阅读说明：本技术 监测颈椎曲度的方法和装置 (Method and device for monitoring cervical curvature ) 是由 段婉茹 陈赞 范充 唐文涛 张逯颖 曾明发 谢朝建 于 2021-07-27 设计创作，主要内容包括：本发明提供了一种监测颈椎曲度的方法和装置,涉及颈椎监测技术领域。所述方法包括步骤如下：S1、采集静态数据；S2、采集动态数据；S3、数据标注；S4、聚合矩阵；S5、多组采集；S6、训练模型；S7、用户监测；所述装置包括：数据存储模块、算法模块、智能提醒模块和通信模块。本发明中,通过对监测对象的颈椎关键部位进行监测,采集静态和动态数据,再由医生判断监测对象的颈椎是否健康,对数据进行标注,数据与标注聚合得到静态矩阵和动态矩阵,训练模型得到静态模型和动态模型,通过两个模型,用户能够随时随地监测自己的颈椎健康情况,无需去医院挂号拍片检查,节约了大量的时间成本和经济成本,并且能够在病症早期就得到注意,便于医治康复。(The invention provides a method and a device for monitoring cervical vertebra curvature, and relates to the technical field of cervical vertebra monitoring. The method comprises the following steps: s1, collecting static data; s2, collecting dynamic data; s3, marking data; s4, aggregating the matrix; s5, collecting a plurality of groups; s6, training a model; s7, monitoring by a user; the device comprises: the intelligent reminding system comprises a data storage module, an algorithm module, an intelligent reminding module and a communication module. According to the invention, by monitoring the key part of the cervical vertebra of the monitored object, acquiring static and dynamic data, judging whether the cervical vertebra of the monitored object is healthy by a doctor, marking the data, aggregating the data and the marking to obtain a static matrix and a dynamic matrix, training the model to obtain the static model and the dynamic model, and through the two models, a user can monitor the health condition of the cervical vertebra at any time and any place, does not need to go to a hospital for registration and shooting for examination, saves a large amount of time cost and economic cost, can pay attention to the early stage of disease, and is convenient for medical rehabilitation.)

1. A method of monitoring cervical curvature, the method comprising the steps of:

s1, collecting static data:

the device for monitoring the curvature of the cervical vertebra is worn at the cervical vertebra of the monitored object, and the static data of the cervical vertebra is read and calculated by the device

D ═ static right side, static left side, static lower section ];

s2, collecting dynamic data:

a monitoring object wearing device performs the following actions: left turn, right turn, left yaw, right yaw, head up, head down; reading and calculating cervical vertebra dynamic data through the device

E ═ left turn right side, left turn left side, left turn lower section,

right side surface of the right-turning head, left side surface of the right-turning head, lower section of the right-turning head,

a left oblique head right side surface, a left oblique head left side surface, a left oblique head lower section,

right side surface of right eccentric head, left side surface of right eccentric head, lower section of right eccentric head,

the right side surface of the head raising, the left side surface of the head raising, the lower section of the head raising,

the left side of the head, the right side of the head, the lower cross section of the head ];

s3, data annotation:

carrying out X-ray imaging on a monitored object, judging whether the cervical vertebra curvature of the monitored object is healthy by a doctor, and labeling static data and dynamic data by using a judgment result;

s4, aggregation matrix:

directly connecting and aggregating the static data and the labels into a static matrix; the static matrix

D' ═ static right side, static left side, static lower section, healthy OR diseased;

aggregating the static data, the dynamic data and the labels into a dynamic matrix; the dynamic matrix

