阅读说明：本技术 评估肌肉疲劳 (Assessment of muscle fatigue ) 是由 F·萨尔托尔 于 2020-03-20 设计创作，主要内容包括：根据一个方面,提供了一种用于评估对象的至少一块肌肉中的肌肉疲劳的计算机实施的方法。所述方法包括：(i)在第一时间段内获得所述至少一块肌肉的肌肉收缩的第一组测量结果；(ii)基于根据所述第一组测量结果确定的肌肉收缩特征的值来形成第一频率分布；(iii)确定所述第一频率分布的第一分布拟合；(iv)确定所述第一分布拟合是否在统计学上是稳定的；(v)如果所述第一分布拟合被确定为在统计学上是稳定的,则根据所述第一分布拟合来确定针对分布拟合特征的第一值；(vi)将所述第一值与针对所述分布拟合特征的第二值进行比较,以确定在所述第一时间段内所述至少一块肌肉的所述疲劳的度量,其中,针对所述分布拟合特征的所述第二值涉及第二频率分布的第二分布拟合,其中,所述第二频率分布是基于根据在不同于所述第一时间段的第二时间段内所述至少一块肌肉的肌肉收缩的第二组测量结果确定的所述肌肉收缩特征的值来形成的；并且(vii)输出表示所确定的所述疲劳的度量的信号。(According to one aspect, a computer-implemented method for assessing muscle fatigue in at least one muscle of a subject is provided. The method comprises the following steps: (i) obtaining a first set of measurements of muscle contraction of the at least one muscle over a first time period; (ii) forming a first frequency distribution based on values of a muscle contraction characteristic determined from the first set of measurements; (iii) determining a first distribution fit of the first frequency distribution; (iv) determining whether the first distribution fit is statistically stable; (v) determining a first value for a distribution fit feature from the first distribution fit if the first distribution fit is determined to be statistically stable; (vi) comparing the first value to a second value for the distribution-fitted feature to determine a measure of the fatigue of the at least one muscle over the first time period, wherein the second value for the distribution-fitted feature relates to a second distribution fit of a second frequency distribution, wherein the second frequency distribution is formed based on values of the muscle contraction feature determined from a second set of measurements of muscle contraction of the at least one muscle over a second time period different from the first time period; and (vii) outputting a signal representative of the determined measure of fatigue.)

1. A computer-implemented method for assessing muscle fatigue in at least one muscle of a subject, the method comprising:

(i) obtaining a first set of measurements of muscle contraction of the at least one muscle over a first time period;

(ii) forming a first frequency distribution based on values of a muscle contraction characteristic determined from the first set of measurements;

(iii) determining a first distribution fit of the first frequency distribution;

(iv) determining whether the first distribution fit is statistically stable;

(v) determining a first value for a distribution fit feature from the first distribution fit if the first distribution fit is determined to be statistically stable;

(vi) comparing the first value to a second value for the distribution-fitted feature to determine a measure of the fatigue of the at least one muscle over the first time period, wherein the second value for the distribution-fitted feature relates to a second distribution fit of a second frequency distribution, wherein the second frequency distribution is formed based on values of the muscle contraction feature determined from a second set of measurements of muscle contraction of the at least one muscle over a second time period different from the first time period; and is

(vii) Outputting a signal representative of the determined measure of fatigue.

2. The method of claim 1, wherein the measurement of muscle contraction is obtained by one or more of: a surface electromyography (sEMG) sensor, an electromyography (MMG) sensor, an accelerometer, a strain gauge sensor, a piezoelectric sensor, a stretch sensor, or a deformation sensor.

3. The method of claim 1 or 2, wherein the first frequency distribution is formed by:

processing the first set of measurements to determine values of the muscle contraction characteristic for muscle contraction over the first time period; and is

Forming the first frequency distribution from the determined values of the muscle contraction characteristic.

4. The method of any one of claims 1-3, wherein the muscle contraction characteristic is any one of: the intensity of the muscle contraction, the duration of the muscle contraction, and the tremor in the muscle contraction.

