 1% to 60% of one walking cycle, and using, as an output value, any one of sarcopenia, a muscle degeneration preliminary population, and a healthy subject as a test object. The prediction model is a regression model in which a target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and time series data of displacement of the toe of one leg in the vertical direction during 1% to 60% of one walking cycle is used as an explanatory variable. In particular, the memory 12 stores in advance a prediction model generated by using as an input value an average value of time-series data of displacements of the toe of one leg in the vertical direction during 1% to 60% of one walking cycle and using as an output value a test object which is one of sarcopenia, a muscle degeneration preliminary population, and a healthy person.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia using the displacement of the toe of one leg in the vertical direction. The muscle degeneration determination unit 113 determines whether or not the test subject is muscle degeneration by inputting the displacement of the toe of one leg in the vertical direction detected by the walking parameter detection unit 112 into a prediction model, which is generated by using the displacement of the toe of one leg in the vertical direction as an input value and using whether or not the test subject is muscle degeneration as an output value.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of time-series data of the displacement of the toe of one leg in the vertical direction during a predetermined period of the stance phase of one leg. The predetermined period is a period of 1% to 60% of one walking cycle. The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of the time-series data of the vertical displacement of the toe of one leg during 1% to 60% of one walking cycle. The sarcopenia determination unit 113 inputs an average value of time series data of the displacement of the toe of one leg in the vertical direction during 1% to 60% of one walking cycle to the prediction model, and obtains a determination result indicating whether or not the test object is sarcopenia from the prediction model.

The sarcopenia determination unit 113 determines whether the test object is sarcopenia, a sarcopenia preliminary population, or a healthy subject, using an average value of time series data of the displacement of the toe portion of one leg in the vertical direction during a predetermined period of the stance period of one leg. The predetermined period is a period of 1% to 60% of one walking cycle. Specifically, the sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preliminary population, and the healthy subject is the test subject, using an average value of the time-series data of the displacement of the toe of one leg in the vertical direction during 1% to 60% of one walking cycle. The sarcopenia determination unit 113 inputs an average value of time series data of the displacement of the toe of one leg in the vertical direction during 1% to 60% of one walking cycle to the prediction model, and obtains a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person from the prediction model.

Next, a walking parameter of a fourth modification of the present embodiment will be described.

The walking parameter in the fourth modification of the present embodiment may be an average value of time-series data of the displacement of the toe portion of one leg of the test subject in the vertical direction during a predetermined period of the foot suspension period of the one leg.

In the fourth modification of the present embodiment, as in the above-described experiment, time series data of the vertical displacement of the toe portion of one leg of each of a plurality of test subjects is detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of a sarcopenia ready population, and the test subject of a healthy person. Then, a prediction model is created in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and the average value of time-series data of the vertical displacement of the toe of one leg during a predetermined period of the foot suspension period is used as an explanatory variable. The predetermined period is a period of 61% to 100% of one walking cycle. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of the prediction model is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model of the fourth modification of the present embodiment is created using as an explanatory variable an average value of the vertical displacement of the toe of one leg during 61% to 100% of one walking cycle, with which the test object is one of sarcopenia, a muscle degeneration preparatory population, and a healthy person as a target variable. In fig. 18, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in fig. 18 is a curve depicting the true positive rate and the false positive rate of a prediction model created using as explanatory variables the average value of the displacement in the vertical direction of the toe of one leg during 61% to 100% of one walking cycle. The AUC value of the ROC curve shown in fig. 18 is 0.514.

In fig. 19, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia preparation population as the sarcopenia preparation population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as the sarcopenia preparation population.

The ROC curve shown in fig. 19 is a curve depicting the true positive rate and the false positive rate of a prediction model created using as explanatory variables the average value of the displacement in the vertical direction of the toe of one leg during 61% to 100% of one walking cycle. The AUC value of the ROC curve shown in fig. 19 is 0.626.

In a fourth modification of the present embodiment, the average value of the vertical displacement of the toe of one leg during 61% to 100% of one walking cycle is determined as the walking parameter. Then, a prediction model created using as an explanatory variable the average value of the vertical displacement of the toe of one leg during 61% to 100% of one walking cycle is determined as the prediction model used by the muscle-wasting disease determination unit 113.

The walking parameter detecting unit 112 detects, based on the walking data, a displacement of the toe of one leg of the test subject in the vertical direction during a predetermined period of the foot suspension period of the one leg. The walking parameter detecting unit 112 detects a vertical displacement of the toe of one leg in a predetermined period of the foot suspension period from the time-series skeleton data corresponding to one extracted walking cycle. In particular, the walking parameter detecting unit 112 detects time-series data of the vertical displacement of the toe of one leg during a predetermined period of the foot suspension period of one leg. Specifically, the predetermined period is a period of 61% to 100% of one walking cycle. The walking parameter detecting unit 112 detects time-series data of the vertical displacement of the toe of one leg during a period of 61% to 100% of one walking cycle. The walking parameter detecting unit 112 calculates an average value of time-series data of the vertical displacement of the toe of one leg during 61% to 100% of one walking cycle.

The memory 12 stores in advance a prediction model generated by using, as input values, displacements of the toe of one leg in the vertical direction during 61% to 100% of one walking cycle, and as output values, which of the muscle degeneration, the muscle degeneration preparatory population, and the healthy person is the test object. The prediction model is a regression model in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and time series data of displacement of the toe of one leg in the vertical direction during 61% to 100% of one walking cycle is used as an explanatory variable. In particular, the memory 12 stores in advance a prediction model generated by using as an input value an average value of time-series data of displacements of the toe of one leg in the vertical direction during 61% to 100% of one walking cycle and using as an output value either of the muscle degeneration, the muscle degeneration preparatory population, and the healthy person as the test object.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of time-series data of the displacement of the toe of one leg in the vertical direction during a predetermined period of the foot suspension period. The predetermined period is a period of 61% to 100% of one walking cycle. The muscle degeneration determination unit 113 determines whether or not the test object is muscle degeneration by inputting the average value of the time-series data of the vertical displacement of the toe of one leg during the predetermined period of the foot suspension period, which is detected by the walking parameter detection unit 112, to a prediction model that is generated by using the average value of the time-series data of the vertical displacement of the toe of one leg during the predetermined period of the foot suspension period as an input value and using whether or not the test object is muscle degeneration as an output value.

The sarcopenia determination unit 113 determines whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person, using an average value of time series data of the displacement of the toe portion of one leg in the vertical direction during a predetermined period of the foot suspension period of one leg. The predetermined period is a period of 61% to 100% of one walking cycle. Specifically, the sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preliminary population, and the healthy subject is the test subject, using an average value of the time-series data of the displacement of the toe of one leg in the vertical direction during 61% to 100% of one walking cycle. The sarcopenia determination unit 113 inputs an average value of time series data of the displacement of the toe of one leg in the vertical direction during 61% to 100% of one walking cycle to the prediction model, and obtains a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person from the prediction model.

Next, walking parameters of a fifth modification of the present embodiment will be described.

The walking parameter in the fifth modification of the present embodiment may be an average value of time-series data of displacements of the toe of one leg in the vertical direction during 65% to 70% of one walking cycle.

Fig. 20 is a schematic diagram showing the vertical displacement of the toe of one leg in one walking cycle in the fifth modification of the present embodiment. In fig. 20, the vertical axis represents the displacement of the toe in the vertical direction, and the horizontal axis represents one walking cycle after normalization. In fig. 20, the broken line shows the average waveform of the vertical displacement of the toe of one leg of healthy people, and the solid line shows the average waveform of the vertical displacement of the toe of one leg of test subjects who are population candidates for sarcopenia or sarcopenia.

In the fourth modification of the present embodiment, it is determined whether the test subject is a person among a sarcopenia, a sarcopenia ready population, and a healthy person, but in the fifth modification of the present embodiment, it is determined whether the test subject is a sarcopenia or a sarcopenia ready population. In addition, the test subjects that are not sarcopenia or sarcopenia preparation population are judged to be healthy subjects.

In the fifth modification of the present embodiment, time-series data of the vertical displacement of the toe of one leg of each of a plurality of test subjects is detected, similarly to the above-described experiment. Then, a prediction model is created in which whether the test subject is sarcopenia or a muscle degeneration preliminary population is used as a target variable, and an average value of the vertical displacement of the toe of one leg during 65% to 70% of one walking cycle is used as an explanatory variable. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of a prediction model for determining whether the population is a population suffering from sarcopenia or a population ready for sarcopenia is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

Fig. 21 is a schematic diagram showing an ROC curve obtained from the results of determining whether a population is a sarcopenia or a sarcopenia ready population using the prediction model of the fifth modification example of the present embodiment.

The prediction model of the fifth modification of the present embodiment is created using, as an objective variable, whether or not the test subject is sarcopenia or a muscle degeneration preliminary population, and using, as an explanatory variable, an average value of the vertical displacement of the toe of one leg during 65% to 70% of one walking cycle. In fig. 21, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia or sarcopenia preliminary population as sarcopenia or sarcopenia preliminary population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as sarcopenia or sarcopenia preliminary population.

The ROC curve shown in fig. 21 is a curve depicting the true positive rate and the false positive rate of a prediction model created using as explanatory variables the average value of the displacement in the vertical direction of the toe of one leg during 65% to 70% of one walking cycle. The AUC value of the ROC curve shown in fig. 21 is 0.6525.

In a fifth modification of the present embodiment, the average value of the displacement in the vertical direction of the toe of one leg during 65% to 70% of one walking cycle is determined as the walking parameter. Then, a prediction model created using as an explanatory variable the average value of the vertical displacement of the toe of one leg during 65% to 70% of one walking cycle is determined as the prediction model used by the muscle-wasting disease determination unit 113.

The walking parameter detecting unit 112 detects time-series data of the vertical displacement of the toe of one leg during a predetermined period of the foot suspension period of the one leg. Specifically, the predetermined period is a period of 65% to 70% of one walking cycle. The walking parameter detecting unit 112 detects time-series data of the vertical displacement of the toe of one leg during 65% to 70% of one walking cycle. The walking parameter detecting unit 112 calculates an average value of time-series data of the vertical displacement of the toe of one leg during 65% to 70% of one walking cycle.

The memory 12 stores in advance a prediction model generated by using, as input values, displacements of the toe of one leg in the vertical direction during 65% to 70% of one walking cycle, and as output values, whether or not the test subject is a sarcopenia or a sarcopenia preparation population. The prediction model is a regression model in which time series data of displacement in the vertical direction of the toe of one leg during 65% to 70% of one walking cycle is used as an explanatory variable, with the target variable being whether or not the test subject is sarcopenia or a pre-stage sarcopenia. In particular, the memory 12 stores in advance a prediction model generated by using as an input value an average value of time-series data of displacements of the toe of one leg in the vertical direction during 65% to 70% of one walking cycle and as an output value whether or not the test subject is sarcopenia or a muscle degeneration preliminary population.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of time-series data of the displacement of the toe of one leg in the vertical direction during a predetermined period of the foot suspension period. The predetermined period is 65% to 70% of one walking cycle. The muscle degeneration determination unit 113 determines whether or not the test object is muscle degeneration by inputting the average value of the time-series data of the vertical displacement of the toe of one leg during the predetermined period of the foot suspension period, which is detected by the walking parameter detection unit 112, to a prediction model that is generated by using the average value of the time-series data of the vertical displacement of the toe of one leg during the predetermined period of the foot suspension period as an input value and using whether or not the test object is muscle degeneration as an output value.

The sarcopenia determination unit 113 determines whether or not the test subject is a sarcopenia or a sarcopenia preliminary population using an average value of time series data of the displacement of the toe of one leg in the vertical direction during a predetermined period of the foot suspension period of the one leg. The predetermined period is 65% to 70% of one walking cycle. Specifically, the sarcopenia determination unit 113 determines whether or not the test subject is a sarcopenia or a sarcopenia preliminary population using an average value of the time-series data of the displacement of the toe of one leg in the vertical direction during 65% to 70% of one walking cycle. The sarcopenia determination unit 113 inputs an average value of time series data of displacements in the vertical direction of the toe of one leg during 65% to 70% of one walking cycle to the prediction model, and obtains a determination result indicating whether or not the test subject is sarcopenia or a muscle degeneration preparation population from the prediction model.

Fig. 22 is a schematic diagram showing an average of time-series data of vertical displacements of the toe of one leg of test subjects who are population candidates for sarcopenia or sarcopenia and an average of time-series data of vertical displacements of the toe of one leg of test subjects who are healthy subjects, in a fifth modification of the present embodiment.

As shown in fig. 22, the average of the time-series data of the vertical displacement of the toe of one leg of the test subjects who were candidates for the muscular dystrophy or the muscular dystrophy preliminary population was 37mm, and the average of the time-series data of the vertical displacement of the toe of one leg of the test subjects who were healthy subjects was 31 mm.

In this way, the average of the time-series data of the vertical displacement of the toe of one leg of the test subjects who are the population ready for sarcopenia or sarcopenia is larger than the average of the time-series data of the vertical displacement of the toe of one leg of the test subjects who are healthy subjects during 65% to 70% of one walking cycle. Therefore, a value between the average of the time-series data of the vertical displacement of the toe of one leg of the test subjects obtained through the experiment as the population ready for the muscular dystrophy or the sarcopenia in 65% to 70% of one walking cycle and the average of the time-series data of the vertical displacement of the toe of one leg of the test subjects obtained through the experiment in 65% to 70% of one walking cycle can be stored in the memory 12 as the threshold value. The sarcopenia determination unit 113 may determine whether or not the test subject is sarcopenia or a sarcopenia preliminary population by comparing an average value of time series data of displacement in the vertical direction of the toe portion of one leg of the test subject during 65% to 70% of one walking cycle with a threshold value stored in advance.

Next, walking parameters of a sixth modification of the present embodiment will be described.

The walking parameter in the sixth modification of the present embodiment may be an average value of time-series data of angles of ankle joints of one leg during a predetermined period of the stance phase of one leg.

Fig. 23 is a schematic diagram showing a change in angle of an ankle joint of one leg in one walking cycle in a sixth modification of the present embodiment. In fig. 23, the vertical axis represents the angle of the ankle joint, and the horizontal axis represents one walking cycle after normalization. In fig. 23, the dotted line shows the average waveform of the angle of the ankle joint of one leg of a healthy subject, the one-dot chain line shows the average waveform of the angle of the ankle joint of one leg of the test subjects who are candidates for muscular attenuation, and the solid line shows the average waveform of the angle of the ankle joint of one leg of the test subjects who are candidates for muscular attenuation.

In the sixth modification of the present embodiment, as in the above-described experiment, time-series data of the angle of the ankle joint of one leg of each of a plurality of test subjects is detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of the sarcopenia ready population, and the test subject of the healthy person. As shown in fig. 2, the ankle joint angle θ is an angle formed by a straight line connecting a characteristic point 212 representing the right ankle joint and a characteristic point 211 representing the right knee joint and a straight line connecting the characteristic point 212 representing the right ankle joint and a characteristic point 213 representing the right toe portion on an arrow-shaped surface.

In the experiment, one walking cycle after normalization was divided into ten intervals, and the average value of the angles of the ankle joint of one leg in one interval or two or more consecutive intervals was calculated for each test subject. Then, a prediction model is created in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and the average value of the angles of the ankle joint of one leg during 1% to 60% of one walking cycle is used as an explanatory variable. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of the prediction model for which healthy subjects and sarcopenia are determined and an ROC curve of the prediction model for which healthy subjects and sarcopenia ready population are determined are calculated. Further, AUC values of two ROC curves of the prediction model are calculated respectively.

The prediction model of the sixth modification of the present embodiment is created by using as an explanatory variable an average value of angles of the ankle joint of one leg during 1% to 60% of one walking cycle, with which the test object is one of sarcopenia, a muscle degeneration preparatory population, and a healthy person as a target variable. In fig. 24, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in fig. 24 is a curve that depicts the true positive rate and the false positive rate of a prediction model that uses the average value of the angles of the ankle joint of one leg during 1% to 60% of one walking cycle as an explanatory variable. The AUC value of the ROC curve shown in fig. 24 was 0.498.