E' ═ left turn right side-static right side, left turn left side-static left side, left turn lower cross-static lower cross-section,

right side of right-turn head-static right side, left side of right-turn head-static left side, lower cross-section of right-turn head-static lower cross-section,

left eccentric right side-static right side, left eccentric left side-static left side, left eccentric lower cross-section-static lower cross-section,

right side of right deflection head-static right side, left side of right deflection head-static left side, right deflection head lower cross-section-static lower cross-section,

a head-up right side surface-a static right side surface, a head-up left side surface-a static left side surface, a head-up lower cross-section-a static lower cross-section,

low head right side-static right side, low head left side-static left side, low head lower cross-section-static lower cross-section, healthy OR diseased ];

the symbol "-" represents the difference value of each corresponding edge;

s5, multi-group collection:

monitoring a plurality of different monitoring objects, and repeating the steps S1-S4 to obtain a plurality of groups of static matrixes and dynamic matrixes;

s6, training a model:

respectively inputting a plurality of groups of static matrixes and dynamic matrixes into a machine learning algorithm training model to obtain a static model and a dynamic model;

s7, user monitoring:

monitoring the user, repeating the steps S1-S2, and respectively inputting the static data and the dynamic data of the user into the model to obtain a monitoring result; the monitoring result judgment standard is as follows: both models considered diseased; only one of the two models was considered to be at risk; both models considered healthy.

2. The method of monitoring curvature of cervical vertebrae as set forth in claim 1, wherein the apparatus for monitoring curvature of cervical vertebrae in S1 comprises:

a stent, the stent comprising: first stent a1, second stent a2, third stent A3, fourth stent a4, fifth stent a5, sixth stent a 6;

a bending sensor comprising: a first bending sensor B1, a second bending sensor B2, a third bending sensor B3, a fourth bending sensor B4, a fifth bending sensor B5; adjacent stents are connected by corresponding bending sensors.

3. The method for monitoring cervical curvature according to claim 2, wherein in S1, the first bracket a1 and the sixth bracket a6 are respectively attached to the top of the binaural root of the subject, the third curvature sensor B3 is attached to the posterior cervical prominence, and the front ends of the third bracket A3 and the fourth bracket a4 are respectively attached to the extreme distal prominences of the two clavicles.

4. The method of claim 3, wherein the face data in the static data and the dynamic data are each composed of three side lengths corresponding thereto;

the static right side surface is a triangular surface formed by a second bracket A2, a first bending sensor B1 and a second bending sensor B2, and the three sides of the triangular surface are respectively as follows:

L2，

the static left side surface is a triangular surface formed by a fifth bracket A5, a fourth bending sensor B4 and a fifth bending sensor B5, and the three sides of the triangular surface are respectively as follows:

L5，

the section is a triangular surface formed by a third bracket A3, a fourth bracket A4 and a third bending sensor B3 under the static state, and three sides of the triangular surface are respectively as follows:

L3，L4，

the static matrix

The bending sensors are arranged on the two ends of the first support A1, the second support A2, the third support A3, the fourth support A4, the fifth support A5 and the sixth support A6 respectively, namely the bending sensors on the two ends of the supports are arranged at L1, L2, L3, L4, L5 and L6 respectively;

b1, B2, B3, B4 and B5 are numerical values of a first bending sensor B1, a second bending sensor B2, a third bending sensor B3, a fourth bending sensor B4 and a fifth bending sensor B5 respectively;

and f (x) is the relationship between the bending sensor value and the angle represented by that value.

5. The method for monitoring curvature of cervical vertebrae as claimed in claim 1, wherein in S6, the machine learning algorithm of the static model and the dynamic model includes but is not limited to: linear regression, Logistic regression, support vector machine, K-means approximation, random forest, naive Bayes, decision tree.

6. The method of claim 1, wherein the step of determining whether the cervical vertebrae of the subject are healthy by the doctor in step S3 comprises: monitoring whether the static posture and the X-ray imaging of the object are obviously distorted or not and whether the movement of the neck of the object is limited or not; in S6, the machine learning algorithm models a doctor decision process and models a static matrix and a dynamic matrix of the monitored object, respectively.

7. The method for monitoring the curvature of the cervical vertebrae as claimed in claim 1, further comprising the steps of:

s8, daily monitoring:

in the step S7, after the user monitors and obtains the result, the data of the user is continuously measured to obtain the daily behavior data of the user, and a timestamp and the result monitored in the step S7 are added to the tail of the daily behavior data; the format of the daily behavior data is as follows:

[ daily static right side, daily static left side, daily static lower section, time, healthy or with risk of disease or illness ];

s9, training a model of daily behaviors:

the daily behavior data of a plurality of users are uploaded to the cloud server, the cloud server inputs the neural network algorithm into the cloud server, the model is further trained, the daily model is obtained, the health condition of the cervical vertebra of the user can be judged by the daily behavior of the user through the daily model, the abnormal posture of the user can be reminded, and once the user continuously appears actions which can lead to cervical spondylosis, the intelligent reminding module can remind a monitored object in real time through sound or vibration.