5. The method of any one of claims 1-4, wherein step (iv) comprises:

determining a measure of variability of the first distribution fit; and

comparing the determined measure of variability to a threshold;

wherein the first distribution fit is determined to be statistically stable if the determined measure of variability is below the threshold.

6. The method of any one of claims 1-5, wherein the distribution fitting feature comprises any one of:

a maximum value of the muscle contraction characteristic;

a minimum value of the muscle contraction characteristic;

a scale of values fitted to the first distribution;

a measure of dispersion of the first distribution fit;

a measure of a shape of the first distribution fit;

a number of muscle contractions in a predetermined portion of the first distribution fit; and

the number of muscle contractions in the first distribution fit.

7. The method of claim 6, wherein the measure of the fatigue of the at least one muscle during the first period of time is determined based on a difference or ratio between the first value of the distribution-fitting feature and the second value of the distribution-fitting feature.

8. A computer program product comprising a computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method of any of claims 1-7.

9. An apparatus for assessing muscle fatigue in at least one muscle of a subject, the apparatus comprising a processing unit configured to:

obtaining a first set of measurements of muscle contraction of the at least one muscle over a first time period;

forming a first frequency distribution based on values of a muscle contraction characteristic determined from the first set of measurements;

determining a first distribution fit of the first frequency distribution;

determining whether the first distribution fit is statistically stable;

determining a first value for a distribution fit feature from the first distribution fit if the first distribution fit is determined to be statistically stable;

comparing the first value to a second value for the distribution-fitted feature to determine a measure of the fatigue of the at least one muscle over the first time period, wherein the second value for the distribution-fitted feature relates to a second distribution fit of a second frequency distribution, wherein the second frequency distribution is formed based on values of the muscle contraction feature determined from a second set of measurements of muscle contraction of the at least one muscle over a second time period different from the first time period; and is

Outputting a signal representative of the determined measure of fatigue.

10. The apparatus of claim 9, wherein the measurement of muscle contraction is obtained by one or more of: a surface electromyography (sEMG) sensor, an electromyography (MMG) sensor, an accelerometer, a strain gauge sensor, a piezoelectric sensor, a stretch sensor, or a deformation sensor.

11. The apparatus of claim 9 or 10, wherein the processing unit is configured to form the first frequency distribution by:

processing the first set of measurements to determine values of the muscle contraction characteristic for muscle contraction over the first time period; and is

Forming the first frequency distribution from the determined values of the muscle contraction characteristic.

12. The apparatus of any one of claims 9-11, wherein the muscle contraction characteristic is any one of: the intensity of the muscle contraction, the duration of the muscle contraction, and the tremor in the muscle contraction.

13. The apparatus of any of claims 9-12, wherein the processing unit is configured to determine whether the first distribution fit is statistically stable by:

determining a measure of variability of the first distribution fit; and is

Comparing the determined measure of variability to a threshold;

wherein the first distribution fit is determined to be statistically stable if the determined measure of variability is below the threshold.

14. The apparatus of any one of claims 9-13, wherein the distribution fitting feature comprises any one of:

a maximum value of the muscle contraction characteristic;

a minimum value of the muscle contraction characteristic;

a scale of values fitted to the first distribution;

a measure of dispersion of the first distribution fit;

a measure of a shape of the first distribution fit;

a number of muscle contractions in a predetermined portion of the first distribution fit; and

the number of muscle contractions in the first distribution fit.

15. A system, comprising:

a device to be carried or worn by a subject, the device comprising a muscle contraction sensor for measuring the contraction of one or more muscles of the subject; and

the apparatus of any one of claims 9-14.

Technical Field

The present invention relates to the assessment of muscle fatigue in at least one muscle of a subject, and in particular to a computer implemented method, apparatus and computer program product for assessing muscle fatigue.

Background

Muscle fatigue and muscle fatigue are useful information in several fields, such as sports, injury or post-operative rehabilitation, patient care and deterioration, independent living (especially elderly), and in the context of occupational physiology.

Conventionally, muscle fatigue can be inferred by electrical stimulation of muscles under controlled conditions and using dedicated instruments in the laboratory using surface electromyography (sEMG) and fatigue protocols (repeated muscle contractions).