In fig. 25, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia preparation population as the sarcopenia preparation population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as the sarcopenia preparation population.

The ROC curve shown in fig. 25 is a curve that depicts the true positive rate and the false positive rate of a prediction model that uses the average value of the angles of the ankle joint of one leg during 1% to 60% of one walking cycle as an explanatory variable. The AUC value of the ROC curve shown in fig. 25 is 0.610.

In a sixth modification of the present embodiment, the average value of the angles of the ankle joints of one leg during 1% to 60% of one walking cycle is determined as the walking parameter. A prediction model created using as an explanatory variable the average value of the angles of the ankle joints of one leg during 1% to 60% of one walking cycle is determined as the prediction model used by the muscle attenuation syndrome determination unit 113.

The walking parameter detecting unit 112 detects the angle of the ankle joint of one leg of the test subject in the stance phase of one leg from the walking data. The walking parameter detection unit 112 detects the angle of the ankle joint of one leg of the test subject at the stance phase from the extracted time-series skeleton data corresponding to one walking cycle. In particular, the walking parameter detecting unit 112 detects time-series data of the angle of the ankle joint of one leg during a predetermined period of the stance phase of one leg. Specifically, the predetermined period is a period of 1% to 60% of one walking cycle. The walking parameter detection unit 112 detects time-series data of the angle of the ankle joint of one leg in a period of 1% to 60% of one walking cycle. The walking parameter detecting unit 112 calculates an average value of time-series data of angles of ankle joints of one leg in a period of 1% to 60% of one walking cycle.

In the sixth modification of the present embodiment, since one walking cycle is a period from landing of the right foot of the test subject to landing of the right foot again, the walking parameter detector 112 detects the angle θ of the ankle joint of the right leg. When one walking cycle is a period from the landing of the left foot of the test subject to the landing of the left foot again, the walking parameter detecting unit 112 may detect the angle θ of the ankle joint of the left leg.

The memory 12 stores in advance a prediction model generated by using as an input value an angle of an ankle joint of one leg during a period of 1% to 60% of one walking cycle and using as an output value any of sarcopenia, a sarcopenia preparation group, and a healthy person as a test object. The prediction model is a regression model in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and time series data of the angle of the ankle joint of one leg during 1% to 60% of one walking cycle is used as an explanatory variable. In particular, the memory 12 stores in advance a prediction model generated by using as an input value an average value of time-series data of angles of ankle joints of one leg during a period of 1% to 60% of one walking cycle and using as an output value a test object which is one of sarcopenia, a sarcopenia preparation group, and a healthy person.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using the angle of the ankle joint of one leg in the stance phase. The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia by inputting the angle of the ankle joint of one leg in the stance phase detected by the walking parameter detection unit 112 to a prediction model that is generated by using the angle of the ankle joint of one leg in the stance phase as an input value and using whether or not the test subject is sarcopenia as an output value.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of time-series data of angles of ankle joints of one leg during a predetermined period of the stance phase of one leg. The predetermined period is a period of 1% to 60% of one walking cycle. The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of the time-series data of the angles of the ankle joints of one leg during 1% to 60% of one walking cycle. The sarcopenia determination unit 113 obtains a determination result indicating whether or not the test subject is sarcopenia from the prediction model by inputting the average value of the time-series data of the angle of the ankle joint of one leg during 1% to 60% of one walking cycle to the prediction model.

The sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preparation group, and the healthy person is the test object, using an average value of the time-series data of the angle of the ankle joint of one leg during a predetermined period of the stance phase of one leg. The predetermined period is a period of 1% to 60% of one walking cycle. Specifically, the sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preliminary population, and the healthy subject is the test subject, using an average value of the time-series data of the angles of the ankle joints of one leg during 1% to 60% of one walking cycle. The sarcopenia determination unit 113 inputs an average value of time series data of angles of the ankle joint of one leg during 1% to 60% of one walking cycle to the prediction model, and obtains a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person from the prediction model.

In addition, in the period of 1% to 60% of one walking cycle shown in fig. 23, the average waveform of the angle of the ankle joint of the test subjects as the population ready for muscular dystrophy is larger than the average waveform of the angle of the ankle joint of the test subjects as the population ready for muscular dystrophy. Therefore, a value between the average of the time-series data of the angle of the ankle joint of the test subjects who are candidates for muscular dystrophy during 1% to 60% of one walking cycle and the average of the time-series data of the angle of the ankle joint of the test subjects who are candidates for muscular dystrophy during 1% to 60% of one walking cycle, which are obtained through experiments, may be stored in the memory 12 as the first threshold. The sarcopenia determination unit 113 may determine whether or not the test subject is sarcopenia by comparing an average value of the time-series data of the angles of the one ankle joint of the test subject during 1% to 60% of one walking cycle with a first threshold value stored in advance.

In addition, in the period of 1% to 60% of one walking cycle shown in fig. 23, the average waveform of the angle of the ankle joint of the test subjects as the population ready for muscular attenuation is larger than the average waveform of the angle of the ankle joint of the test subjects as the healthy subjects. For this purpose, a value between the average of the average values of the time-series data of the angles of the ankle joints of the test subjects who are candidates for muscular attenuation obtained through the experiment during 1% to 60% of one walking cycle and the average of the average values of the time-series data of the angles of the ankle joints of the test subjects who are healthy subjects during 1% to 60% of one walking cycle may be stored in the memory 12 as the second threshold. The sarcopenia determination unit 113 may determine whether or not the test subject is a sarcopenia preparation group by comparing an average value of the time-series data of the angles of the one ankle joint of the test subject during 1% to 60% of one walking cycle with a second threshold value stored in advance.

Next, a walking parameter of a seventh modification of the present embodiment will be described.

The walking parameter in the seventh modification of the present embodiment may be an average value of time-series data of angles of ankle joints of one leg during a predetermined period of the foot suspension period of one leg.

In the seventh modification of the present embodiment, as in the above-described experiment, time-series data of the angle of one ankle joint of each of a plurality of test subjects is detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of a sarcopenia ready population, and the test subject of a healthy person.

In the experiment, one walking cycle after normalization was divided into ten intervals, and the average value of the angles of the ankle joint of one leg in one interval or two or more consecutive intervals was calculated for each test subject. Then, a prediction model is created in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and the average value of the angles of the ankle joints of one leg during 61% to 100% of one walking cycle is used as an explanatory variable. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of the prediction model for which healthy subjects and sarcopenia are determined and an ROC curve of the prediction model for which healthy subjects and sarcopenia ready population are determined are calculated. Further, AUC values of two ROC curves of the prediction model are calculated respectively.

The prediction model of the seventh modification of the present embodiment is created by using as an explanatory variable an average value of angles of the ankle joint of one leg during 61% to 100% of one walking cycle, which is a target variable of whether the test object is sarcopenia, a muscle degeneration preparatory population, or a healthy person. In fig. 26, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in fig. 26 is a curve that depicts the true positive rate and the false positive rate of a prediction model created using, as explanatory variables, the average value of the angles of the ankle joints of one leg during 61% to 100% of one walking cycle. The AUC value of the ROC curve shown in fig. 26 is 0.389.

In fig. 27, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia preparation population as the sarcopenia preparation population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as the sarcopenia preparation population.

The ROC curve shown in fig. 27 is a curve that depicts the true positive rate and the false positive rate of a prediction model created using, as explanatory variables, the average value of the angles of the ankle joints of one leg during 61% to 100% of one walking cycle. The AUC value of the ROC curve shown in fig. 27 is 0.622.

In a seventh modification of the present embodiment, the average value of the angles of the ankle joints of one leg during the period of 61% to 100% of one walking cycle is determined as the walking parameter. A prediction model created using as an explanatory variable the average value of the angles of the ankle joints of one leg during 61% to 100% of one walking cycle is determined as the prediction model used by the muscle degeneration determination unit 113.

The walking parameter detecting unit 112 detects the angle of the ankle joint of one leg of the test subject in the foot suspension period of one leg from the walking data. The walking parameter detecting unit 112 detects the angle of the ankle joint of one leg of the test subject in the foot suspension period from the extracted time-series skeleton data corresponding to one walking cycle. In particular, the walking parameter detecting unit 112 detects time-series data of the angle of the ankle joint of one leg during a predetermined period of the foot suspension period of one leg. Specifically, the predetermined period is a period of 61% to 100% of one walking cycle. The walking parameter detection unit 112 detects time-series data of the angle of the ankle joint of one leg in a period of 61% to 100% of one walking cycle. The walking parameter detecting unit 112 calculates an average value of time-series data of angles of ankle joints of one leg in a period of 61% to 100% of one walking cycle.

The memory 12 stores in advance a prediction model generated by using as an input value an angle of an ankle joint of one leg during a period of 61% to 100% of one walking cycle and using as an output value any of sarcopenia, a sarcopenia ready population, and a healthy subject. The prediction model is a regression model in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and time series data of the angle of the ankle joint of one leg during 61% to 100% of one walking cycle is used as an explanatory variable. In particular, the memory 12 stores in advance a prediction model generated by using as an input value an average value of time-series data of angles of ankle joints of one leg during a period of 61% to 100% of one walking cycle and using as an output value a test object which is one of sarcopenia, a sarcopenia preparation group, and a healthy person.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using the angle of the ankle joint of one leg in the foot suspension period. The muscle wasting determination unit 113 determines whether or not the test subject is muscle wasting by inputting the angle of the ankle joint of one leg in the foot suspension period detected by the walking parameter detection unit 112 to a prediction model that is generated by using the angle of the ankle joint of one leg in the foot suspension period as an input value and using whether or not the test subject is muscle wasting as an output value.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of time-series data of angles of ankle joints of one leg during a predetermined period of the foot suspension period of one leg. The predetermined period is a period of 61% to 100% of one walking cycle. The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of the time-series data of the angle of the ankle joint of one leg during a period of 61% to 100% of one walking cycle. The sarcopenia determination unit 113 obtains a determination result indicating whether or not the test subject is sarcopenia from the prediction model by inputting the average value of the time-series data of the angle of the ankle joint of one leg during the period of 61% to 100% of one walking cycle to the prediction model.

The sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preparation group, and the healthy person is the test object, using an average value of the time-series data of the angle of the ankle joint of one leg during a predetermined period of the foot suspension period of one leg. The predetermined period is a period of 61% to 100% of one walking cycle. Specifically, the sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preliminary population, and the healthy subject is the test subject, using an average value of the time-series data of the angles of the ankle joints of one leg during the period of 61% to 100% of one walking cycle. The sarcopenia determination unit 113 inputs an average value of time series data of angles of the ankle joint of one leg during a period of 61% to 100% of one walking cycle to the prediction model, and obtains a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person from the prediction model.

Next, walking parameters of an eighth modification of the present embodiment will be described.

The walking parameter in the eighth modification of the present embodiment may be an average value of time-series data of a first angle of the ankle joint in a first period of the foot standing period of one leg and an average value of time-series data of a second angle of the ankle joint in a second period of the foot floating period of one leg.

In the eighth modification of the present embodiment, as in the above-described experiment, time-series data of the angle of one ankle joint of each of a plurality of test subjects is detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of a sarcopenia ready population, and the test subject of a healthy person. Then, a prediction model is created in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and the average value of the angles of the ankle joint of one leg during 11% to 40% of one walking cycle and the average value of the angles of the ankle joint of one leg during 71% to 80% of one walking cycle are used as explanatory variables. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of the prediction model for judging healthy persons and sarcopenia is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model of the eighth modification of the present embodiment is created by using as the target variables which of the sarcopenia, the sarcopenia ready population, and the healthy subject is the test subject, and using as the explanatory variables the average value of the angles of the ankle joint of one leg during 11% to 40% of one walking cycle and the average value of the angles of the ankle joint of one leg during 71% to 80% of one walking cycle. In fig. 28, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in fig. 28 is a curve that depicts the true positive rate and the false positive rate of a prediction model created using, as explanatory variables, the average value of the angles of the ankle joint of one leg during 11% to 40% of one walking cycle and the average value of the angles of the ankle joint of one leg during 71% to 80% of one walking cycle. The AUC value of the ROC curve shown in fig. 28 is 0.608.

In an eighth modification of the present embodiment, an average value of angles of the ankle joint of one leg during 11% to 40% of one walking cycle and an average value of angles of the ankle joint of one leg during 71% to 80% of one walking cycle are determined as walking parameters. A prediction model created using as explanatory variables the average value of the angles of the ankle joint of one leg during 11% to 40% of one walking cycle and the average value of the angles of the ankle joint of one leg during 71% to 80% of one walking cycle is determined as the prediction model used by the muscle-wasting disease determination unit 113.

The walking parameter detecting unit 112 detects time-series data of a first angle of the ankle joint in a first period of the foot standing period of one leg and time-series data of a second angle of the ankle joint in a second period of the foot floating period of one leg. The first period is a period of 11% to 40% of one walking cycle, and the second period is a period of 71% to 80% of one walking cycle. The walking parameter detecting unit 112 detects time-series data of the angle of the ankle joint of one leg during 11% to 40% of one walking cycle and time-series data of the angle of the ankle joint of one leg during 71% to 80% of one walking cycle. The walking parameter detecting unit 112 calculates an average value of time-series data of angles of the ankle joint of one leg during 11% to 40% of one walking cycle and an average value of time-series data of angles of the ankle joint of one leg during 71% to 80% of one walking cycle.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of the time-series data of the first angle of the ankle joint and an average value of the time-series data of the second angle of the ankle joint.

The memory 12 stores in advance a prediction model generated by using as input values an average value of time-series data of a first angle of an ankle joint in a first period of a foot standing period of one leg and an average value of time-series data of a second angle of the ankle joint in a second period of a foot floating period of one leg, and using as output values either of a person to be tested, and a healthy person. The memory 12 stores in advance a prediction model generated by using as input values an average value of angles of an ankle joint of one leg during 11% to 40% of one walking cycle and an average value of angles of an ankle joint of one leg during 71% to 80% of one walking cycle, and using as an output value which of a person to be tested is sarcopenia, a person who is a.

The sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preparation population, and the healthy subjects is the test subject, using an average value of the time-series data of the angle of the ankle joint of one leg during 11% to 40% of one walking cycle and an average value of the time-series data of the angle of the ankle joint of one leg during 71% to 80% of one walking cycle. The sarcopenia determination unit 113 inputs the average value of the time-series data of the angle of the ankle joint of one leg during 11% to 40% of one walking cycle and the average value of the time-series data of the angle of the ankle joint of one leg during 71% to 80% of one walking cycle to the prediction model, and thereby obtains a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person from the prediction model.

As described above, the AUC value obtained as a result of the sarcopenia was 0.498 in the prediction model created using only the average value of the angle of the ankle joint in the foot standing period, and the AUC value obtained as a result of the sarcopenia was 0.389 in the prediction model created using only the average value of the angle of the ankle joint in the foot suspension period. On the other hand, the AUC value obtained from the result of sarcopenia was judged to be 0.608 from the prediction model created using the average value of the angles of the ankle joint in two periods. Therefore, the prediction model created using the average value of the angles of the ankle joint in two periods can determine sarcopenia with high accuracy, compared to the prediction model created using only the average value of the angles of the ankle joint in one period.

Next, walking parameters of a ninth modification of the present embodiment will be described.

The walking parameter in the ninth modification of the present embodiment may be the span of one leg.

In the ninth modification of the present embodiment, as in the above-described experiment, the span of one leg of each of the plurality of test subjects is detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of the sarcopenia ready population, and the test subject of the healthy person. The span is the distance from the point where one heel falls to the ground to the point where one heel falls again. When one walking cycle is a period from the landing of the right foot of the test subject to the landing of the right foot again, the span is a distance from a point where the heel of the right foot lands to a point where the heel of the right foot lands again.

In the experiment, the span of one leg was calculated for each test subject. Then, a prediction model is created in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and the span of one leg in one walking cycle is used as an explanatory variable. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of the prediction model for judging healthy persons and sarcopenia is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model of the ninth modification of the present embodiment is created with a target variable being any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject as a test target and a span of one leg in one walking cycle as an explanatory variable. In fig. 29, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in fig. 29 is a curve that depicts the true positive rate and the false positive rate of a prediction model generated using the span of one leg in one walking cycle as an explanatory variable. The AUC value of the ROC curve shown in fig. 29 is 0.6677.