8. An apparatus for monitoring cervical curvature, the apparatus comprising: the intelligent reminding system comprises a data storage module, an algorithm module, an intelligent reminding module and a communication module;

the curvature sensor generates data and transmits the data to the data storage module, the algorithm module extracts the data from the data storage module and calculates the data, the algorithm module transmits a calculation result to the intelligent reminding module, and the intelligent reminding module reminds a user by using sound and/or vibration according to the calculation result; the communication module extracts data from the data storage module and transmits the data to the mobile phone APP or the cloud server, the mobile phone APP transmits the data to the cloud server, the cloud server improves the existing algorithm by using big data, an AI technology and doctor review, doctor suggestions are combined, a user cervical vertebra monitoring report is generated, the mobile phone APP can download the monitoring report, and the cloud server can update the algorithm module in the equipment once a more appropriate model is trained according to more data.

9. The apparatus of claim 8, wherein the data storage module comprises SD and/or EMMC; the algorithm module comprises an FPGA and/or an AI; the intelligent reminding module comprises a loudspeaker and/or a buzzer and/or a vibrating motor; the communication module comprises wifi and/or Bluetooth and/or LTE.

Technical Field

The invention relates to the technical field of cervical vertebra monitoring, in particular to a method and a device for monitoring cervical vertebra curvature.

Background

With the development of society, people have longer and longer time to watch mobile phones and work and study on desk. The problem of cervical curvature straightening is more serious due to a long-time head-lowering state. The normal cervical vertebra is convexly curved forwards, but the normal radian of the cervical vertebra disappears due to excessive anteflexion of the neck, so that the curvature of the cervical vertebra is straightened, which is the most common pathological basis for cervical spondylosis.

According to epidemiological investigation, the incidence rate of cervical curvature straightening in China is in a straight-line rising trend in the last decade, and gradually becomes a low-age trend in recent years, so that the incidence rate of cervical curvature straightening in the old is more serious than 40%. Therefore, it is necessary to monitor the mobility of the cervical vertebrae.

There are two common methods for examining the curvature of cervical vertebrae: 1. x-ray imaging; 2. the cervical vertebra movement is measured using an angular velocity sensor. Both of these approaches have major limitations. The X-ray imaging operation is complex, the cost is high, and only the doctor goes to the hospital to shoot and analyzes the imaging result. The angular velocity sensor can only measure dynamic data, and requires a monitored object to carry out cervical vertebra movement according to specific steps, so that the use is inconvenient. In addition, the angular velocity sensor cannot directly measure the curvature of the cervical vertebrae, and can only roughly estimate through an inaccurate range of motion.

Disclosure of Invention

Technical problem to be solved

Aiming at the defects of the prior art, the invention provides a method and a device for monitoring cervical vertebra curvature, and solves the problems of inconvenience and high cost in cervical vertebra curvature monitoring.

(II) technical scheme

In order to achieve the purpose, the invention is realized by the following technical scheme:

a method of monitoring cervical curvature, the method comprising the steps of:

s1, collecting static data:

the device for monitoring the curvature of the cervical vertebra is worn at the cervical vertebra of the monitored object, and the static data of the cervical vertebra is read and calculated by the device

D ═ static right side, static left side, static lower section ];

s2, collecting dynamic data:

a monitoring object wearing device performs the following actions: left turn, right turn, left yaw, right yaw, head up, head down; reading and calculating cervical vertebra dynamic data through the device

E ═ left turn right side, left turn left side, left turn lower section,

right side surface of the right-turning head, left side surface of the right-turning head, lower section of the right-turning head,

a left oblique head right side surface, a left oblique head left side surface, a left oblique head lower section,

right side surface of right eccentric head, left side surface of right eccentric head, lower section of right eccentric head,

the right side surface of the head raising, the left side surface of the head raising, the lower section of the head raising,

the left side of the head, the right side of the head, the lower cross section of the head ];

s3, data annotation:

carrying out X-ray imaging on a monitored object, judging whether the cervical vertebra curvature of the monitored object is healthy by a doctor, and labeling static data and dynamic data by using a judgment result;

s4, aggregation matrix:

directly connecting and aggregating the static data and the labels into a static matrix; the static matrix