The article "Detection of muscle facial using muscle based control device" (8 th international information technology conference (ICAT), ann, 2017, pages 44-49) by t.m. neurohan, m.piechnick, j.falkenberg and t.nazmy describes fatigue assessment outside the laboratory using sEMG implemented in wearable devices.

However, conventional devices are limited because some background information about muscle contraction is required. For example, the contraction is responsive to light, medium or heavy load. Whether the contraction is static or dynamic. Whether the contraction is passive (eccentric) or active (concentric). Furthermore, sEMG-derived features (e.g., Root Mean Square (RMS)) and integrated electromyography (ieg) may provide misleading information because they are not robust to motion artifacts and signal noise, and they need to be normalized.

Therefore, there is a need for improved assessment of muscle fatigue.

Disclosure of Invention

In particular, there is a need for an improved assessment of muscle fatigue, which enables assessment of muscle fatigue over time outside a laboratory or clinical setting (e.g. during activities of daily living ADL).

Additionally or alternatively, there is also a need for an improved assessment of muscle fatigue, which enables the use of sensors for assessing muscle fatigue in addition to sEMG and/or features that have to be derived from sEMG measurements.

Additionally or alternatively, there is also a need for an improved assessment of muscle fatigue that is capable of assessing muscle fatigue without requiring calibration of the subject and/or without requiring background information about any observed muscle contractions.

Thus, according to a first particular aspect, a computer-implemented method for assessing muscle fatigue in at least one muscle of a subject is provided. The method comprises the following steps: (i) obtaining a first set of measurements of muscle contraction of the at least one muscle over a first time period; (ii) forming a first frequency distribution based on values of a muscle contraction characteristic determined from the first set of measurements; (iii) determining a first distribution fit of the first frequency distribution; (iv) determining whether the first distribution fit is statistically stable; (v) determining a first value for a distribution fit feature from the first distribution fit if the first distribution fit is determined to be statistically stable; (vi) comparing the first value to a second value for the distribution-fitted feature to determine a measure of the fatigue of the at least one muscle over the first time period, wherein the second value for the distribution-fitted feature relates to a second distribution fit of a second frequency distribution, wherein the second frequency distribution is formed based on values of the muscle contraction feature determined from a second set of measurements of muscle contraction of the at least one muscle over a second time period different from the first time period; and (vii) outputting a signal representative of the determined measure of fatigue. Thus, the first aspect provides a means to assess muscle fatigue using any sensor capable of measuring muscle contraction outside of a laboratory or clinical setting without requiring subject calibration and without requiring background information about any observed muscle contraction.

In some embodiments, the measurement of muscle contraction is obtained by one or more of: a surface electromyography (sEMG) sensor, an electromyography (MMG) sensor, an accelerometer, a strain gauge sensor, a piezoelectric sensor, a stretch sensor, or a deformation sensor.

In some embodiments, the first set of measurements includes measurements of at least a minimum number of muscle contractions of the at least one muscle.

In some embodiments, the method further comprises: determining the minimum number of required muscle contractions based on a number of muscle contractions of the at least one muscle during the second time period.

In some embodiments, the first frequency distribution is formed by: processing the first set of measurements to determine values of the muscle contraction characteristic for muscle contraction over the first time period; and forming the first frequency distribution from the determined values of the muscle contraction characteristic. The muscle contraction characteristic can be any one of: the intensity of the muscle contraction, the duration of the muscle contraction, and the tremor in the muscle contraction.

In some embodiments, step (iv) comprises: determining a measure of variability of the first distribution fit; and comparing the determined measure of variability to a threshold. The first distribution fit can be determined to be statistically stable if the determined measure of variability is below the threshold.

In some embodiments, the distribution fitting feature comprises any one of: a maximum value of the muscle contraction characteristic; a minimum value of the muscle contraction characteristic; a scale of values fitted to the first distribution; a measure of dispersion of the first distribution fit; a measure of a shape of the first distribution fit; a number of muscle contractions in a predetermined portion of the first distribution fit; and the number of muscle contractions in the first distribution fit. In these embodiments, the measure of the fatigue of the at least one muscle during the first period of time can be determined based on a difference or ratio between the first value of the distribution-fitting feature and the second value of the distribution-fitting feature. In an alternative embodiment, the measure of the fatigue of the at least one muscle during the first period of time can be determined based on a difference or ratio between a maximum of the muscle contraction characteristic fitted for the first distribution and a maximum of the muscle contraction characteristic fitted for the second distribution.