In a ninth modification of the present embodiment, the span of one leg in one walking cycle is determined as a walking parameter. Then, a prediction model created using the span of one leg in one walking cycle as an explanatory variable is determined as the prediction model used by the muscle-wasting disease determination unit 113.

The walking parameter detection unit 112 detects the span of one leg of the test subject from the walking data. The walking parameter detection unit 112 detects the span of one leg of the test subject from the extracted time-series skeleton data corresponding to one walking cycle.

In the ninth modification of the present embodiment, since one walking cycle is a period from the time when the right foot of the test subject lands on the ground to the time when the right foot again lands on the ground, the walking parameter detecting unit 112 detects a span from a point where the heel of the right foot lands on the ground to a point where the heel of the right foot again lands on the ground. When one walking cycle is a period from the landing of the left foot to the landing of the left foot again, the walking parameter detector 112 may detect a span from a point where the heel of the left foot lands to a point where the heel of the left foot lands again. The walking parameter detection unit 112 may detect a plurality of strides in a plurality of walking cycles, and calculate an average value of the plurality of detected strides.

The memory 12 stores in advance a prediction model generated using the span of one leg in one walking cycle as an input value and using any one of the subjects of sarcopenia, a muscle degeneration preliminary population, and a healthy subject as an output value. The prediction model is a regression model in which the target variable is any one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject, and the span of one leg in one walking cycle is used as an explanatory variable.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia using the span of one leg in one walking cycle. The muscle wasting determination unit 113 determines whether or not the test object is muscle wasting by inputting the span of one leg in one walking cycle detected by the walking parameter detection unit 112 to a prediction model that is generated by using the span of one leg in one walking cycle as an input value and using whether or not the test object is muscle wasting as an output value. Then, the sarcopenia determination unit 113 inputs the span of one leg in one walking cycle into the prediction model, and obtains a determination result indicating whether or not the test subject is sarcopenia from the prediction model.

The sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia ready population, and the healthy subject is the test subject, using the span of one leg in one walking cycle. The sarcopenia determination unit 113 inputs the span of one leg in one walking cycle to the prediction model, and obtains a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person from the prediction model.

Fig. 30 is a schematic diagram showing the average of the span of one leg of the test subjects suffering from sarcopenia and the average of the span of one leg of the test subjects suffering from healthy persons in the ninth modification of the present embodiment.

As shown in fig. 30, the average of the span of one leg in one walking cycle of the test subjects suffering from sarcopenia was 1.28m, and the average of the span of one leg in one walking cycle of the test subjects suffering from healthy subjects was 1.39 m.

In this way, the average of the span of one leg of the test subjects as the muscular attenuation disease is smaller than the average of the span of one leg of the test subjects as the healthy subjects in one walking cycle. For this purpose, a value obtained by an experiment between the average of the spans of one leg of the test subjects in one walking cycle, which is a muscle wasting disease, and the average of the spans of one leg of the test subjects in one walking cycle, which is a healthy subject, may be stored in the memory 12 as a threshold value. The sarcopenia determination unit 113 may determine whether or not the test subject is sarcopenia by comparing the span of one leg of the test subject in one walking cycle with a threshold value stored in advance. The sarcopenia determination unit 113 may determine that the test subject is sarcopenia when the span of one leg is smaller than the threshold value.

Next, walking parameters of a tenth modification of the present embodiment will be described.

The walking parameters in the tenth modification of the present embodiment may be an average value of time-series data of the vertical displacement of the toe of one leg during the first period of the stance phase of one leg, an average value of time-series data of the angle of the knee joint of one leg during the second period of the stance phase of one leg, an average value of time-series data of the angle of the knee joint of one leg during the third period of the foot suspension phase of one leg, and an average value of time-series data of the angle of the knee joint of one leg during the fourth period of the foot suspension phase of one leg.

In the tenth modification of the present embodiment, as in the above-described experiment, time-series data of the displacement in the vertical direction of the toe of one leg of each of the plurality of test subjects and time-series data of the angle of the knee joint of one leg of each of the plurality of test subjects are detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of the sarcopenia preparatory group, and the test subject of the healthy person. In the experiment, one walking cycle after normalization was divided into ten intervals, and the average value of the displacement in the vertical direction of the toe of one leg in one interval or two or more continuous intervals and the average value of the angle of the knee joint of one leg in one interval or two or more continuous intervals were calculated for each test object.

The prediction model is created using as an explanatory variable an average value of time-series data of a vertical displacement of the toe of one leg in the first period of the stance phase, an average value of time-series data of an angle of the knee joint of one leg in the second period of the stance phase, an average value of time-series data of an angle of the knee joint of one leg in the third period of the foot suspension phase, and an average value of time-series data of an angle of the knee joint of one leg in the fourth period of the foot suspension phase, as a target variable. The first period is a period of 1% to 30% of one walking cycle, the second period is a period of 1% to 50% of one walking cycle, the third period is a period of 61% to 70% of one walking cycle, and the fourth period is a period of 81% to 100% of one walking cycle. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of the prediction model for judging healthy persons and sarcopenia is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model according to the tenth modification of the present embodiment is created by using as objective variables which of the muscle wasting disease, the muscle wasting disease preparation population, and the healthy subject is the test subject, and using as explanatory variables the average value of the vertical displacement of the toe of one leg during 1% to 30% of one walking cycle, and the average value of the angle of the knee joint of one leg during 1% to 50%, 61% to 70%, and 81% to 100% of one walking cycle. In fig. 31, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in fig. 31 is a curve that depicts the true positive rate and the false positive rate of a prediction model created with the average value of the displacement of the toe of one leg in the vertical direction during 1% to 30% of one walking cycle, the average value of the angle of the knee joint of one leg during 1% to 50% of one walking cycle, the average value of the angle of the knee joint of one leg during 61% to 70% of one walking cycle, and the average value of the angle of the knee joint of one leg during 81% to 100% of one walking cycle as explanatory variables. The AUC value of the ROC curve shown in fig. 31 is 0.790.

In a tenth modification of the present embodiment, an average value of the displacement in the vertical direction of the toe portion of one leg during 1% to 30% of one walking cycle, an average value of the angle of the knee joint of one leg during 1% to 50% of one walking cycle, an average value of the angle of the knee joint of one leg during 61% to 70% of one walking cycle, and an average value of the angle of the knee joint of one leg during 81% to 100% of one walking cycle are determined as walking parameters. Then, a prediction model created by using as explanatory variables the average value of the displacement in the vertical direction of the toe of one leg during 1% to 30% of one walking cycle, the average value of the angle of the knee joint of one leg during 1% to 50% of one walking cycle, the average value of the angle of the knee joint of one leg during 61% to 70% of one walking cycle, and the average value of the angle of the knee joint of one leg during 81% to 100% of one walking cycle is determined as the prediction model used by the muscle degeneration determination unit 113.

The walking parameter detecting unit 112 detects time-series data of a vertical displacement of the toe of one leg in the first period of the stance phase, time-series data of an angle of the knee joint of one leg in the second period of the stance phase, time-series data of an angle of the knee joint of one leg in the third period of the foot suspension phase, and time-series data of an angle of the knee joint of one leg in the fourth period of the foot suspension phase. The first period is a period of 1% to 30% of one walking cycle, the second period is a period of 1% to 50% of one walking cycle, the third period is a period of 61% to 70% of one walking cycle, and the fourth period is a period of 81% to 100% of one walking cycle. The walking parameter detecting unit 112 detects time-series data of a vertical displacement of a toe portion of one leg during a period of 1% to 30% of one walking cycle, time-series data of an angle of a knee joint of one leg during a period of 1% to 50% of one walking cycle, time-series data of an angle of a knee joint of one leg during a period of 61% to 70% of one walking cycle, and time-series data of an angle of a knee joint of one leg during a period of 81% to 100% of one walking cycle. The walking parameter detecting unit 112 calculates an average value of time-series data of the vertical displacement of the toe of one leg during 1% to 30% of one walking cycle, an average value of time-series data of the angle of the knee joint of one leg during 1% to 50% of one walking cycle, an average value of time-series data of the angle of the knee joint of one leg during 61% to 70% of one walking cycle, and an average value of time-series data of the angle of the knee joint of one leg during 81% to 100% of one walking cycle.

The memory 12 stores in advance a prediction model generated by using as input values an average value of time-series data of a vertical displacement of a toe of one leg in a first period of a stance phase, an average value of time-series data of an angle of a knee joint of one leg in a second period of the stance phase, an average value of time-series data of an angle of the knee joint of one leg in a third period of a foot suspension phase, and an average value of time-series data of an angle of the knee joint of one leg in a fourth period of the foot suspension phase, and using as output values either one of a muscle degeneration disease, a muscle degeneration disease preparation population, and a healthy subject to be tested. The memory 12 stores in advance a prediction model generated by using as input values an average value of time-series data of a displacement in a vertical direction of a toe of one leg during a period of 1% to 30% of one walking cycle, an average value of time-series data of an angle of a knee joint of one leg during a period of 1% to 50% of one walking cycle, an average value of time-series data of an angle of a knee joint of one leg during a period of 61% to 70% of one walking cycle, and an average value of time-series data of an angle of a knee joint of one leg during a period of 81% to 100% of one walking cycle, and using as an output value any one of a muscle degeneration disease, a muscle degeneration disease preparatory population, and a healthy subject as a test object.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of time-series data of the vertical displacement of the toe of one leg in the first period and an average value of time-series data of the angle of the knee joint of one leg in the second period, the third period, and the fourth period.

The sarcopenia determination unit 113 determines whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person using an average value of time-series data of displacements in the vertical direction during 1% to 30% of one walking cycle of the toe portion of one leg, an average value of time-series data of angles during 1% to 50% of one walking cycle of the knee joint of one leg, an average value of time-series data of angles during 61% to 70% of one walking cycle of the knee joint of one leg, and an average value of time-series data of angles during 81% to 100% of one walking cycle of the knee joint of one leg. The sarcopenia determination unit 113 obtains a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation group, or a healthy person from the prediction model by inputting, to the prediction model, an average value of time-series data of displacements in the vertical direction during 1% to 30% of one walking cycle of the toe portion of one leg, an average value of time-series data of angles during 1% to 50% of one walking cycle of the knee joint of one leg, an average value of time-series data of angles during 61% to 70% of one walking cycle of the knee joint of one leg, and an average value of time-series data of angles during 81% to 100% of one walking cycle of the knee joint of one leg.

As described above, the AUC value obtained as a result of the muscular attenuation was 0.636 from the prediction model created using only the displacement of the toe portion of one leg in the vertical direction at the stance phase, 0.586 from the prediction model created using only the angle of the knee joint of one leg at the stance phase, and 0.699 from the prediction model created using only the angle of the knee joint of one leg at the foot suspension phase. In response, the AUC value obtained as a result of the muscular dystrophy was determined to be 0.790 from a prediction model created using the displacement of the toe of one leg in the vertical direction during the stance phase, the angle of the knee joint of one leg in the stance phase, and the angle of the knee joint of one leg in the foot suspension phase.

Therefore, the muscle degeneration can be determined with high accuracy by the prediction model created using the displacement of the toe portion of one leg in the vertical direction during the stance phase, the angle of the knee joint of one leg in the stance phase, and the angle of the knee joint of one leg in the foot suspension phase, as compared with the prediction model created using only the displacement of the toe portion of one leg in the vertical direction during the stance phase, the angle of the knee joint of one leg in the stance phase, and the angle of the knee joint of one leg in the foot suspension phase, respectively.

Next, walking parameters of an eleventh modification of the present embodiment will be described.

The walking parameters in the eleventh modification of the present embodiment may be an average value of time-series data of a displacement of the toe of one leg in the vertical direction during the first period of the stance phase of one leg, an average value of time-series data of an angle of the knee joint of one leg during the second period of the stance phase of one leg, an average value of time-series data of an angle of the knee joint of one leg during the third period of the stance phase of one leg, and an average value of time-series data of an angle of the knee joint of one leg during the fourth period of the foot suspension phase of one leg.

In the eleventh modification of the present embodiment, as in the above-described experiment, time-series data of the displacement in the vertical direction of the toe portion of one leg of each of the plurality of test subjects and time-series data of the angle of the knee joint of one leg of each of the plurality of test subjects are detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of the sarcopenia preparatory group, and the test subject of the healthy person. In the experiment, one walking cycle after normalization was divided into ten intervals, and the average value of the displacement in the vertical direction of the toe of one leg in one interval or two or more continuous intervals and the average value of the angle of the knee joint of one leg in one interval or two or more continuous intervals were calculated for each test object.

The prediction model is created using as an explanatory variable an average value of time-series data of a vertical displacement of the toe of one leg in the first period of the stance phase, an average value of time-series data of an angle of the knee joint of one leg in the second period of the stance phase, an average value of time-series data of an angle of the knee joint of one leg in the third period of the stance phase, and an average value of time-series data of an angle of the knee joint of one leg in the fourth period of the foot suspension phase, as a target variable. The first period is a period of 31% to 40% of one walking cycle, the second period is a period of 1% to 10% of one walking cycle, the third period is a period of 41% to 50% of one walking cycle, and the fourth period is a period of 71% to 100% of one walking cycle. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of a prediction model for which healthy subjects and muscle degeneration preparatory persons are determined is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model of the eleventh modification of the present embodiment is created by using as objective variables which of the muscle wasting disease, the muscle wasting disease preparation population, and the healthy subject is the test subject, and using as explanatory variables the average value of the vertical displacement of the toe of one leg during 31% to 40% of one walking cycle and the average value of the angle of the knee joint of one leg during 1% to 10%, 41% to 50%, and 71% to 100% of one walking cycle. In fig. 32, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia preparation population as the sarcopenia preparation population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as the sarcopenia preparation population.

The ROC curve shown in fig. 32 is a curve that depicts the true positive rate and the false positive rate of a prediction model created with the average value of the displacement of the toe of one leg in the vertical direction during 31% to 40% of one walking cycle, the average value of the angle of the knee joint of one leg during 1% to 10% of one walking cycle, the average value of the angle of the knee joint of one leg during 41% to 50% of one walking cycle, and the average value of the angle of the knee joint of one leg during 71% to 100% of one walking cycle as explanatory variables. The AUC value of the ROC curve shown in fig. 32 is 0.772.

In an eleventh modification of the present embodiment, an average value of the displacement of the toe portion of one leg in the vertical direction during 31% to 40% of one walking cycle, an average value of the angle of the knee joint of one leg during 1% to 10% of one walking cycle, an average value of the angle of the knee joint of one leg during 41% to 50% of one walking cycle, and an average value of the angle of the knee joint of one leg during 71% to 100% of one walking cycle are determined as walking parameters. Then, a prediction model created by using as explanatory variables the average value of the displacement in the vertical direction of the toe of one leg during 31% to 40% of one gait cycle, the average value of the angle of the knee joint of one leg during 1% to 10% of one gait cycle, the average value of the angle of the knee joint of one leg during 41% to 50% of one gait cycle, and the average value of the angle of the knee joint of one leg during 71% to 100% of one gait cycle is determined as the prediction model used by the muscle degeneration determination unit 113.

The walking parameter detecting unit 112 detects time-series data of a displacement of a toe portion of one leg in a vertical direction during 31% to 40% of one walking cycle, time-series data of an angle of a knee joint of one leg during 1% to 10% of one walking cycle, time-series data of an angle of a knee joint of one leg during 41% to 50% of one walking cycle, and time-series data of an angle of a knee joint of one leg during 71% to 100% of one walking cycle.

The walking parameter detecting unit 112 calculates an average value of time-series data of the vertical displacement of the toe of one leg during 31% to 40% of one walking cycle, an average value of time-series data of the angle of the knee joint of one leg during 1% to 10% of one walking cycle, an average value of time-series data of the angle of the knee joint of one leg during 41% to 50% of one walking cycle, and an average value of time-series data of the angle of the knee joint of one leg during 71% to 100% of one walking cycle.