D' ═ static right side, static left side, static lower section, healthy OR diseased;

aggregating the static data, the dynamic data and the labels into a dynamic matrix; the dynamic matrix

E' ═ left turn right side-static right side, left turn left side-static left side, left turn lower cross-static lower cross-section,

right side of right-turn head-static right side, left side of right-turn head-static left side, lower cross-section of right-turn head-static lower cross-section,

left eccentric right side-static right side, left eccentric left side-static left side, left eccentric lower cross-section-static lower cross-section,

right side of right deflection head-static right side, left side of right deflection head-static left side, right deflection head lower cross-section-static lower cross-section,

a head-up right side surface-a static right side surface, a head-up left side surface-a static left side surface, a head-up lower cross-section-a static lower cross-section,

low head right side-static right side, low head left side-static left side, low head lower cross-section-static lower cross-section, healthy OR diseased ];

the symbol "-" represents the difference value of each corresponding edge;

s5, multi-group collection:

monitoring a plurality of different monitoring objects, and repeating the steps S1-S4 to obtain a plurality of groups of static matrixes and dynamic matrixes;

s6, training a model:

respectively inputting a plurality of groups of static matrixes and dynamic matrixes into a machine learning algorithm training model to obtain a static model and a dynamic model;

s7, user monitoring:

monitoring the user, repeating the steps S1-S2, and respectively inputting the static data and the dynamic data of the user into the model to obtain a monitoring result; the monitoring result judgment standard is as follows: both models considered diseased; only one of the two models was considered to be at risk; both models considered healthy.

Preferably, the apparatus for monitoring cervical curvature in S1 includes:

a stent, the stent comprising: first stent a1, second stent a2, third stent A3, fourth stent a4, fifth stent a5, sixth stent a 6;

a bending sensor comprising: a first bending sensor B1, a second bending sensor B2, a third bending sensor B3, a fourth bending sensor B4, a fifth bending sensor B5; adjacent stents are connected by corresponding bending sensors.

Preferably, in S1, the first stent a1 and the sixth stent a6 are respectively tightly attached to the top of the two roots of the ears of the monitored subject, the third bending sensor B3 is tightly attached to the back of the neck, and the front ends of the third stent A3 and the fourth stent a4 are respectively tightly attached to the extreme ends of the two clavicles.

Preferably, the surface data in the static data and the dynamic data are both composed of three corresponding edge lengths;

the static right side surface is a triangular surface formed by a second bracket A2, a first bending sensor B1 and a second bending sensor B2, and the three sides of the triangular surface are respectively as follows:

L2，

the static left side surface is a triangular surface formed by a fifth bracket A5, a fourth bending sensor B4 and a fifth bending sensor B5, and the three sides of the triangular surface are respectively as follows:

L5，

the section is a triangular surface formed by a third bracket A3, a fourth bracket A4 and a third bending sensor B3 under the static state, and three sides of the triangular surface are respectively as follows:

L3，L4，

the static matrix

L5，L3，L4，Healthy OR disease]；

The bending sensors are arranged on the two ends of the first support A1, the second support A2, the third support A3, the fourth support A4, the fifth support A5 and the sixth support A6 respectively, namely the bending sensors on the two ends of the supports are arranged at L1, L2, L3, L4, L5 and L6 respectively;

b1, B2, B3, B4 and B5 are numerical values of a first bending sensor B1, a second bending sensor B2, a third bending sensor B3, a fourth bending sensor B4 and a fifth bending sensor B5 respectively;

and f (x) is the relationship between the bending sensor value and the angle represented by that value.

Preferably, in S6, the machine learning algorithms of the static model and the dynamic model include, but are not limited to: linear regression, Logistic regression, support vector machine, K-means approximation, random forest, naive Bayes, decision tree.