In some embodiments, the method further comprises: obtaining a further measurement of muscle contraction of the at least one muscle if the first distribution fit is not determined to be statistically stable; updating the first frequency distribution to include the further measurement; repeating steps (iii) and (iv) for the updated first frequency distribution.

According to a second aspect, there is provided a computer program product comprising a computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform a method according to the first aspect or any embodiment thereof.

According to a third particular aspect, an apparatus for assessing muscle fatigue in at least one muscle of a subject is provided. The apparatus comprises a processing unit configured to: obtaining a first set of measurements of muscle contraction of the at least one muscle over a first time period; forming a first frequency distribution based on values of a muscle contraction characteristic determined from the first set of measurements; determining a first distribution fit of the first frequency distribution; determining whether the first distribution fit is statistically stable; determining a first value for a distribution fit feature from the first distribution fit if the first distribution fit is determined to be statistically stable; comparing the first value to a second value for the distribution-fitted feature to determine a measure of the fatigue of the at least one muscle over the first time period, wherein the second value for the distribution-fitted feature relates to a second distribution fit of a second frequency distribution, wherein the second frequency distribution is formed based on values of the muscle contraction feature determined from a second set of measurements of muscle contraction of the at least one muscle over a second time period different from the first time period; and outputting a signal representative of the determined measure of fatigue. Thus, the third aspect provides a means to assess muscle fatigue using any sensor capable of measuring muscle contraction outside of a laboratory or clinical setting without requiring subject calibration and without requiring background information about any observed muscle contraction.

In some embodiments, the measurement of muscle contraction is obtained by one or more of: a surface electromyography (sEMG) sensor, an electromyography (MMG) sensor, an accelerometer, a strain gauge sensor, a piezoelectric sensor, a stretch sensor, or a deformation sensor.

In some embodiments, the first set of measurements includes measurements of at least a minimum number of muscle contractions of the at least one muscle.

In some embodiments, the processing unit is further configured to: determining the minimum number of required muscle contractions based on a number of muscle contractions of the at least one muscle during the second time period.

In some embodiments, the processing unit is configured to form the first frequency distribution by: processing the first set of measurements to determine values of the muscle contraction characteristic for muscle contraction over the first time period; and forming the first frequency distribution from the determined values of the muscle contraction characteristic. The muscle contraction characteristic can be any one of: the intensity of the muscle contraction, the duration of the muscle contraction, and the tremor in the muscle contraction.

In some embodiments, the processing unit is configured to determine whether the first distribution fit is statistically stable by: determining a measure of variability of the first distribution fit; and comparing the determined measure of variability to a threshold. The first distribution fit can be determined to be statistically stable if the determined measure of variability is below the threshold.

In some embodiments, the distribution fitting feature comprises any one of: a maximum value of the muscle contraction characteristic; a minimum value of the muscle contraction characteristic; a scale of values fitted to the first distribution; a measure of dispersion of the first distribution fit; a measure of a shape of the first distribution fit; a number of muscle contractions in a predetermined portion of the first distribution fit; and the number of muscle contractions in the first distribution fit. In these embodiments, the measure of the fatigue of the at least one muscle during the first period of time can be determined based on a difference or ratio between the first value of the distribution-fitting feature and the second value of the distribution-fitting feature. In an alternative embodiment, the measure of the fatigue of the at least one muscle during the first period of time can be determined based on a difference or ratio between a maximum of the muscle contraction characteristic fitted for the first distribution and a maximum of the muscle contraction characteristic fitted for the second distribution.

In some embodiments, the processing unit is further configured to: obtaining a further measurement of muscle contraction of the at least one muscle if the first distribution fit is not determined to be statistically stable; updating the first frequency distribution to include the further measurement; determining a first distribution fit of the updated first frequency distribution; and determining whether the updated first distribution fit is statistically stable.