The memory 12 stores in advance a prediction model generated by using as input values an average value of time-series data of displacement in the vertical direction of the toe of one leg during 31% to 40% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during 1% to 10% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during 41% to 50% of one walking cycle, and an average value of time-series data of angles of the knee joint of one leg during 71% to 100% of one walking cycle, and using as output values either of a muscle degeneration disease, a muscle degeneration disease preparatory population, and a healthy subject as a test object.

The sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preparation population, and the healthy subjects is the test subject, using an average value of time-series data of the displacement in the vertical direction of the toe of one leg during 31% to 40% of one walking cycle, an average value of time-series data of the angle of the knee joint of one leg during 1% to 10% of one walking cycle, an average value of time-series data of the angle of the knee joint of one leg during 41% to 50% of one walking cycle, and an average value of time-series data of the angle of the knee joint of one leg during 71% to 100% of one walking cycle.

The sarcopenia determination unit 113 inputs an average value of time-series data of a displacement in the vertical direction of the toe of one leg during 31% to 40% of one walking cycle, an average value of time-series data of an angle of the knee joint of one leg during 1% to 10% of one walking cycle, an average value of time-series data of an angle of the knee joint of one leg during 41% to 50% of one walking cycle, and an average value of time-series data of an angle of the knee joint of one leg during 71% to 100% of one walking cycle to the prediction model, and obtains a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation group, and a healthy person from the prediction model.

As described above, the AUC value obtained as a result of the population ready for muscular dystrophy determined by the prediction model created using only the displacement of the toe of one leg in the vertical direction during the stance phase was 0.560, the AUC value obtained as a result of the population ready for muscular dystrophy determined by the prediction model created using only the angle of the knee joint of one leg during the stance phase was 0.537, and the AUC value obtained as a result of the population ready for muscular dystrophy determined by the prediction model created using only the angle of the knee joint of one leg during the foot suspension phase was 0.604. In response to this, the AUC value obtained as a result of the population ready for sarcopenia was judged to be 0.772 from the prediction model created using the displacement of the toe of one leg in the vertical direction during the stance phase, the angle of the knee joint of one leg in the stance phase, and the angle of the knee joint of one leg in the foot suspension phase.

Therefore, the muscle degeneration preparatory population can be determined with high accuracy using the prediction model created using the displacement of the toe portion of one leg in the vertical direction during the stance phase, the angle of the knee joint of one leg in the stance phase, and the angle of the knee joint of one leg in the foot suspension phase, as compared to the prediction model created using only the displacement of the toe portion of one leg in the vertical direction during the stance phase, the angle of the knee joint of one leg in the stance phase, and the angle of the knee joint of one leg in the foot suspension phase, respectively.

Next, walking parameters of a twelfth modification of the present embodiment will be described.

The walking parameter in the twelfth modification of the present embodiment may be an average value of time-series data of a displacement in the vertical direction of the toe portion of one leg in the first period of the foot suspension period of one leg, or an average value of time-series data of an angle of the knee joint of one leg in the second period of the foot standing period of one leg.

Fig. 33 is a schematic diagram showing a change in angle of a knee joint of one leg in one walking cycle in a twelfth modification of the present embodiment. In fig. 33, the vertical axis represents the angle of the knee joint, and the horizontal axis represents one gait cycle after normalization. In fig. 33, the broken line shows the average waveform of the angle of the knee joint of one leg of healthy people, and the solid line shows the average waveform of the angle of the knee joint of one leg of test subjects who are population candidates for sarcopenia or sarcopenia.

In a twelfth modification of the present embodiment, it is determined whether or not the test subject is a sarcopenia or a sarcopenia ready population. In addition, a test subject who is not a population ready for sarcopenia or sarcopenia is judged as a healthy subject.

Fig. 20 shows an average waveform of the vertical displacement of the toe of one leg of healthy persons and an average waveform of the vertical displacement of the toe of one leg of test subjects who are population candidates for sarcopenia or sarcopenia.

In the twelfth modification of the present embodiment, as in the above-described experiment, time-series data of the displacement in the vertical direction of the toe portion of one leg of each of the plurality of test subjects and time-series data of the angle of the knee joint of one leg of each of the plurality of test subjects are detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of the sarcopenia preparatory group, and the test subject of the healthy person. Then, a prediction model is created using as objective variables whether or not the test subject is sarcopenia or a pre-population for sarcopenia, and using as explanatory variables an average value of time-series data of the vertical displacement of the toe of one leg during the first period of the foot suspension period and an average value of time-series data of the angle of the knee joint of one leg during the second period of the foot standing period. The first period is a period of 65% to 70% of one walking cycle, and the second period is a period of 45% to 50% of one walking cycle. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of a prediction model for determining whether the population is a population suffering from sarcopenia or a population ready for sarcopenia is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model of the twelfth modification of the present embodiment is created by using as the objective variables whether or not the test subject is sarcopenia or a muscle degeneration preliminary population, and using as the explanatory variables the average value of the vertical displacement of the toe of one leg during 65% to 70% of one gait cycle and the average value of the angle of the knee joint of one leg during 45% to 50% of one gait cycle. In fig. 34, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia or sarcopenia preliminary population as sarcopenia or sarcopenia preliminary population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as sarcopenia or sarcopenia preliminary population.

The ROC curve shown in fig. 34 is a curve that depicts the true positive rate and the false positive rate of a prediction model created with the average value of the displacement in the vertical direction of the toe of one leg during 65% to 70% of one walking cycle and the average value of the angle of the knee joint of one leg during 45% to 50% of one walking cycle as explanatory variables. The AUC value of the ROC curve shown in fig. 34 is 0.6680.

In a twelfth modification of the present embodiment, an average value of the displacement of the toe of one leg in the vertical direction during 65% to 70% of one walking cycle and an average value of the angle of the knee joint of one leg during 45% to 50% of one walking cycle are determined as walking parameters. Then, a prediction model created using as explanatory variables the average value of the displacement in the vertical direction of the toe of one leg during 65% to 70% of one walking cycle and the average value of the angle of the knee joint of one leg during 45% to 50% of one walking cycle is determined as the prediction model used by the muscle-wasting disease determination unit 113.

The walking parameter detecting unit 112 detects time-series data of a vertical displacement of the toe of one leg in a first period of the foot suspension period and time-series data of an angle of the knee joint of one leg in a second period of the foot standing period. The first period is a period of 65% to 70% of one walking cycle, and the second period is a period of 45% to 50% of one walking cycle. The walking parameter detecting unit 112 detects time-series data of a vertical displacement of the toe of one leg during 65% to 70% of one walking cycle and time-series data of an angle of the knee joint of one leg during 45% to 50% of one walking cycle. The walking parameter detecting unit 112 calculates an average value of time-series data of the vertical displacement of the toe of one leg during 65% to 70% of one walking cycle and an average value of time-series data of the angle of the knee joint of one leg during 45% to 50% of one walking cycle.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia using an average value of time-series data of the vertical displacement of the toe of one leg in the first period and an average value of time-series data of the angle of the knee joint of one leg in the second period.

The sarcopenia determination unit 113 determines whether or not the test subject is a sarcopenia or a sarcopenia preparation group using an average value of time-series data of displacements of the toe of one leg in the vertical direction during 65% to 70% of one walking cycle and an average value of time-series data of angles of the knee joint of one leg during 45% to 50% of one walking cycle. The sarcopenia determination unit 113 obtains a determination result indicating whether or not the test subject is sarcopenia or a muscle sarcopenia candidate from the prediction model by inputting the average value of the time-series data of the displacement of the toe portion of one leg in the vertical direction during 65% to 70% of one walking cycle and the average value of the time-series data of the angle of the knee joint of one leg during 45% to 50% of one walking cycle to the prediction model.

Next, the walking parameters of a thirteenth modification of the present embodiment will be described.

The walking parameter in the thirteenth modification of the present embodiment may be an average value of time-series data of a vertical displacement of the toe portion of one leg in the first period of the foot standing period of one leg, an average value of time-series data of an angle of the ankle joint of one leg in the second period of the foot standing period of one leg, an average value of time-series data of an angle of the ankle joint of one leg in the third period of the foot standing period of one leg, an average value of time-series data of an angle of the ankle joint of one leg in the fourth period of the foot floating period of one leg, and an average value of time-series data of an angle of the ankle joint of one leg in the fifth period of the foot floating period of one leg.

In a thirteenth modification of the present embodiment, as in the above-described experiment, time-series data of the vertical displacement of the toe of one leg of each of the plurality of test subjects and time-series data of the angle of the ankle joint of one leg of each of the plurality of test subjects are detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of the sarcopenia preparatory population, and the test subject of the healthy person. In the experiment, one walking cycle after normalization was divided into ten sections, and the average value of the displacement in the vertical direction of the toe of one leg in one section or two or more continuous sections and the average value of the angle of the ankle joint of one leg in one section or two or more continuous sections were calculated for each test object.

The prediction model is created using as an explanatory variable an average value of time-series data of a vertical displacement of a toe portion of one leg in a first period of a standing period, an average value of time-series data of an angle of an ankle joint of one leg in a second period of the standing period, an average value of time-series data of an angle of an ankle joint of one leg in a third period of the standing period, an average value of time-series data of an angle of an ankle joint of one leg in a fourth period of a foot suspension period, and an average value of time-series data of an angle of an ankle joint of one leg in a fifth period of a foot suspension period. The first period is a period of 1% to 50% of one walking cycle, the second period is a period of 1% to 10% of one walking cycle, the third period is a period of 21% to 60% of one walking cycle, the fourth period is a period of 71% to 80% of one walking cycle, and the fifth period is a period of 91% to 100% of one walking cycle. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of the prediction model for judging healthy persons and sarcopenia is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model according to the thirteenth modification of the present embodiment is created by using as objective variables which of the sarcopenia, the sarcopenia ready population, and the healthy subject is the test subject, and using as explanatory variables the average value of the vertical displacement of the toe of one leg during 1% to 50% of one walking cycle, and the average value of the angle of the ankle joint of one leg during 1% to 50%, 21% to 60%, 71% to 80%, and 91% to 100% of one walking cycle. In fig. 35, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in fig. 35 is a curve that depicts the true positive rate and the false positive rate of a prediction model created with the average value of the displacement of the toe portion of one leg in the vertical direction during 1% to 50% of one walking cycle, the average value of the angle of the ankle joint of one leg during 1% to 10% of one walking cycle, the average value of the angle of the ankle joint of one leg during 21% to 60% of one walking cycle, the average value of the angle of the ankle joint of one leg during 71% to 80% of one walking cycle, and the average value of the angle of the ankle joint of one leg during 91% to 100% of one walking cycle as explanatory variables. The AUC value of the ROC curve shown in fig. 35 is 0.787.

In a thirteenth modification of the present embodiment, an average value of a displacement in a vertical direction of a toe portion of one leg during 1% to 50% of one walking cycle, an average value of an angle of an ankle joint of one leg during 1% to 10% of one walking cycle, an average value of an angle of an ankle joint of one leg during 21% to 60% of one walking cycle, an average value of an angle of an ankle joint of one leg during 71% to 80% of one walking cycle, and an average value of an angle of an ankle joint of one leg during 91% to 100% of one walking cycle are determined as walking parameters. Furthermore, a prediction model created using as explanatory variables the average value of the displacement in the vertical direction of the toe of one leg during 1% to 50% of one walking cycle, the average value of the angle of the ankle joint of one leg during 1% to 10% of one walking cycle, the average value of the angle of the ankle joint of one leg during 21% to 60% of one walking cycle, the average value of the angle of the ankle joint of one leg during 71% to 80% of one walking cycle, and the average value of the angle of the ankle joint of one leg during 91% to 100% of one walking cycle is determined as the prediction model used by the muscle attenuation syndrome determining section 113.

The walking parameter detecting unit 112 detects time-series data of a vertical displacement of the toe portion of one leg in the first period of the foot standing period, time-series data of an angle of the ankle joint of one leg in the second period of the foot standing period, time-series data of an angle of the ankle joint of one leg in the third period of the foot standing period, time-series data of an angle of the ankle joint of one leg in the fourth period of the foot suspension period, and time-series data of an angle of the ankle joint of one leg in the fifth period of the foot suspension period. The first period is a period of 1% to 50% of one walking cycle, the second period is a period of 1% to 10% of one walking cycle, the third period is a period of 21% to 60% of one walking cycle, the fourth period is a period of 71% to 80% of one walking cycle, and the fifth period is a period of 91% to 10% of one walking cycle.

The walking parameter detecting unit 112 detects time-series data of a vertical displacement of a toe portion of one leg in a period of 1% to 50% of one walking cycle, time-series data of an angle of an ankle joint of one leg in a period of 1% to 10% of one walking cycle, time-series data of an angle of an ankle joint of one leg in a period of 21% to 60% of one walking cycle, time-series data of an angle of an ankle joint of one leg in a period of 71% to 80% of one walking cycle, and time-series data of an angle of an ankle joint of one leg in a period of 91% to 100% of one walking cycle.

The walking parameter detecting unit 112 calculates an average value of time-series data of a vertical displacement of the toe of one leg during 1% to 50% of one walking cycle, an average value of time-series data of an angle of the ankle joint of one leg during 1% to 10% of one walking cycle, an average value of time-series data of an angle of the ankle joint of one leg during 21% to 60% of one walking cycle, an average value of time-series data of an angle of the ankle joint of one leg during 71% to 80% of one walking cycle, and an average value of time-series data of an angle of the ankle joint of one leg during 91% to 100% of one walking cycle.

The memory 12 stores in advance a prediction model generated by using as input values an average value of time-series data of a vertical displacement of a toe portion of one leg in a first period of a foot standing period, an average value of time-series data of an angle of an ankle joint of one leg in a second period of the foot standing period, an average value of time-series data of an angle of an ankle joint of one leg in a third period of the foot standing period, an average value of time-series data of an angle of an ankle joint of one leg in a fourth period of a foot floating period, and an average value of time-series data of an angle of an ankle joint of one leg in a fifth period of a foot floating period, and using as output values any one of a person to be tested, a muscle degeneration disease preparation population, and a healthy person.

The memory 12 stores in advance an average value of time-series data of a displacement in a vertical direction of a toe portion of one leg during 1% to 50% of one walking cycle, an average value of time-series data of an angle of an ankle joint of one leg during 1% to 10% of one walking cycle, an average value of time-series data of an angle of an ankle joint of one leg during 21% to 60% of one walking cycle, the prediction model is generated by using as input values an average value of time-series data of angles of an ankle joint of one leg during a period of 71% to 80% of one walking cycle and an average value of time-series data of angles of an ankle joint of one leg during a period of 91% to 100% of one walking cycle, and using as output values a test object which is one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of time-series data of the vertical displacement of the toe of one leg in the first period and an average value of time-series data of the angle of the ankle joint of one leg in the second period, the third period, the fourth period, and the fifth period.

The sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preparation group, and the healthy person the test object is based on an average value of time-series data of displacement in the vertical direction of the toe portion of one leg in a period of 1% to 50% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg in a period of 1% to 10% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg in a period of 21% to 60% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg in a period of 71% to 80% of one walking cycle, and an average value of time-series data of angles of the ankle joint of one leg in a period of 91% to 100% of one walking cycle.

The muscle wasting determination unit 113 determines whether or not the displacement of the toe of one leg in the vertical direction is equal to or greater than an average value of time-series data of a displacement of the toe of one leg in the vertical direction in a period of 1% to 50% of one walking cycle, an average value of time-series data of an angle of the ankle joint of one leg in a period of 1% to 10% of one walking cycle, and an average value of time-series data of an angle of the ankle joint of one leg in a period of 21% to 60% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during a period of 71% to 80% of one walking cycle and an average value of time-series data of angles of the ankle joint of one leg during a period of 91% to 100% of one walking cycle are input to a prediction model, and a determination result indicating whether a test subject is sarcopenia, a sarcopenia preparation group, or a healthy subject is obtained from the prediction model.

As described above, the AUC value obtained as a result of determination of sarcopenia using the prediction model created using only the displacement of the toe portion of one leg in the vertical direction during the stance phase was 0.636, the AUC value obtained as a result of determination of sarcopenia using the prediction model created using only the angle of the ankle joint during the stance phase was 0.498, and the AUC value obtained as a result of determination of sarcopenia using only the angle of the ankle joint during the foot suspension phase was 0.389. In response, the AUC value obtained as a result of the muscular attenuation was 0.787 as determined from a prediction model created using the displacement of the toe portion of one leg in the vertical direction during the stance phase, the angle of the ankle joint of one leg during the stance phase, and the angle of the ankle joint of one leg during the foot suspension phase.