Preferably, in S3, the determining, by the doctor, whether the cervical vertebrae of the subject are healthy includes: monitoring whether the static posture and the X-ray imaging of the object are obviously distorted or not and whether the movement of the neck of the object is limited or not; in S6, the machine learning algorithm models a doctor decision process and models a static matrix and a dynamic matrix of the monitored object, respectively.

Preferably, the method further comprises the steps of:

s8, daily monitoring:

in the step S7, after the user monitors and obtains the result, the data of the user is continuously measured to obtain the daily behavior data of the user, and a timestamp and the result monitored in the step S7 are added to the tail of the daily behavior data; the format of the daily behavior data is as follows:

[ daily static right side, daily static left side, daily static lower section, time, healthy or with risk of disease or illness ];

s9, training a model of daily behaviors:

the daily behavior data of a plurality of users are uploaded to the cloud server, the cloud server inputs the neural network algorithm into the cloud server, the model is further trained, the daily model is obtained, the health condition of the cervical vertebra of the user can be judged by the daily behavior of the user through the daily model, the abnormal posture of the user can be reminded, and once the user continuously appears actions which can lead to cervical spondylosis, the intelligent reminding module can remind a monitored object in real time through sound or vibration.

A device for monitoring cervical curvature, the device comprising: the intelligent reminding system comprises a data storage module, an algorithm module, an intelligent reminding module and a communication module;

the curvature sensor generates data and transmits the data to the data storage module, the algorithm module extracts the data from the data storage module and calculates the data, the algorithm module transmits a calculation result to the intelligent reminding module, and the intelligent reminding module reminds a user by using sound and/or vibration according to the calculation result; the communication module extracts data from the data storage module and transmits the data to the mobile phone APP or the cloud server, the mobile phone APP transmits the data to the cloud server, the cloud server improves the existing algorithm by using big data, an AI technology and doctor review, doctor suggestions are combined, a user cervical vertebra monitoring report is generated, the mobile phone APP can download the monitoring report, and the cloud server can update the algorithm module in the equipment once a more appropriate model is trained according to more data.

Preferably, the data storage module comprises an SD and/or an EMMC; the algorithm module comprises an FPGA and/or an AI; the intelligent reminding module comprises a loudspeaker and/or a buzzer and/or a vibrating motor; the communication module comprises wifi and/or Bluetooth and/or LTE.

(III) advantageous effects

The invention provides a method and a device for monitoring cervical curvature. Compared with the prior art, the method has the following beneficial effects:

according to the invention, by monitoring the key part of the cervical vertebra of the monitored object, acquiring static and dynamic data, judging whether the cervical vertebra of the monitored object is healthy by a doctor, marking the data, aggregating the data and the marking to obtain a static matrix and a dynamic matrix, training the model to obtain the static model and the dynamic model, and through the two models, a user can monitor the health condition of the cervical vertebra at any time and any place, does not need to go to a hospital for registration and shooting for examination, saves a large amount of time cost and economic cost, can pay attention to the early stage of disease, and is convenient for medical rehabilitation.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a flow chart of cervical curvature monitoring according to an embodiment of the present invention;

fig. 2 is a schematic structural view of the device for monitoring cervical curvature in the embodiment of the invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The embodiment of the application solves the problems of inconvenience and high cost in cervical curvature monitoring by providing the method and the device for monitoring the cervical curvature.

In order to solve the technical problems, the general idea of the embodiment of the application is as follows:

in the embodiment of the invention, the cervical vertebra key part of the monitored object is monitored, static and dynamic data are collected, a doctor judges whether the cervical vertebra of the monitored object is healthy or not, the data are labeled, the data and the label are aggregated to obtain the static matrix and the dynamic matrix, and the model is trained to obtain the static model and the dynamic model.

In addition, the device can remind the user in real time to avoid the user to continuously carry out the action of having the injury to the cervical vertebra, and the device can also upload the data of monitoring object through APP and the cloud server that connect to diagnose by professional doctor, issue the diagnosis report that AI and doctor made jointly.

In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.