According to a fourth aspect, there is provided a system comprising: a device to be carried or worn by a subject, the device comprising a muscle contraction sensor for measuring the contraction of one or more muscles of the subject; and an apparatus according to the third aspect or any embodiment thereof.

In some embodiments, the apparatus is part of the device. In an alternative embodiment, the apparatus is separate from the device.

These and other aspects will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

Drawings

Exemplary embodiments will now be described, by way of example, with reference to the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating an apparatus according to an example embodiment;

FIG. 2 is a flow chart illustrating a method according to an exemplary embodiment;

FIG. 3 is a graphical representation of an exemplary frequency distribution formed from a set of muscle contraction measurements;

FIG. 4 is a graphical illustration of an exemplary distribution fit of the frequency distribution shown in FIG. 3;

FIG. 5 is a graphical representation of the frequency distribution of FIG. 4 and another exemplary frequency distribution and corresponding distribution fit; and is

Fig. 6 shows the frequency distribution and distribution fit of fig. 4 with corresponding values for the distribution fit feature.

Detailed Description

According to the techniques described herein, improvements in the assessment of muscle fatigue are provided that enable any of the following: enabling assessment of muscle fatigue over time outside of a laboratory or clinical setting, enabling assessment of muscle fatigue using sensors in addition to surface electromyography (sEMG) and/or features that have to be derived from sEMG measurements, enabling assessment of muscle fatigue without requiring subject calibration and/or without requiring background information about any observed muscle contractions. The subject to which the techniques described herein are applied can be a human or an animal. In some embodiments, the muscle(s) for which fatigue is determined can be any muscle in the forearm (e.g., the flexor hallucis longus, the flexor digitorum profundus, the extensor carpi radialis longus and extensor carpi radialis brevis, the extensor digitorum longus, the extensor carpi ulnaris) or in the leg or back.

Fig. 1 illustrates a system 2 according to an exemplary embodiment of the teachings presented herein. In this embodiment, the system 2 comprises a device 4 carried or worn by the subject, the device 4 comprising a muscle contraction sensor 6 for measuring the contraction of one or more muscles of the subject. The system 2 further comprises a device 10, the device 10 receiving the measurement of muscle contraction and analyzing the measurement to determine a measure of fatigue of the one or more muscles.

The device 4 can be in any form suitable for enabling a subject to carry or wear the device 4. For example, the device 4 may be a watch or a smart watch, a smartphone, a bracelet, a pendant, a necklace, a chest strap integrated in clothing, an arm strap, a leg strap, or the like. Typically, the device 4 will be carried or worn near or on a portion of the subject's body where the muscle or muscles to be evaluated are located (e.g., the device 4 may be worn on a leg to measure fatigue of the muscle or muscles of the leg). When the device 4 is a portable device, it enables measurements of muscle contraction to be obtained while the subject is performing daily activities (e.g. walking, climbing stairs, etc.).

In some embodiments, as shown in fig. 1, the apparatus 10 can be separate from the device 4. In these embodiments, apparatus 10 can be any type of electronic or computing device capable of communicating with device 4 or otherwise receiving muscle contraction measurements, either directly or indirectly. For example, the apparatus 10 can be a computer, laptop, tablet, smartphone, smartwatch, or the like, or a portion thereof, and thus may be an apparatus that is present or used in a subject's home or care environment. In other embodiments, the apparatus 10 can be a device that is remote from the subject, remote from the subject's home or care environment. For example, the device 10 can be a server, such as a server of a data center (also referred to as "in the cloud"). In an alternative embodiment, the apparatus 10 (and in particular the functionality of the apparatus 10 as described herein) can be integral with the device 4. Thus, the apparatus 10 can also be carried or worn by a subject as part of the device 4.