Therefore, the muscle wasting can be determined with high accuracy by using the prediction model created by using the displacement of the toe portion of one leg in the vertical direction during the stance phase, the angle of the ankle joint during the stance phase, and the angle of the ankle joint during the foot suspension phase, as compared with the prediction model created by using only the displacement of the toe portion of one leg in the vertical direction during the stance phase, the angle of the ankle joint during the stance phase, and the angle of the ankle joint during the foot suspension phase, respectively.

Next, walking parameters of a fourteenth modification of the present embodiment will be described.

The walking parameter in the fourteenth modification of the present embodiment may be an average value of time-series data of displacement of the toe portion of one leg in the vertical direction during the first period of the stance phase of one leg, an average value of time-series data of displacement of the toe portion of one leg in the vertical direction during the second period of the foot suspension phase of one leg, an average value of time-series data of angles of the ankle joint of one leg during the third period of the stance phase of one leg, an average value of time-series data of angles of the ankle joint of one leg during the stance phase of one leg and the fourth period of the foot suspension phase, and an average value of time-series data of angles of the ankle joint of one leg during the fifth period of the foot suspension phase of one leg.

In the fourteenth modification of the present embodiment, as in the above-described experiment, time-series data of the vertical displacement of the toe portion of one leg of each of the plurality of test subjects and time-series data of the angle of the ankle joint of one leg of each of the plurality of test subjects are detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of the sarcopenia preparatory population, and the test subject of the healthy person. In the experiment, one walking cycle after normalization was divided into ten sections, and the average value of the displacement in the vertical direction of the toe of one leg in one section or two or more continuous sections and the average value of the angle of the ankle joint of one leg in one section or two or more continuous sections were calculated for each test object.

The prediction model is created using as an explanatory variable an average value of time-series data of vertical displacement of the toe portion of one leg in the first period of the foot standing period, an average value of time-series data of vertical displacement of the toe portion of one leg in the second period of the foot floating period, an average value of time-series data of angles of the ankle joint of one leg in the third period of the foot standing period, an average value of time-series data of angles of the ankle joint of one leg in the fourth periods of the foot standing period and the foot floating period, and an average value of time-series data of angles of the ankle joint of one leg in the fifth period of the foot floating period. The first period is a period of 1% to 50% of one walking cycle, the second period is a period of 71% to 100% of one walking cycle, the third period is a period of 1% to 10% of one walking cycle, the fourth period is a period of 51% to 70% of one walking cycle, and the fifth period is a period of 81% to 100% of one walking cycle. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of a prediction model for which healthy subjects and muscle degeneration preparatory persons are determined is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model of the fourteenth modification of the present embodiment is created by using as objective variables which of the muscle degeneration, the muscle degeneration preparatory population, and the healthy subjects is the test subject, and using as explanatory variables the average value of the vertical displacement of the toe of one leg during 1% to 50% and 71% to 100% of one walking cycle, and the average value of the angle of the ankle joint of one leg during 1% to 10%, 51% to 70%, and 81% to 100% of one walking cycle. In fig. 36, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia preparation population as the sarcopenia preparation population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as the sarcopenia preparation population.

The ROC curve shown in fig. 36 is a curve that depicts the true positive rate and the false positive rate of a prediction model created with the average value of the displacement in the vertical direction of the toe portion of one leg during 1% to 50% of one walking cycle, the average value of the displacement in the vertical direction of the toe portion of one leg during 71% to 100% of one walking cycle, the average value of the angle of the ankle joint of one leg during 1% to 10% of one walking cycle, the average value of the angle of the ankle joint of one leg during 51% to 70% of one walking cycle, and the average value of the angle of the ankle joint of one leg during 81% to 100% of one walking cycle as explanatory variables. The AUC value of the ROC curve shown in fig. 36 is 0.764.

In a fourteenth modification of the present embodiment, an average value of the displacement in the vertical direction of the toe portion of one leg during 1% to 50% of one walking cycle, an average value of the displacement in the vertical direction of the toe portion of one leg during 71% to 100% of one walking cycle, an average value of the angle of the ankle joint of one leg during 1% to 10% of one walking cycle, an average value of the angle of the ankle joint of one leg during 51% to 70% of one walking cycle, and an average value of the angle of the ankle joint of one leg during 81% to 100% of one walking cycle are determined as walking parameters. Furthermore, a prediction model created using as explanatory variables the average value of the displacement in the vertical direction of the toe portion of one leg during 1% to 50% of one walking cycle, the average value of the displacement in the vertical direction of the toe portion of one leg during 71% to 100% of one walking cycle, the average value of the angle of the ankle joint of one leg during 1% to 10% of one walking cycle, the average value of the angle of the ankle joint of one leg during 51% to 70% of one walking cycle, and the average value of the angle of the ankle joint of one leg during 81% to 100% of one walking cycle is determined as the prediction model used by the muscle attenuation syndrome determination unit 113.

The walking parameter detecting unit 112 detects time-series data of a vertical displacement of the toe portion of one leg in the first period of the foot standing period, time-series data of a vertical displacement of the toe portion of one leg in the second period of the foot floating period, time-series data of an angle of the ankle joint of one leg in the third period of the foot standing period, time-series data of an angle of the ankle joint of one leg in the fourth period of the foot standing period and the foot floating period, and time-series data of an angle of the ankle joint of one leg in the fifth period of the foot floating period. The first period is a period of 1% to 50% of one walking cycle, the second period is a period of 71% to 100% of one walking cycle, the third period is a period of 1% to 10% of one walking cycle, the fourth period is a period of 51% to 70% of one walking cycle, and the fifth period is a period of 81% to 100% of one walking cycle.

The walking parameter detecting unit 112 detects time-series data of a vertical displacement of a toe portion of one leg during 1% to 50% of one walking cycle, time-series data of a vertical displacement of a toe portion of one leg during 71% to 100% of one walking cycle, time-series data of an angle of an ankle joint of one leg during 1% to 10% of one walking cycle, time-series data of an angle of an ankle joint of one leg during 51% to 70% of one walking cycle, and time-series data of an angle of an ankle joint of one leg during 81% to 100% of one walking cycle.

The walking parameter detecting unit 112 calculates an average value of time-series data of vertical displacement of the toe portion of one leg during 1% to 50% of one walking cycle, an average value of time-series data of vertical displacement of the toe portion of one leg during 71% to 100% of one walking cycle, an average value of time-series data of angle of the ankle joint of one leg during 1% to 10% of one walking cycle, an average value of time-series data of angle of the ankle joint of one leg during 51% to 70% of one walking cycle, and an average value of time-series data of angle of the ankle joint of one leg during 81% to 100% of one walking cycle.

The memory 12 stores in advance a prediction model generated by using as input values an average value of time-series data of a vertical displacement of a toe portion of one leg in a first period of a footfall period, an average value of time-series data of a vertical displacement of a toe portion of one leg in a second period of a footfall period, an average value of time-series data of an angle of an ankle joint of one leg in a third period of the footfall period, an average value of time-series data of angles of an ankle joint of one leg in a fourth period of the footfall period and the footfall period, and an average value of time-series data of an angle of an ankle joint of one leg in a fifth period of the footfall period, and using as output values either of a muscle degeneration disease, a muscle degeneration disease preparation population, and a healthy person as a test object.

The memory 12 stores in advance an average value of time-series data of a displacement in the vertical direction of the toe portion of one leg during 1% to 50% of one walking cycle, an average value of time-series data of a displacement in the vertical direction of the toe portion of one leg during 71% to 100% of one walking cycle, an average value of time-series data of an angle of the ankle joint of one leg during 1% to 10% of one walking cycle, the prediction model is generated by using as input values an average value of time-series data of angles of an ankle joint of one leg during a period of 51% to 70% of one walking cycle and an average value of time-series data of angles of an ankle joint of one leg during a period of 81% to 100% of one walking cycle, and using as output values a test object which is one of sarcopenia, a muscle degeneration preparatory population, and a healthy subject.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of the time-series data of the vertical displacement of the toe of one leg in the first period and the second period and an average value of the time-series data of the angle of the ankle joint of one leg in the third period, the fourth period, and the fifth period.

The sarcopenia determination unit 113 determines which of the population having sarcopenia and sarcopenia preliminary population and the healthy population the test object is based on an average value of time-series data of the displacement in the vertical direction of the toe portion of one leg during 1% to 50% of one walking cycle, an average value of time-series data of the displacement in the vertical direction of the toe portion of one leg during 71% to 100% of one walking cycle, an average value of time-series data of the angle of the ankle joint of one leg during 1% to 10% of one walking cycle, an average value of time-series data of the angle of the ankle joint of one leg during 51% to 70% of one walking cycle, and an average value of time-series data of the angle of the ankle joint of one leg during 81% to 100% of one walking cycle.

The muscle wasting determination unit 113 determines whether or not the displacement of the toe of one leg in the vertical direction is equal to or greater than an average value of time-series data on the displacement of the toe of one leg in the vertical direction in a period of 1% to 50% of one walking cycle, an average value of time-series data on the displacement of the toe of one leg in the vertical direction in a period of 71% to 100% of one walking cycle, and an average value of time-series data on the angle of the ankle joint of one leg in a period of 1% to 10% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during 51% to 70% of one walking cycle and an average value of time-series data of angles of the ankle joint of one leg during 81% to 100% of one walking cycle are input to a prediction model, and a determination result indicating whether a test subject is sarcopenia, a sarcopenia preparation group, or a healthy subject is obtained from the prediction model.

As described above, the AUC value obtained as a result of the muscle wasting disorder preliminary population judged by the prediction model created by using only the displacement of the toe portion of one leg in the vertical direction in the stance phase was 0.560, the AUC value obtained as a result of the muscle wasting disorder preliminary population judged by the prediction model created by using only the displacement of the toe portion of one leg in the vertical direction in the foot suspension phase was 0.626, the AUC value obtained as a result of the muscle wasting disorder preliminary population judged by the prediction model created by using only the angle of the ankle joint in the stance phase was 0.610, and the AUC value obtained as a result of the muscle wasting disorder preliminary population judged by the prediction model created by using only the angle of the ankle joint in the foot suspension phase was 0.622. In contrast, the AUC value obtained as a result of the muscle wasting preliminary population was judged to be 0.764 from the prediction model created using the displacement of the toe portion of one leg in the vertical direction during the stance phase, the displacement of the toe portion of one leg in the vertical direction during the foot suspension phase, the angle of the ankle joint of one leg in the stance phase, and the angle of the ankle joint of one leg in the foot suspension phase.

Therefore, compared to the prediction models created using only the displacement of the toe portion of one leg in the vertical direction during the stance phase, the displacement of the toe portion of one leg in the vertical direction during the foot suspension phase, the angle of the ankle joint during the stance phase, and the angle of the ankle joint during the foot suspension phase, the prediction models created using the displacement of the toe portion of one leg in the vertical direction during the stance phase, the displacement of the toe portion of one leg in the vertical direction during the foot suspension phase, the angle of the ankle joint during the stance phase, and the angle of the ankle joint during the foot suspension phase can determine the muscle attenuation preparatory population with high accuracy.

Next, walking parameters of a fifteenth modification of the present embodiment will be described.

The walking parameter in the fifteenth modification of the present embodiment may be an average value of time-series data of an angle of the knee joint of one leg in the first period of the foot standing period of one leg, an average value of time-series data of an angle of the knee joint of one leg in the second period of the foot hanging period of one leg, an average value of time-series data of an angle of the knee joint of one leg in the third period of the foot hanging period of one leg, an average value of time-series data of an angle of the ankle joint of one leg in the fourth period of the foot standing period of one leg, an average value of time-series data of an angle of the ankle joint of one leg in the fifth period of the foot hanging period of one leg, and an average value of time-series data of an angle of the ankle joint of one leg in the sixth period of the foot hanging period of one leg.

In the fifteenth modification of the present embodiment, as in the above-described experiment, time-series data of the angle of the knee joint of one leg of each of the plurality of test subjects and time-series data of the angle of the ankle joint of one leg of each of the plurality of test subjects are detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of the sarcopenia preparatory group, and the test subject of the healthy person. In the experiment, one standardized walking cycle was divided into ten intervals, and the average value of the angles of the knee joint of one leg in one interval or two or more continuous intervals and the average value of the angles of the ankle joint of one leg in one interval or two or more continuous intervals were calculated for each test object.

The prediction model is created with a target variable being any one of sarcopenia, a muscle degeneration preliminary population, and a healthy subject, and with an explanatory variable being an average value of time-series data of an angle of the knee joint of one leg in the first period of the stance phase, an average value of time-series data of an angle of the knee joint of one leg in the second period of the foot suspension phase, an average value of time-series data of an angle of the knee joint of one leg in the third period of the foot suspension phase, an average value of time-series data of an angle of the ankle joint of one leg in the fourth period of the stance phase, an average value of time-series data of an angle of the ankle joint of one leg in the fifth period of the foot suspension phase, and an average value of time-series data of an angle of the ankle joint of one leg in the sixth period of the foot suspension phase. The first period is a period of 1% to 40% of one walking cycle, the second period is a period of 61% to 70% of one walking cycle, the third period is a period of 81% to 100% of one walking cycle, the fourth period is a period of 1% to 50% of one walking cycle, the fifth period is a period of 61% to 70% of one walking cycle, and the sixth period is a period of 91% to 100% of one walking cycle. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of the prediction model for judging healthy persons and sarcopenia is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model according to the fifteenth modification of the present embodiment is created by using as objective variables which of the sarcopenia, the sarcopenia ready population, and the healthy subject is the test subject, and using as explanatory variables the average values of the angles of the knee joint of one leg during the period of 1% to 40%, 61% to 70%, and 81% to 100% of one walking cycle, and the average values of the angles of the ankle joint of one leg during the period of 1% to 50%, 61% to 70%, and 91% to 100% of one walking cycle. In fig. 37, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in fig. 37 is a curve that depicts the true positive rate and the false positive rate of a prediction model created using as explanatory variables the average value of the angles of the knee joint of one leg during 1% to 40% of one walking cycle, the average value of the angles of the knee joint of one leg during 61% to 70% of one walking cycle, the average value of the angles of the knee joint of one leg during 81% to 100% of one walking cycle, the average value of the angles of the ankle joint of one leg during 1% to 50% of one walking cycle, the average value of the angles of the ankle joint of one leg during 61% to 70% of one walking cycle, and the average value of the angles of the ankle joint of one leg during 91% to 100% of one walking cycle. The AUC value of the ROC curve shown in fig. 37 is 0.865.

In a fifteenth modification of the present embodiment, an average value of angles of the knee joint of one leg during 1% to 40% of one walking cycle, an average value of angles of the knee joint of one leg during 61% to 70% of one walking cycle, an average value of angles of the knee joint of one leg during 81% to 100% of one walking cycle, an average value of angles of the ankle joint of one leg during 1% to 50% of one walking cycle, an average value of angles of the ankle joint of one leg during 61% to 70% of one walking cycle, and an average value of angles of the ankle joint of one leg during 91% to 100% of one walking cycle are determined as walking parameters. Furthermore, a prediction model created using as explanatory variables the average value of the angles of the knee joint of one leg during 1% to 40% of one walking cycle, the average value of the angles of the knee joint of one leg during 61% to 70% of one walking cycle, the average value of the angles of the knee joint of one leg during 81% to 100% of one walking cycle, the average value of the angles of the ankle joint of one leg during 1% to 50% of one walking cycle, the average value of the angles of the ankle joint of one leg during 61% to 70% of one walking cycle, and the average value of the angles of the ankle joint of one leg during 91% to 100% of one walking cycle is determined as the prediction model used by the muscular dystrophy determining unit 113.