Example (b):

as shown in fig. 1, the present invention provides a method for monitoring cervical curvature, the method comprising the steps of:

s1, collecting static data:

the device for monitoring the curvature of the cervical vertebra is worn at the cervical vertebra of the monitored object, and the static data of the cervical vertebra is read and calculated by the device

D ═ static right side, static left side, static lower section ];

s2, collecting dynamic data:

a monitoring object wearing device performs the following actions: left turn, right turn, left yaw, right yaw, head up, head down; reading and calculating cervical vertebra dynamic data through the device

E ═ left turn right side, left turn left side, left turn lower section,

right side surface of the right-turning head, left side surface of the right-turning head, lower section of the right-turning head,

a left oblique head right side surface, a left oblique head left side surface, a left oblique head lower section,

right side surface of right eccentric head, left side surface of right eccentric head, lower section of right eccentric head,

the right side surface of the head raising, the left side surface of the head raising, the lower section of the head raising,

the left side of the head, the right side of the head, the lower cross section of the head ];

s3, data annotation:

carrying out X-ray imaging on a monitored object, judging whether the cervical vertebra curvature of the monitored object is healthy by a doctor, and labeling static data and dynamic data by using a judgment result;

s4, aggregation matrix:

directly connecting and aggregating the static data and the labels into a static matrix; the static matrix

D' ═ static right side, static left side, static lower section, healthy OR diseased;

aggregating the static data, the dynamic data and the labels into a dynamic matrix; the dynamic matrix

E' ═ left turn right side-static right side, left turn left side-static left side, left turn lower cross-static lower cross-section,

right side of right-turn head-static right side, left side of right-turn head-static left side, lower cross-section of right-turn head-static lower cross-section,

left eccentric right side-static right side, left eccentric left side-static left side, left eccentric lower cross-section-static lower cross-section,

right side of right deflection head-static right side, left side of right deflection head-static left side, right deflection head lower cross-section-static lower cross-section,

a head-up right side surface-a static right side surface, a head-up left side surface-a static left side surface, a head-up lower cross-section-a static lower cross-section,

low head right side-static right side, low head left side-static left side, low head lower cross-section-static lower cross-section, healthy OR diseased ];

the symbol "-" represents the difference value of each corresponding edge;

s5, multi-group collection:

monitoring a plurality of different monitoring objects, and repeating the steps S1-S4 to obtain a plurality of groups of static matrixes and dynamic matrixes;

s6, training a model:

respectively inputting a plurality of groups of static matrixes and dynamic matrixes into a machine learning algorithm training model to obtain a static model and a dynamic model;

s7, user monitoring:

monitoring the user, repeating the steps S1-S2, and respectively inputting the static data and the dynamic data of the user into the model to obtain a monitoring result; the monitoring result judgment standard is as follows: both models considered diseased; only one of the two models was considered to be at risk; both models considered healthy.

As shown in fig. 2, the apparatus includes:

a stent, the stent comprising: first stent a1, second stent a2, third stent A3, fourth stent a4, fifth stent a5, sixth stent a 6;

a bending sensor comprising: a first bending sensor B1, a second bending sensor B2, a third bending sensor B3, a fourth bending sensor B4, a fifth bending sensor B5; adjacent stents are connected by corresponding bending sensors.

The first support A1 and the sixth support A6 are respectively tightly attached to the top of the two ears of the monitored object, the third bending sensor B3 is tightly attached to the back of the neck, and the front ends of the third support A3 and the fourth support A4 are respectively tightly attached to the extreme ends of the two clavicles.

As shown in fig. 1, the apparatus further comprises: the intelligent reminding system comprises a data storage module, an algorithm module, an intelligent reminding module and a communication module;

the curvature sensor generates data and transmits the data to the data storage module, the algorithm module extracts the data from the data storage module and calculates the data, the algorithm module transmits a calculation result to the intelligent reminding module, and the intelligent reminding module reminds a user by using sound or vibration according to the calculation result; the communication module extracts data from the data storage module and transmits the data to the mobile phone APP or the cloud server, the mobile phone APP can continue to transmit the data to the cloud server, the cloud server improves the existing algorithm by using big data, an AI technology and doctor review, the existing algorithm can be combined with doctor suggestions, a user cervical vertebra monitoring report is generated, the mobile phone APP can download the monitoring report, and the cloud server can update the algorithm module in the equipment once a more appropriate model is trained according to more data.