The muscle contraction sensor 6 can comprise any type of sensor(s) for measuring the contraction of one or more muscles of the subject or for providing measurements indicative of the muscle contraction of the subject. The muscle contraction sensor 6 generates and outputs a muscle contraction signal representing a measurement of the muscle contraction of the subject over time (e.g., over a first time period). The muscle contraction signal can include a time series of muscle contraction measurements (samples of the time series) covering the time period. The muscle contraction sensor 6 can use any desired sampling frequency, for example 50 measurements per second (50Hz), 60Hz or 100 Hz. The muscle contraction sensor 6 can be a sensor that measures an electrical component associated with muscle contraction (e.g., a sEMG sensor), a sensor that measures a mechanical component associated with muscle contraction (e.g., a electromyography (MMG) sensor), a sensor that measures an amount or level of movement associated with muscle contraction (e.g., an accelerometer), or a sensor that responds to a change in geometry due to muscle contraction (e.g., a strain gauge, a piezoelectric sensor, a sensor comprising conductive rubber, or any other type of stretch sensor or deformation sensor).

The apparatus 10 includes a processing unit 12, the processing unit 12 controlling the operation of the apparatus 10 and being capable of being configured to execute or carry out the methods described herein. In particular, the processing unit 12 is able to obtain muscle contraction signals/measurements and process them to assess the fatigue of the muscle(s). The processing unit 12 can be implemented in a variety of ways using software and/or hardware to perform the various functions described herein. Processing unit 12 may include one or more microprocessors or Digital Signal Processors (DSPs) that can be programmed using software or computer program code to perform the required functions and/or to control the components of processing unit 12 to practice the required functions. Processing unit 12 may be implemented as a combination of dedicated hardware for performing some functions, such as amplifiers, preamplifiers, analog-to-digital converters (ADCs), and/or digital-to-analog converters (DACs), and a processor (e.g., one or more programmed microprocessors, controllers, DSPs, and associated circuitry) for performing other functions. Examples of components that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, DSPs, Application Specific Integrated Circuits (ASICs), and Field Programmable Gate Arrays (FPGAs).

The processing unit 12 is connected to a memory unit 14, the memory unit 14 being capable of storing data, information and/or signals (including muscle contraction measurements) for use by the processing unit 12 in controlling the operation and/or running of the apparatus 10 or performing the methods described herein. In some embodiments, memory unit 14 stores computer readable code that is executable by processing unit 12 to cause processing unit 12 to perform one or more functions (including the methods described herein). In a particular embodiment, the program code can be in the form of an application for a smart watch, a smartphone, a tablet, a laptop, or a computer. The memory unit 14 can include any type of non-transitory machine-readable medium, such as cache memory or system memory (including volatile and non-volatile computer memory, e.g., Random Access Memory (RAM) implemented in the form of memory chips, static RAM (sram), dynamic RAM (dram), Read Only Memory (ROM), programmable ROM (prom), erasable prom (eprom) and electrically erasable prom (eeprom)), an optical disk (e.g., a Compact Disk (CD), a Digital Versatile Disk (DVD), or a blu-ray disk), a hard disk, a tape storage solution, or a solid state device (including memory sticks, Solid State Drives (SSDs), memory cards), and so forth.

In the embodiment of the system 2 shown in fig. 1, when the apparatus 10 is separate from the device 4 comprising the muscle contraction sensor 6, the apparatus 10 further comprises an interface circuit 16 for enabling data connection and/or data exchange with other devices, including the device 4, and optionally further comprises any one or more of a server, a database, a user device and a sensor. The connection may be direct or indirect (e.g., via the internet), so the interface circuit 16 is capable of effecting a connection between the apparatus 10 and a network (e.g., the internet) or between the apparatus 10 and the device 4 via any desirable wired or wireless communication protocol. For example, the interface circuit 16 can operate using WiFi, bluetooth, Zigbee, or any cellular communication protocol including, but not limited to, global system for mobile communications (GSM), Universal Mobile Telecommunications System (UMTS), Long Term Evolution (LTE), LTE advanced, etc. In the case of a wireless connection, the interface circuitry 16 (and thus the apparatus 10) may include one or more suitable antennas for transmission/reception in a transmission medium (e.g., air). Alternatively, in the case of a wireless connection, the interface circuit 16 may include means (e.g., a connector or plug) to enable the interface circuit 16 to be connected to one or more suitable antennas external to the apparatus 10 for transmission/reception in a transmission medium (e.g., air). The interface circuit 16 is connected to the processing unit 12 to enable information or data received by the interface circuit 16 to be provided to the processing unit 12 and/or to enable information or data from the processing unit 12 to be transmitted by the interface circuit 16.