The walking parameter detection unit 112 detects time-series data of an angle of the knee joint of one leg in the first period of the stance period, time-series data of an angle of the knee joint of one leg in the second period of the foot suspension period, time-series data of an angle of the knee joint of one leg in the third period of the foot suspension period, time-series data of an angle of the ankle joint of one leg in the fourth period of the stance period, time-series data of an angle of the ankle joint of one leg in the fifth period of the foot suspension period, and time-series data of an angle of the ankle joint of one leg in the sixth period of the foot suspension period. The first period is a period of 1% to 40% of one walking cycle, the second period is a period of 61% to 70% of one walking cycle, the third period is a period of 81% to 100% of one walking cycle, the fourth period is a period of 1% to 50% of one walking cycle, the fifth period is a period of 61% to 70% of one walking cycle, and the sixth period is a period of 91% to 100% of one walking cycle.

The walking parameter detecting unit 112 detects time-series data of an angle of a knee joint of one leg during a period of 1% to 40% of one walking cycle, time-series data of an angle of a knee joint of one leg during a period of 61% to 70% of one walking cycle, time-series data of an angle of a knee joint of one leg during a period of 81% to 100% of one walking cycle, time-series data of an angle of an ankle joint of one leg during a period of 1% to 50% of one walking cycle, time-series data of an angle of an ankle joint of one leg during a period of 61% to 70% of one walking cycle, and time-series data of an angle of an ankle joint of one leg during a period of 91% to 100% of one walking cycle.

The walking parameter detecting unit 112 calculates an average value of time-series data of angles of the knee joint of one leg during a period of 1% to 40% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during a period of 61% to 70% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during a period of 81% to 100% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during a period of 1% to 50% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during a period of 61% to 70% of one walking cycle, and an average value of time-series data of angles of the ankle joint of one leg during a period of 91% to 100% of one walking cycle.

The memory 12 stores in advance a prediction model generated by using as input values an average value of time-series data of an angle of a knee joint of one leg in a first period of a stance period, an average value of time-series data of an angle of a knee joint of one leg in a second period of a foot suspension period, an average value of time-series data of an angle of a knee joint of one leg in a third period of a foot suspension period, an average value of time-series data of an angle of an ankle joint of one leg in a fourth period of a stance period, an average value of time-series data of an angle of an ankle joint of one leg in a fifth period of a foot suspension period, and an average value of time-series data of an angle of an ankle joint of one leg in a sixth period of a foot suspension period, and using as output values either of a muscle degeneration, a muscle degeneration preparatory population, and a healthy subject.

The memory 12 stores in advance, as input values, an average value of time-series data of angles of a knee joint of one leg during a period of 1% to 40% of one walking cycle, an average value of time-series data of angles of a knee joint of one leg during a period of 61% to 70% of one walking cycle, an average value of time-series data of angles of a knee joint of one leg during a period of 81% to 100% of one walking cycle, an average value of time-series data of angles of an ankle joint of one leg during a period of 1% to 50% of one walking cycle, an average value of time-series data of angles of an ankle joint of one leg during a period of 61% to 70% of one walking cycle, and an average value of time-series data of angles of an ankle joint of one leg during a period of 91% to 100% of one walking cycle, and the test subjects are sarcopenia, A prediction model generated by using either the muscle degeneration preparatory population or the healthy person as an output value.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of the time-series data of the angles of the knee joint of one leg in the first period, the second period, and the third period and an average value of the time-series data of the angles of the ankle joint of one leg in the fourth period, the fifth period, and the sixth period.

The sarcopenia determination unit 113 determines whether the test subject is sarcopenia, whether the test subject is sarcopenia or not, using an average value of time-series data of angles of the knee joint of one leg during 1% to 40% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during 61% to 70% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during 81% to 100% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during 1% to 50% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during 61% to 70% of one walking cycle, and an average value of time-series data of angles of the ankle joint of one leg during 91% to 100% of one walking cycle, Muscle wasting is a pre-prepared population and a healthy person.

The sarcopenia determination unit 113 obtains a time series data representing that the test object is sarcopenia, a time series data of an angle of the knee joint of one leg during 1% to 40% of one walking cycle, a time series data of an angle of the knee joint of one leg during 61% to 70% of one walking cycle, a time series data of an angle of the knee joint of one leg during 81% to 100% of one walking cycle, a time series data of an angle of the ankle joint of one leg during 1% to 50% of one walking cycle, a time series data of an angle of the ankle joint of one leg during 61% to 70% of one walking cycle, and a time series data of an angle of the ankle joint of one leg during 91% to 100% of one walking cycle, from the prediction model by inputting the time series data to the prediction model, The result of determination of the muscle wasting disease preparation population or the healthy population.

As described above, the AUC value obtained as a result of the muscular attenuation disease was 0.586 from the prediction model created with only the angle of the knee joint of one leg in the stance phase, the AUC value obtained as a result of the muscular attenuation disease was 0.699 from the prediction model created with only the angle of the knee joint of one leg in the foot suspension phase, the AUC value obtained as a result of the muscular attenuation disease was 0.498 from the prediction model created with only the angle of the ankle joint in the stance phase, and the AUC value obtained as a result of the muscular attenuation disease was 0.389 from the prediction model created with only the angle of the ankle joint in the foot suspension phase. In contrast, the AUC value obtained as a result of the muscular dystrophy determined by the prediction model created using the angle of the knee joint of one leg in the stance phase, the angle of the knee joint of one leg in the foot suspension phase, the angle of the ankle joint of one leg in the stance phase, and the angle of the ankle joint of one leg in the foot suspension phase was 0.865.

Therefore, the muscle wasting can be determined with high accuracy by the prediction model created using the angle of the knee joint of one leg in the stance phase, the angle of the knee joint of one leg in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase, compared to the prediction model created using only the angle of the knee joint of one leg in the stance phase, the angle of the knee joint of one leg in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase.

Next, walking parameters of a sixteenth modification of the present embodiment will be described.

The walking parameters in the sixteenth modification of the present embodiment may be an average value of time-series data of angles of the knee joint of one leg in the first period of the stance period of one leg, an average value of time-series data of angles of the knee joint of one leg in the second period of the foot suspension period of one leg, an average value of time-series data of angles of the ankle joint of one leg in the third period of the stance period of one leg, and an average value of time-series data of angles of the ankle joint of one leg in the stance period of one leg and the fourth period of the foot suspension period.

In a sixteenth modification of the present embodiment, as in the above-described experiment, time-series data of the angle of the knee joint of one leg of each of the plurality of test subjects and time-series data of the angle of the ankle joint of one leg of each of the plurality of test subjects are detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of the sarcopenia preparatory group, and the test subject of the healthy person. In the experiment, one standardized walking cycle was divided into ten intervals, and the average value of the angles of the knee joint of one leg in one interval or two or more continuous intervals and the average value of the angles of the ankle joint of one leg in one interval or two or more continuous intervals were calculated for each test object.

The prediction model is created with a target variable being any one of sarcopenia, a muscle degeneration preliminary population, and a healthy subject, and with an explanatory variable being an average value of time-series data of angles of the knee joint of one leg in the first period of the stance phase, an average value of time-series data of angles of the knee joint of one leg in the second period of the foot suspension phase, an average value of time-series data of angles of the ankle joint of one leg in the third period of the stance phase, and an average value of time-series data of angles of the ankle joint of one leg in the stance phase and the fourth period of the foot suspension phase. The first period is a period of 1% to 10% of one walking cycle, the second period is a period of 81% to 100% of one walking cycle, the third period is a period of 1% to 10% of one walking cycle, and the fourth period is a period of 21% to 70% of one walking cycle. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of a prediction model for which healthy subjects and muscle degeneration preparatory persons are determined is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model according to the sixteenth modification of the present embodiment is created by using as the target variables which of the sarcopenia, the sarcopenia ready population, and the healthy subject is the test subject, and using as the explanatory variables the average value of the angles of the knee joint of one leg during 1% to 10% and 81% to 100% of one walking cycle and the average value of the angles of the ankle joint of one leg during 1% to 10% and 21% to 70% of one walking cycle. In fig. 38, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia preparation population as the sarcopenia preparation population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as the sarcopenia preparation population.

The ROC curve shown in fig. 38 is a curve that depicts the true positive rate and the false positive rate of a prediction model created with the average value of the angle of the knee joint of one leg during 1% to 10% of one walking cycle, the average value of the angle of the knee joint of one leg during 81% to 100% of one walking cycle, the average value of the angle of the ankle joint of one leg during 1% to 10% of one walking cycle, and the average value of the angle of the ankle joint of one leg during 21% to 70% of one walking cycle as explanatory variables. The AUC value of the ROC curve shown in fig. 38 is 0.743.

In a sixteenth modification of the present embodiment, an average value of angles of the knee joint of one leg during 1% to 10% of one walking cycle, an average value of angles of the knee joint of one leg during 81% to 100% of one walking cycle, an average value of angles of the ankle joint of one leg during 1% to 10% of one walking cycle, and an average value of angles of the ankle joint of one leg during 21% to 70% of one walking cycle are determined as walking parameters. A prediction model created using as explanatory variables the average value of the angles of the knee joint of one leg during 1% to 10% of one walking cycle, the average value of the angles of the knee joint of one leg during 81% to 100% of one walking cycle, the average value of the angles of the ankle joint of one leg during 1% to 10% of one walking cycle, and the average value of the angles of the ankle joint of one leg during 21% to 70% of one walking cycle is determined as the prediction model used by the muscle degeneration determination unit 113.

The walking parameter detection unit 112 detects time-series data of an angle of a knee joint of one leg in a first period of the stance period, time-series data of an angle of a knee joint of one leg in a second period of the foot suspension period, time-series data of an angle of an ankle joint of one leg in a third period of the stance period, and time-series data of an angle of an ankle joint of one leg in a fourth period of the stance period and the foot suspension period. The first period is a period of 1% to 10% of one walking cycle, the second period is a period of 81% to 100% of one walking cycle, the third period is a period of 1% to 10% of one walking cycle, and the fourth period is a period of 21% to 70% of one walking cycle.

The walking parameter detection unit 112 detects time-series data of an angle of the knee joint of one leg during a period of 1% to 10% of one walking cycle, time-series data of an angle of the knee joint of one leg during a period of 81% to 100% of one walking cycle, time-series data of an angle of the ankle joint of one leg during a period of 1% to 10% of one walking cycle, and time-series data of an angle of the ankle joint of one leg during a period of 21% to 70% of one walking cycle.

The walking parameter detecting unit 112 calculates an average value of time-series data of angles of the knee joint of one leg during a period of 1% to 10% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during a period of 81% to 100% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during a period of 1% to 10% of one walking cycle, and an average value of time-series data of angles of the ankle joint of one leg during a period of 21% to 70% of one walking cycle.

The memory 12 stores in advance a prediction model generated by using as input values an average value of time-series data of angles of a knee joint of one leg in a first period of a stance period, an average value of time-series data of angles of a knee joint of one leg in a second period of a foot suspension period, an average value of time-series data of angles of an ankle joint of one leg in a third period of the stance period, and an average value of time-series data of angles of an ankle joint of one leg in a fourth period of the stance period and the foot suspension period, and using as output values either of a muscle degeneration disease, a muscle degeneration disease preparatory population, and a healthy person as a test object.

The memory 12 stores in advance a prediction model generated by using as input values an average value of time-series data of angles of a knee joint of one leg during a period of 1% to 10% of one walking cycle, an average value of time-series data of angles of a knee joint of one leg during a period of 81% to 100% of one walking cycle, an average value of time-series data of angles of an ankle joint of one leg during a period of 1% to 10% of one walking cycle, and an average value of time-series data of angles of an ankle joint of one leg during a period of 21% to 70% of one walking cycle, and using as an output value any one of a population subject to be tested, a population prepared for muscular dystrophy, and a healthy subject.

The sarcopenia determination unit 113 determines whether or not the test subject is sarcopenia, using an average value of the time-series data of the angles of the knee joint of one leg in the first period and the second period and an average value of the time-series data of the angles of the ankle joint of one leg in the third period and the fourth period.

The sarcopenia determination unit 113 determines which of the sarcopenia, the sarcopenia preparation population, and the healthy subjects is the test subject, using an average value of the time-series data of the angles of the knee joint of one leg during 1% to 10% of one walking cycle, an average value of the time-series data of the angles of the knee joint of one leg during 81% to 100% of one walking cycle, an average value of the time-series data of the angles of the ankle joint of one leg during 1% to 10% of one walking cycle, and an average value of the time-series data of the angles of the ankle joint of one leg during 21% to 70% of one walking cycle.

The sarcopenia determination unit 113 inputs an average value of time-series data of angles of the knee joint of one leg during 1% to 10% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during 81% to 100% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during 1% to 10% of one walking cycle, and an average value of time-series data of angles of the ankle joint of one leg during 21% to 70% of one walking cycle to the prediction model, and acquires a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation group, and a healthy person from the prediction model.

As described above, the AUC value obtained as a result of the preliminary population for muscular dystrophy determined by the prediction model created only with the angle of the knee joint of one leg in the stance phase was 0.537, the AUC value obtained as a result of the preliminary population for muscular dystrophy determined by the prediction model created only with the angle of the knee joint of one leg in the foot suspension phase was 0.604, the AUC value obtained as a result of the preliminary population for muscular dystrophy determined by the prediction model created only with the angle of the ankle joint in the stance phase was 0.610, and the AUC value obtained as a result of the preliminary population for muscular dystrophy determined by the prediction model created only with the angle of the ankle joint in the foot suspension phase was 0.622. In response, the AUC value obtained as a result of the population ready for sarcopenia was judged to be 0.743 from a prediction model created using the angle of the knee joint of one leg in the stance phase, the angle of the knee joint of one leg in the foot suspension phase, the angle of the ankle joint of one leg in the stance phase, and the angle of the ankle joint of one leg in the foot suspension phase.

Therefore, the prediction model created using the angle of the knee joint of one leg in the stance phase, the angle of the knee joint of one leg in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase can determine the muscle attenuation syndrome preparatory population with high accuracy, as compared with the prediction model created using only the angle of the knee joint of one leg in the stance phase, the angle of the knee joint of one leg in the foot suspension phase, the angle of the ankle joint in the stance phase, and the angle of the ankle joint in the foot suspension phase, respectively.

Next, walking parameters of a seventeenth modification of the present embodiment will be described.

The walking parameters of the seventeenth modification of the present embodiment may be an average value of time-series data of displacements in the vertical direction of the toe portion of one leg during the first period of the stance period of one leg, an average value of time-series data of displacements in the vertical direction of the toe portion of one leg during the second period of the stance period of one leg, an average value of time-series data of displacements in the vertical direction of the toe portion of one leg during the third period of the stance period of one leg, an average value of time-series data of angles of the knee joint of one leg during the fourth period of the stance period of one leg, an average value of time-series data of angles of the knee joint of one leg during the fifth period of the stance period and the foot suspension period of one leg, an average value of time-series data of angles of the ankle joint of one leg during the sixth period of the stance period of one leg, and a time-series of angles of the ankle joint of one leg during the stance period and the seventh period of the foot suspension period of one leg Mean value of data.

In the seventeenth modification of the present embodiment, as in the above-described experiment, time-series data of the vertical displacement of the toe of one leg of each of the plurality of test subjects, time-series data of the angle of the knee joint of one leg of each of the plurality of test subjects, and time-series data of the angle of the ankle joint of one leg of each of the plurality of test subjects are detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of the sarcopenia preparatory group, and the test subject of the healthy person. In the experiment, one walking cycle after normalization was divided into ten intervals, and an average value of the vertical displacement of the toe of one leg in one interval or two or more consecutive intervals, an average value of the angle of the knee joint of one leg in one interval or two or more consecutive intervals, and an average value of the angle of the ankle joint of one leg in one interval or two or more consecutive intervals were calculated for each test subject.