The data storage module comprises an SD and/or an EMMC; the algorithm module comprises an FPGA and/or an AI; the intelligent reminding module comprises a loudspeaker and/or a buzzer and/or a vibrating motor; the communication module comprises wifi and/or Bluetooth and/or LTE.

L1, L2, L3, L4, L5 and L6 are respectively the linear distances between two end points of a first bracket a1, a second bracket a2, a third bracket A3, a fourth bracket a4, a fifth bracket a5 and a sixth bracket A6, namely the linear distances between the bending sensors at two ends of the brackets, and the brackets are all rigid bodies with fixed shapes, so that L1 to L6 are all constants.

B1, B2, B3, B4 and B5 are numerical values of a first bending sensor B1, a second bending sensor B2, a third bending sensor B3, a fourth bending sensor B4 and a fifth bending sensor B5, respectively.

f (x) is the relationship between the value of the bending sensor and the angle represented by that value, which function is provided by the manufacturer of the bending sensor, so that f (b1) is the angle between first stent a1 and second stent a 2.

The surface data in the static data and the dynamic data are both composed of corresponding three-edge lengths;

according to sine theorem, knowing two corners and the length of the included side of the two corners can determine the triangle and calculate the length of the remaining two sides;

according to the cosine theorem, the length of two sides and the included angle thereof are known to determine the triangle and calculate the length of the remaining side;

the static right side is thus a triangular surface formed by the second bracket a2, the first bending sensor B1 and the second bending sensor B2, the three sides of which are:

L2，

the static left side surface is a triangular surface formed by a fifth bracket A5, a fourth bending sensor B4 and a fifth bending sensor B5, and the three sides of the triangular surface are respectively as follows:

L5，

the section is a triangular surface formed by a third bracket A3, a fourth bracket A4 and a third bending sensor B3 under the static state, and three sides of the triangular surface are respectively as follows:

L3，L4，

the static matrix

L5，L3，L4，Healthy OR disease]。

In S6, the machine learning algorithms of the static model and the dynamic model include, but are not limited to: linear regression, Logistic regression, support vector machine, K-means approximation, random forest, naive Bayes, decision tree.

In S3, the determining, by the doctor, whether the cervical vertebrae of the monitoring subject are healthy includes: monitoring whether the static posture and the X-ray imaging of the object are obviously distorted or not and whether the movement of the neck of the object is limited or not; in S6, the machine learning algorithm models a doctor decision process and models a static matrix and a dynamic matrix of the monitored object, respectively.

The method further comprises the steps of:

s8, daily monitoring:

in the step S7, after the user monitors and obtains the result, the data of the user is continuously measured to obtain the daily behavior data of the user, and a timestamp and the result monitored in the step S7 are added to the tail of the daily behavior data; the format of the daily behavior data is as follows:

[ daily static right side, daily static left side, daily static lower section, time, healthy or with risk of disease or illness ];

s9, training a model of daily behaviors:

the daily behavior data of a plurality of users are uploaded to the cloud server, the cloud server inputs the neural network algorithm into the cloud server, the model is further trained, the daily model is obtained, the health condition of the cervical vertebra of the user can be judged by the daily behavior of the user through the daily model, the abnormal posture of the user can be reminded, and once the user continuously appears actions which can lead to cervical spondylosis, the intelligent reminding module can remind a monitored object in real time through sound or vibration.

In summary, compared with the prior art, the invention has the following beneficial effects:

1. in the embodiment of the invention, the cervical vertebra key part of the monitored object is monitored, static and dynamic data are collected, a doctor judges whether the cervical vertebra of the monitored object is healthy or not, the data are labeled, the data and the label are aggregated to obtain the static matrix and the dynamic matrix, and the model is trained to obtain the static model and the dynamic model.

2. In the embodiment of the invention, the device can remind the user in real time so as to prevent the user from continuously performing the action of injuring the cervical vertebra, and the device can upload the data of the monitored object through the connected APP and the cloud server, diagnose by a professional doctor and issue a diagnosis report jointly made by an AI and the doctor.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.