The target variable is one of sarcopenia, a muscle degeneration preliminary population, and a healthy subject, and the average value of time-series data on the vertical displacement of the toe portion of one leg in the first period of the stance phase, the average value of time-series data on the vertical displacement of the toe portion of one leg in the second period of the stance phase, the average value of time-series data on the vertical displacement of the toe portion of one leg in the third period of the foot suspension phase, the average value of time-series data on the angle of the knee joint of one leg in the fourth period of the stance phase, the average value of time-series data on the angles of the knee joint of one leg in the fifth periods of the stance phase and the foot suspension phase, the average value of time-series data on the angle of the ankle joint of one leg in the sixth period of the stance phase, and the average value of the time-series data on the angles of the ankle joint of one leg in the stance phase and the seventh period of the foot suspension phase are created The mean value is used as a prediction model of the explanatory variable. The first period is a period of 21% to 30% of one walking cycle, the second period is a period of 51% to 60% of one walking cycle, the third period is a period of 81% to 100% of one walking cycle, the fourth period is a period of 11% to 20% of one walking cycle, the fifth period is a period of 41% to 80% of one walking cycle, the sixth period is a period of 1% to 20% of one walking cycle, and the seventh period is a period of 51% to 80% of one walking cycle. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of the prediction model for judging healthy persons and sarcopenia is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model of the seventeenth modification of the present embodiment is created by using as explanatory variables an average value of the vertical displacement of the toe of one leg during 21% to 30%, 51% to 60%, and 81% to 100% of one walking cycle, an average value of the angle of the knee joint of one leg during 11% to 20% and 41% to 80% of one walking cycle, and an average value of the angle of the ankle joint of one leg during 1% to 20% and 51% to 80% of one walking cycle, as target variables of which the test object is one of sarcopenia, a muscle degeneration preparatory population, and a healthy person. In fig. 39, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of sarcopenia as sarcopenia, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy person as sarcopenia.

The ROC curve shown in figure 39 is a curve that depicts the true positive rate and the false positive rate of a predictive model, the prediction model is created with an average value of displacement of a toe portion of one leg in a vertical direction during 21% to 30% of one walking cycle, an average value of displacement of a toe portion of one leg in a vertical direction during 51% to 60% of one walking cycle, an average value of displacement of a toe portion of one leg in a vertical direction during 81% to 100% of one walking cycle, an average value of angles of a knee joint of one leg during 11% to 20% of one walking cycle, an average value of angles of a knee joint of one leg during 41% to 80% of one walking cycle, an average value of angles of an ankle joint of one leg during 1% to 20% of one walking cycle, and an average value of angles of an ankle joint of one leg during 51% to 80% of one walking cycle as explanatory variables. The AUC value of the ROC curve shown in fig. 39 was 0.881.

In a seventeenth modification of the present embodiment, an average value of displacement in the vertical direction of the toe portion of one leg during 21% to 30% of one walking cycle, an average value of displacement in the vertical direction of the toe portion of one leg during 51% to 60% of one walking cycle, an average value of displacement in the vertical direction of the toe portion of one leg during 81% to 100% of one walking cycle, an average value of angles of the knee joint of one leg during 11% to 20% of one walking cycle, the average value of the angle of the knee joint of one leg during 41% to 80% of one walking cycle, the average value of the angle of the ankle joint of one leg during 1% to 20% of one walking cycle, and the average value of the angle of the ankle joint of one leg during 51% to 80% of one walking cycle are determined as walking parameters.

Furthermore, the average value of the displacement of the toe of one leg in the vertical direction during 21% to 30% of one walking cycle, the average value of the displacement of the toe of one leg in the vertical direction during 51% to 60% of one walking cycle, the average value of the displacement of the toe of one leg in the vertical direction during 81% to 100% of one walking cycle, and the average value of the angle of the knee joint of one leg during 11% to 20% of one walking cycle, a prediction model created using as explanatory variables the average value of the angles of the knee joint of one leg during 41% to 80% of one walking cycle, the average value of the angles of the ankle joint of one leg during 1% to 20% of one walking cycle, and the average value of the angles of the ankle joint of one leg during 51% to 80% of one walking cycle is determined as the prediction model used by the muscle degeneration determination unit 113.

The walking parameter detecting unit 112 detects time-series data of a vertical displacement of the toe portion of one leg in the first period of the stance phase, time-series data of a vertical displacement of the toe portion of one leg in the second period of the stance phase, time-series data of a vertical displacement of the toe portion of one leg in the third period of the foot suspension phase, time-series data of an angle of the knee joint of one leg in the fourth period of the stance phase, time-series data of an angle of the knee joint of one leg in the fifth period of the stance phase and the foot suspension phase, time-series data of an angle of the ankle joint of one leg in the sixth period of the stance phase, and time-series data of an angle of the ankle joint of one leg in the seventh period of the stance phase and the foot suspension phase. The first period is a period of 21% to 30% of one walking cycle, the second period is a period of 51% to 60% of one walking cycle, the third period is a period of 81% to 100% of one walking cycle, the fourth period is a period of 11% to 20% of one walking cycle, the fifth period is a period of 41% to 80% of one walking cycle, the sixth period is a period of 1% to 20% of one walking cycle, and the seventh period is a period of 51% to 80% of one walking cycle.

The walking parameter detecting unit 112 detects time-series data of displacement in the vertical direction of the toe of one leg during a period of 21% to 30% of one walking cycle, time-series data of displacement in the vertical direction of the toe of one leg during a period of 51% to 60% of one walking cycle, time-series data of displacement in the vertical direction of the toe of one leg during a period of 81% to 100% of one walking cycle, time-series data of angle of the knee joint of one leg during a period of 11% to 20% of one walking cycle, time-series data of angles of knee joints of one leg during 41% to 80% of one walking cycle, time-series data of angles of ankle joints of one leg during 1% to 20% of one walking cycle, and time-series data of angles of ankle joints of one leg during 51% to 80% of one walking cycle.

The walking parameter detecting unit 112 calculates an average value of time-series data of displacement in the vertical direction of the toe portion of one leg during 21% to 30% of one walking cycle, an average value of time-series data of displacement in the vertical direction of the toe portion of one leg during 51% to 60% of one walking cycle, an average value of time-series data of displacement in the vertical direction of the toe portion of one leg during 81% to 100% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during 11% to 20% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during 41% to 80% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during 1% to 20% of one walking cycle, a mean value of the walking cycle, Average of time series data of angles of ankle joints of one leg during 51% to 80% of one walking cycle.

The memory 12 stores in advance, as input values, an average value of time-series data of displacements in the vertical direction of the toe portion of one leg in the first period of the stance period, an average value of time-series data of displacements in the vertical direction of the toe portion of one leg in the second period of the stance period, an average value of time-series data of displacements in the vertical direction of the toe portion of one leg in the third period of the foot suspension period, an average value of time-series data of angles of the knee joint of one leg in the fourth period of the stance period, an average value of time-series data of angles of the knee joint of one leg in the stance period and the fifth period of the foot suspension period, an average value of time-series data of angles of the ankle joint of one leg in the sixth period of the stance period, and an average value of time-series data of angles of the ankle joint of one leg in the stance period and the seventh period of the foot suspension period, a prediction model generated using as an output value any one of a muscle degeneration disease, a muscle degeneration disease preparation population, and a healthy subject as a test object.

The memory 12 stores in advance an average value of time-series data of displacement in the vertical direction of the toe portion of one leg during 21% to 30% of one walking cycle, an average value of time-series data of displacement in the vertical direction of the toe portion of one leg during 51% to 60% of one walking cycle, an average value of time-series data of displacement in the vertical direction of the toe portion of one leg during 81% to 100% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during 11% to 20% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during 41% to 80% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during 1% to 20% of one walking cycle, and a flat time-series data of angles of the ankle joint of one leg during 51% to 80% of one walking cycle The mean value is a prediction model generated using, as an input value, any one of sarcopenia, a sarcopenia preparation population, and a healthy subject as an output value.

The sarcopenia determination unit 113 determines whether the test subject is sarcopenia using an average value of the time-series data of the vertical displacement of the toe of one leg in the first period, the second period, and the third period, an average value of the time-series data of the angle of the knee joint of one leg in the fourth period and the fifth period, and an average value of the time-series data of the angle of the ankle joint of one leg in the sixth period and the seventh period.

The sarcopenia determination unit 113 uses an average value of time-series data of displacement in the vertical direction during 21% to 30% of one walking cycle of the toe portion of one leg, an average value of time-series data of displacement in the vertical direction during 51% to 60% of one walking cycle of the toe portion of one leg, an average value of time-series data of displacement in the vertical direction during 81% to 100% of one walking cycle of the toe portion of one leg, an average value of time-series data of angles during 11% to 20% of one walking cycle of the knee joint of one leg, an average value of time-series data of angles during 41% to 80% of one walking cycle of the knee joint of one leg, an average value of time-series data of angles during 1% to 20% of one walking cycle of the ankle joint of one leg, and a time-series data of angles during 51% to 80% of one walking cycle of the ankle joint of one leg The average value is used to determine whether the test object is a person who suffers from sarcopenia, a person who is a person who suffers from sarcopenia, or a person who is a healthy person.

The muscle wasting determination unit 113 determines the average value of time-series data on the displacement in the vertical direction of the toe portion of one leg during 21% to 30% of one walking cycle, the average value of time-series data on the displacement in the vertical direction of the toe portion of one leg during 51% to 60% of one walking cycle, the average value of time-series data on the displacement in the vertical direction of the toe portion of one leg during 81% to 100% of one walking cycle, the average value of time-series data on the angle of the knee joint of one leg during 11% to 20% of one walking cycle, the average value of time-series data on the angle of the knee joint of one leg during 41% to 80% of one walking cycle, the average value of time-series data on the angle of the ankle joint of one leg during 1% to 20% of one walking cycle, the average value of time-series data on the displacement in the vertical direction of one leg during 1% to 30% of one walking cycle, the, The average value of the time-series data of the angle of the ankle joint of one leg during 51% to 80% of one walking cycle is input to the prediction model, and a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation population, or a healthy person is obtained from the prediction model.

As described above, the AUC value obtained as a result of determination of sarcopenia was 0.636 from the prediction model created using only the displacement of the toe portion of one leg in the vertical direction during the stance phase, the AUC value obtained as a result of determination of sarcopenia was 0.514 from the prediction model created using only the displacement of the toe portion of one leg in the vertical direction during the foot suspension phase, the AUC value obtained as a result of determination of sarcopenia was 0.586 from the prediction model created using only the angle of the knee joint in the stance phase, the AUC value obtained as a result of determination of sarcopenia was 0.699 from the prediction model created using only the angle of the knee joint in the foot suspension phase, the AUC value obtained as a result of determination of sarcopenia was 0.498 from the prediction model created using only the angle of the ankle joint in the stance phase, and the AUC value obtained as a result of determination of sarcopenia was 0.498 from the prediction model created using only the angle of the ankle joint in the foot suspension phase, and the AUC value obtained as a result of determination of sarcopenia was 0.389.

In response to this, the AUC value obtained as a result of the muscular attenuation disease was determined to be 0.881 from the prediction model created using the displacement of the toe portion of one leg in the vertical direction during the first period and the second period of the stance phase, the displacement of the toe portion of one leg in the vertical direction during the third period of the foot suspension phase, the angle of the knee joint of one leg in the fourth period of the stance phase, the angle of the knee joint of one leg in the fifth period of the stance phase and the foot suspension phase, the angle of the ankle joint of one leg in the sixth period of the stance phase, and the angle of the ankle joint of one leg in the seventh period of the stance phase and the foot suspension phase.

Therefore, compared to a prediction model created using only the displacement of the toe portion of one leg in the vertical direction during the stance phase, the displacement of the toe portion of one leg in the vertical direction during the foot suspension phase, the angle of the knee joint of one leg in the stance phase, the angle of the knee joint of one leg in the foot suspension phase, the angle of the ankle joint of one leg in the stance phase, and the angle of the ankle joint of one leg in the foot suspension phase, a prediction model created using the displacements of the toe portion of one leg in the vertical direction during the first and second periods of the stance phase, the displacement of the toe portion of one leg in the vertical direction during the third period of the foot suspension phase, the angle of the knee joint of one leg during the fourth period of the stance phase, the angles of the knee joint of one leg during the stance phase and the fifth period of the foot suspension phase, the angle of the ankle joint of one leg during the sixth period of the stance phase, and the angle of the ankle joint of one leg during the stance phase and the seventh period can be highly accurate And judging the muscular attenuation.

Next, walking parameters of an eighteenth modification of the present embodiment will be described.

The walking parameters in the eighteenth modification of the present embodiment may be an average value of time-series data of displacements in the vertical direction of the toe portion of one leg during the first period of the stance phase of one leg, an average value of time-series data of displacements in the vertical direction of the toe portion of one leg during the second period of the stance phase of one leg, an average value of time-series data of angles of the knee joint of one leg during the third period of the stance phase of one leg, an average value of time-series data of angles of the knee joint of one leg during the fourth period of the foot suspension phase of one leg, the average value of the time-series data of the angle of the ankle joint of one leg during the fifth period of the foot standing period of one leg, the average value of the time-series data of the angle of the ankle joint of one leg during the sixth period of the foot standing period and the foot suspension period of one leg, and the average value of the time-series data of the angle of the ankle joint of one leg during the seventh period of the foot suspension period of one leg.

In the eighteenth modification of the present embodiment, as in the above-described experiment, time-series data of the vertical displacement of the toe of one leg of each of the plurality of test subjects, time-series data of the angle of the knee joint of one leg of each of the plurality of test subjects, and time-series data of the angle of the ankle joint of one leg of each of the plurality of test subjects are detected from the skeletal data of the plurality of test subjects including the test subject of sarcopenia, the test subject of the sarcopenia preparatory group, and the test subject of the healthy person. In the experiment, one walking cycle after normalization was divided into ten intervals, and an average value of the vertical displacement of the toe of one leg in one interval or two or more consecutive intervals, an average value of the angle of the knee joint of one leg in one interval or two or more consecutive intervals, and an average value of the angle of the ankle joint of one leg in one interval or two or more consecutive intervals were calculated for each test subject.

The target variable is one of a muscle degeneration disease, a muscle degeneration disease preliminary population, and a healthy person, and the target variable is a time-series data average value of displacement in the vertical direction of the toe portion of one leg in the first period of the foothold period, a time-series data average value of displacement in the vertical direction of the toe portion of one leg in the second period of the foothold period, a time-series data average value of an angle of the knee joint of one leg in the third period of the foothold period, a time-series data average value of an angle of the knee joint of one leg in the fourth period of the foot suspension period, a time-series data average value of an angle of the ankle joint of one leg in the fifth period of the foothold period, a time-series data average value of an angle of the ankle joint of one leg in the foothold period and the sixth period of the foot suspension period, and a time average value of an angle of the ankle joint of one leg in the seventh period of the foot suspension period are used as explanatory variables And (4) predicting the model. The first period is a period of 11% to 20% of one walking cycle, the second period is a period of 41% to 60% of one walking cycle, the third period is a period of 41% to 50% of one walking cycle, the fourth period is a period of 61% to 100% of one walking cycle, the fifth period is a period of 11% to 20% of one walking cycle, the sixth period is a period of 31% to 70% of one walking cycle, and the seventh period is a period of 91% to 100% of one walking cycle. The predictive model was evaluated by cross-validation. As the cross-validation, leave-one-out cross-validation is adopted. Then, an ROC curve of a prediction model for which healthy subjects and muscle degeneration preparatory persons are determined is calculated. And further calculating an AUC value of the ROC curve of the prediction model.

The prediction model of the eighteenth modification of the present embodiment is created using as the target variables which of the sarcopenia, the sarcopenia ready population, and the healthy subjects is the subject, and using as the explanatory variables the average value of the displacement in the vertical direction of the toe portion of one leg during 11% to 20% and 41% to 60% of one walking cycle, the average value of the angles of the knee joint of one leg during 41% to 50% and 61% to 100% of one walking cycle, and the average value of the angles of the ankle joint of one leg during 11% to 20%, 31% to 70% and 91% to 100% of one walking cycle. In fig. 40, the vertical axis represents the true positive rate, and the horizontal axis represents the false positive rate. The true positive rate indicates a rate at which the prediction model correctly determines a test subject of the sarcopenia preparation population as the sarcopenia preparation population, and the false positive rate indicates a rate at which the prediction model incorrectly determines a test subject of a healthy subject as the sarcopenia preparation population.

The ROC curve shown in figure 40 is a curve that depicts the true positive rate and the false positive rate of the prediction model, the prediction model is created using as explanatory variables an average value of displacement of a toe portion of one leg in a vertical direction during 11% to 20% of one walking cycle, an average value of displacement of a toe portion of one leg in a vertical direction during 41% to 60% of one walking cycle, an average value of angles of a knee joint of one leg during 41% to 50% of one walking cycle, an average value of angles of a knee joint of one leg during 61% to 100% of one walking cycle, an average value of angles of an ankle joint of one leg during 11% to 20% of one walking cycle, an average value of angles of an ankle joint of one leg during 31% to 70% of one walking cycle, and an average value of angles of an ankle joint of one leg during 91% to 100% of one walking cycle. The AUC value of the ROC curve shown in fig. 40 is 0.861.

In an eighteenth modification of the present embodiment, an average value of the displacement in the vertical direction of the toe portion of one leg during 11% to 20% of one walking cycle, an average value of the displacement in the vertical direction of the toe portion of one leg during 41% to 60% of one walking cycle, an average value of the angle of the knee joint of one leg during 41% to 50% of one walking cycle, an average value of the angle of the knee joint of one leg during 61% to 100% of one walking cycle, an average value of the angle of the ankle joint of one leg during 11% to 20% of one walking cycle, an average value of the angle of the ankle joint of one leg during 31% to 70% of one walking cycle, and an average value of the angle of the ankle joint of one leg during 91% to 100% of one walking cycle are determined as walking parameters.

Furthermore, the average value of the displacement of the toe of one leg in the vertical direction during 11% to 20% of one walking cycle, the average value of the displacement of the toe of one leg in the vertical direction during 41% to 60% of one walking cycle, the average value of the angle of the knee joint of one leg during 41% to 50% of one walking cycle, the average value of the angle of the knee joint of one leg during 61% to 100% of one walking cycle, the prediction model created by using as explanatory variables the average value of the angle of the ankle joint of one leg during 11% to 20% of one walking cycle, the average value of the angle of the ankle joint of one leg during 31% to 70% of one walking cycle, and the average value of the angle of the ankle joint of one leg during 91% to 100% of one walking cycle is determined as the prediction model used by the muscle-wasting determination unit 113.

The walking parameter detecting unit 112 detects time-series data of a vertical displacement of the toe portion of one leg in the first period of the foot standing period, time-series data of a vertical displacement of the toe portion of one leg in the second period of the foot standing period, time-series data of an angle of the knee joint of one leg in the third period of the foot standing period, time-series data of an angle of the knee joint of one leg in the fourth period of the foot suspension period, time-series data of an angle of the ankle joint of one leg in the fifth period of the foot standing period, time-series data of an angle of the ankle joint of one leg in the sixth period of the foot standing period and the foot suspension period, and time-series data of an angle of the ankle joint of one leg in the seventh period of the foot suspension period. The first period is a period of 11% to 20% of one walking cycle, the second period is a period of 41% to 60% of one walking cycle, the third period is a period of 41% to 50% of one walking cycle, the fourth period is a period of 61% to 100% of one walking cycle, the fifth period is a period of 11% to 20% of one walking cycle, the sixth period is a period of 31% to 70% of one walking cycle, and the seventh period is a period of 91% to 100% of one walking cycle.

The walking parameter detecting unit 112 detects time-series data of a displacement in the vertical direction of the toe of one leg during a period of 11% to 20% of one walking cycle, time-series data of a displacement in the vertical direction of the toe of one leg during a period of 41% to 60% of one walking cycle, time-series data of an angle of the knee joint of one leg during a period of 41% to 50% of one walking cycle, time-series data of an angle of the knee joint of one leg during a period of 61% to 100% of one walking cycle, time-series data of angles of ankle joints of one leg during 11% to 20% of one walking cycle, time-series data of angles of ankle joints of one leg during 31% to 70% of one walking cycle, and time-series data of angles of ankle joints of one leg during 91% to 100% of one walking cycle.

The walking parameter detecting unit 112 calculates an average value of time-series data of vertical displacement of the toe portion of one leg during 11% to 20% of one walking cycle, an average value of time-series data of vertical displacement of the toe portion of one leg during 41% to 60% of one walking cycle, an average value of time-series data of angle of knee joint of one leg during 41% to 50% of one walking cycle, an average value of time-series data of angle of knee joint of one leg during 61% to 100% of one walking cycle, an average value of time-series data of angle of ankle joint of one leg during 11% to 20% of one walking cycle, an average value of time-series data of angle of ankle joint of one leg during 31% to 70% of one walking cycle, and a time-series data of angle of ankle joint of one leg during 91% to 100% of one walking cycle Average value.

The memory 12 stores in advance, as input values, an average value of time-series data of a vertical displacement of a toe portion of one leg in a first period of a standing period, an average value of time-series data of a vertical displacement of a toe portion of one leg in a second period of a standing period, an average value of time-series data of an angle of a knee joint of one leg in a third period of a standing period, an average value of time-series data of an angle of a knee joint of one leg in a fourth period of a foot suspension period, an average value of time-series data of an angle of an ankle joint of one leg in a fifth period of a standing period, an average value of time-series data of angles of an ankle joint of one leg in a sixth period of the standing period and the foot suspension period, and an average value of time-series data of an angle of an ankle joint of one leg in a seventh period of the foot suspension period, and the test objects are sarcopenia, A prediction model generated by using either the muscle degeneration preparatory population or the healthy person as an output value.

The memory 12 stores in advance an average value of time-series data of displacement in the vertical direction of the toe portion of one leg during 11% to 20% of one walking cycle, an average value of time-series data of displacement in the vertical direction of the toe portion of one leg during 41% to 60% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during 41% to 50% of one walking cycle, an average value of time-series data of angles of the knee joint of one leg during 61% to 100% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during 11% to 20% of one walking cycle, an average value of time-series data of angles of the ankle joint of one leg during 31% to 70% of one walking cycle, and an average value of time-series data of angles of the ankle joint of one leg during 91% to 100% of one walking cycle as the average values The input value is a prediction model generated using as an output value any one of a sarcopenia, a sarcopenia preparation population, and a healthy subject.

The sarcopenia determination unit 113 determines whether the test subject is sarcopenia using an average value of the time-series data of the vertical displacement of the toe of one leg in the first period and the second period, an average value of the time-series data of the angle of the knee joint of one leg in the third period and the fourth period, and an average value of the time-series data of the angle of the ankle joint of one leg in the fifth period, the sixth period, and the seventh period.

The sarcopenia determination unit 113 uses an average value of time-series data of displacement in the vertical direction during 11% to 20% of one walking cycle of the toe portion of one leg, an average value of time-series data of displacement in the vertical direction during 41% to 60% of one walking cycle of the toe portion of one leg, an average value of time-series data of angles during 41% to 50% of one walking cycle of the knee joint of one leg, an average value of time-series data of angles during 61% to 100% of one walking cycle of the knee joint of one leg, an average value of time-series data of angles during 11% to 20% of one walking cycle of the ankle joint of one leg, an average value of time-series data of angles during 31% to 70% of one walking cycle of the ankle joint of one leg, and an average time-series data of angles during 91% to 100% of one walking cycle of the ankle joint of one leg The value is used to determine whether the test object is a person of sarcopenia, a person of sarcopenia preparation and a healthy person.

The sarcopenia determination unit 113 determines the average value of the time-series data of the vertical displacement of the toe portion of one leg during 11% to 20% of one walking cycle, the average value of the time-series data of the vertical displacement of the toe portion of one leg during 41% to 60% of one walking cycle, the average value of the time-series data of the angle of the knee joint of one leg during 41% to 50% of one walking cycle, the average value of the time-series data of the angle of the knee joint of one leg during 61% to 100% of one walking cycle, the average value of the time-series data of the angle of the ankle joint of one leg during 11% to 20% of one walking cycle, the average value of the time-series data of the angle of the ankle joint of one leg during 31% to 70% of one walking cycle, and the average value of the time-series data of the angle of the ankle joint of one leg during 91% to 100% of one walking cycle The mean value is input to the prediction model, and a determination result indicating whether the test object is sarcopenia, a sarcopenia preparation population, or a healthy subject is obtained from the prediction model.

In this way, the AUC value obtained as a result of the muscle wasting disease preliminary population judged to be 0.560 from the prediction model created using only the displacement of the toe portion of one leg in the vertical direction during the stance phase, the AUC value obtained as a result of the muscle wasting disease preliminary population judged to be 0.626 from the prediction model created using only the displacement of the toe portion of one leg in the vertical direction during the foot suspension phase, the AUC value obtained as a result of the muscle wasting disease preliminary population judged to be 0.537 from the prediction model created using only the angle of the knee joint of one leg in the stance phase, the AUC value obtained as a result of the muscle wasting disease preliminary population judged to be 0.604 from the prediction model created using only the angle of the knee joint of one leg in the foot suspension phase, the AUC value obtained as a result of the muscle wasting disease preliminary population judged to be 0.610 from the prediction model created using only the angle of the ankle joint of one leg in the stance phase, the AUC value obtained from the results of the population prepared for sarcopenia was judged to be 0.622 from a prediction model created using only the ankle joint of one leg in the foot suspension phase.

In response to this, the AUC value obtained as a result of the population ready for muscular attenuation was judged to be 0.861 from the prediction model created using the displacement in the vertical direction of the toe portion of one leg in the stance phase in the first period and the second period, the angle of the knee joint of one leg in the third period in the stance phase, the angle of the knee joint of one leg in the fourth period in the foot suspension phase, the angle of the ankle joint of one leg in the fifth period in the stance phase, the angle of the ankle joint of one leg in the sixth period in the stance phase and the foot suspension phase, and the angle of the ankle joint of one leg in the seventh period in the foot suspension phase.

Therefore, compared to a prediction model created using only the displacement of the toe portion of one leg in the vertical direction during the stance phase, the displacement of the toe portion of one leg in the vertical direction during the foot suspension phase, the angle of the knee joint of one leg in the stance phase, the angle of the knee joint of one leg in the foot suspension phase, the angle of the ankle joint of one leg in the stance phase, and the angle of the ankle joint of one leg in the foot suspension phase, a prediction model created using the displacements of the toe portion of one leg in the vertical direction during the first and second periods, the angle of the knee joint of one leg during the third period of the stance phase, the angle of the knee joint of one leg during the fourth period of the foot suspension phase, the angle of the ankle joint of one leg during the fifth period of the stance phase, the angle of the ankle joint of one leg during the stance and sixth period, and the angle of the ankle joint of one leg during the stance and the seventh period of the ankle joint of one leg in the foot suspension phase, respectively, can determine the deterioration of one leg with high accuracy People with pre-existing symptoms are reduced.

Fig. 41 is a schematic diagram showing an example of the evaluation result screen displayed in the present embodiment.

The display unit 3 displays an evaluation result screen shown in fig. 41. The evaluation result screen includes a muscle degeneration evaluation presentation area 31 and an evaluation message 32 that indicate the past evaluation value of muscle degeneration and the current evaluation value of muscle degeneration. The sarcopenia evaluation presentation area 31 of fig. 41 shows that the evaluation of sarcopenia was performed once a month, the evaluation of sarcopenia in the past six months, and the evaluation of sarcopenia in the present month.

The evaluation value of sarcopenia is a value indicating that the test subject calculated by the predictive model is likely to be sarcopenia. The value indicating that the test subject is likely to be sarcopenia is expressed by, for example, 0.0 to 2.0. The evaluation result presentation section 114 presents a value indicating that the test subject is likely to be sarcopenia converted to a percentage as an evaluation value of sarcopenia.

In the second modification of the present embodiment, the average value of the time-series data of the angle of the knee joint of one leg of the test subjects suffering from sarcopenia during the period of 50% to 60% of one walking cycle was 15.3 degrees, and the average value of the time-series data of the angle of the knee joint of one leg of the test subjects suffering from healthy persons during the period of 50% to 60% of one walking cycle was 9.3 degrees. Here, the evaluation result presentation unit 114 may normalize the minimum value of 9.3 degrees to 0 and the maximum value of 15.3 degrees to 1, and convert the average value of the time-series data of the angle of the knee joint of one leg of the test subject in the period of 50% to 60% of one walking cycle, which is calculated by the walking parameter detection unit 112, to a value between 0 and 1. The evaluation result presentation unit 114 may present the converted value as a percentage as an evaluation value of the sarcopenia.

In addition, when the evaluation value of the muscular attenuation disease in the past is displayed in addition to the evaluation value of the present muscular attenuation disease, the muscular attenuation disease determination section 113 stores the evaluation value of the muscular attenuation disease in the memory 12.

The muscle degeneration evaluation presentation area 31 may display whether or not the test subject is muscle degeneration as the evaluation result. The sarcopenia evaluation presentation area 31 may display, as the evaluation result, which of the sarcopenia, the sarcopenia preparation population, and the healthy subject is the test subject.

Further, it was shown that, for example, the risk of "sarcopenia" was reduced more than the last month, and a good state was maintained. Please keep living in this way. "such an evaluation message 32. The evaluation result presenting unit 114 reads out the evaluation message 32 shown in fig. 41 from the memory 12 and outputs the message to the display unit 3 when the evaluation value of sarcopenia in this month is lower than the evaluation value of sarcopenia in the previous month and the evaluation value of sarcopenia in this month is lower than 0.5.

In the present embodiment, the present estimated value of the present sarcopenia and the previous sarcopenia are displayed, but the present invention is not limited to this, and only the present estimated value of the sarcopenia may be displayed. In this case, the sarcopenia determination section 113 may not store the evaluation value of sarcopenia in the memory 12.

The camera 2 of the present embodiment may be a security camera installed in front of the entrance, a slave camera of an intercom, or a monitoring camera installed indoors. The display unit 3 may be a monitor of a smartphone, a tablet computer, or an intercom.

In the present embodiment, the walking parameter detection unit 112 extracts the bone data based on the moving image data acquired from the camera 2, but the present invention is not limited to this, and the bone data may be extracted by a motion capture system (motion capture system). The motion capture system may operate based on any of optical, magnetic, mechanical, and inertial sensor types. For example, an optical motion capture system captures an image of a test object with a label attached to a joint portion with a camera, and detects the position of the label from the captured image. The walking parameter detection unit 112 acquires skeletal data of the test subject from the position data detected by the motion capture system. As the optical motion capture system, for example, a three-dimensional motion analysis device manufactured by intet Reha co, Ltd.

The motion capture system may further include a depth sensor and a color camera, and may automatically extract position information of an articulation point of the test object from the video image and detect the posture of the test object. In this case, the test object does not need to be labeled. As such a motion capture system, for example, Kinect manufactured by microsoft corporation can be used.

When the walking motion is measured by the motion capture system, it is preferable to extract the angle of the ankle joint, the angle of the knee joint, or the displacement of the toe in the vertical direction during the walking motion from the position coordinates, and detect the feature amount of the walking motion from the extracted angle or displacement.

In the above embodiments, each component may be configured by dedicated hardware, or may be realized by executing a software program suitable for each component. Each component may be realized by causing a program execution unit such as a CPU or a processor to read a software program recorded in a storage medium such as a hard disk or a semiconductor memory.

A part or all of the functions of the apparatus according to the embodiment of the present invention may be typically realized as an lsi (large Scale integration) integrated circuit. Some or all of these functions may be formed into chips, or may be formed into chips including some or all of these functions. The integrated circuit is not limited to the LSI, and may be realized by a dedicated circuit or a general-purpose processor. An fpga (field Programmable Gate array) which can be programmed after LSI manufacturing or a reconfigurable processor which can reconfigure connection or setting of circuit cells inside LSI can be used.

In addition, a part or all of the functions of the apparatus according to the embodiment of the present invention may be realized by causing a processor such as a CPU to execute a program.

Further, the numbers used in the above are examples given for specifically explaining the present invention, and the present invention is not limited to these exemplified numbers.

The order in which the steps shown in the flowcharts are executed is merely an example given for specifically explaining the present invention, and may be an order other than the above, as long as the same effects can be obtained. Moreover, some of the above steps may be performed concurrently with (or in parallel with) other steps.

The technique according to the present invention is useful as a technique for evaluating sarcopenia based on walking movements of a test subject because it can evaluate sarcopenia easily and with high accuracy.

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Method and apparatus for evaluating muscle degeneration and storage medium

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