The interface circuit 16 can be used to receive muscle contraction measurements generated by the muscle contraction sensor 6.

In some embodiments, interface circuit 16 can be used to output results (e.g., an indication of muscle fatigue) processed by processing unit 12 and/or information related to the determined muscle fatigue.

In some embodiments, the apparatus 10 includes a user interface 18, the user interface 18 including one or more components that enable a user of the apparatus 10 (e.g., a subject or a care provider for the subject) to input information, data, and/or commands into the apparatus 10 (e.g., to initiate or enable analysis of muscle contraction measurements according to the techniques described herein) and/or to enable the apparatus 10 to output information or data to the user of the apparatus 10. For example, the output may be an audible, visual, and/or tactile indication of the level of muscle fatigue. User interface 18 can include any suitable input component(s) including, but not limited to, a keyboard, keys, one or more buttons, switches or dials, a mouse, a touch pad, a touch screen, a stylus, a camera, a microphone, etc., and user interface 18 can include any suitable output component(s) including, but not limited to, a display screen, one or more lights or light elements, one or more speakers, a vibrating element, etc.

It will be appreciated that a practical embodiment of the apparatus 10 may include additional components beyond those shown in fig. 1. For example, the apparatus 10 may also include a power source (e.g., a battery) or components for enabling the apparatus 10 to be connected to a mains power supply.

As mentioned above, the muscle contraction sensor 6 is part of the apparatus 4, in the embodiment shown in fig. 1 the apparatus 4 is separate from the device 10. To communicate the muscle contraction measurements from the device 4 to the apparatus 10, the device 4 comprises an interface circuit 20. Interface circuit 20 may be implemented in a similar manner as interface circuit 16 in device 10.

In some embodiments, the device 4 can also include a processing unit 22 for controlling the operation of the device 4. The processing unit 22 can also be used to perform some pre-processing of the muscle contraction measurements before they are transmitted to the apparatus 10, for example the measurements can be filtered to reduce or remove noise components or artefacts. The processing unit 22 may be implemented in a similar manner as the processing unit 12 in the apparatus 10.

It will be appreciated that a practical embodiment of the device 4 may comprise additional components than those shown in fig. 1. For example, the device 4 may also comprise a power supply (preferably a battery, so that the device 4 is a portable device) or means for enabling the device 4 to be connected to a mains power supply.

In an alternative embodiment of the system 2, in which the apparatus 10 is part of the device 4, it will be appreciated that there may be only one processing unit 12/22 and that the interface circuitry is not required to communicate the muscle contraction measurements to the processing unit 12.

The flowchart in fig. 2 illustrates an exemplary method in accordance with the techniques described herein. The processing unit 12 in the apparatus 10 is capable of performing one or more of the steps of the method, in combination with any of the memory unit 14, the interface circuit 16 and the user interface 18, where appropriate. The processing unit 12 may perform one or more steps in response to executing computer program code, which can be stored on a computer readable medium (e.g., the memory unit 14). The flow chart in fig. 2 is described with reference to fig. 3-6.

In a first step (step 101), the processing unit 12 obtains a first set of measurements of muscle contractions of at least one muscle of the subject during at least a first period of time. In some embodiments, the at least one muscle is any muscle in the forearm (e.g., flexor hallucis longus, flexor digitorum profundus, extensor carpi radialis longus and extensor carpi radialis brevis, extensor digitorum longus, extensor carpi ulnaris) or in the leg or back. The measurement of muscle contraction is obtained from the output of muscle contraction signals by muscle contraction sensors 6 in the device 4. The device 4 is carried or worn by the subject at least during the first time period. The processing unit 12 can obtain the muscle contraction signal directly from the muscle contraction sensor 6 or indirectly (e.g., via the interface circuit 16 and the interface circuit 20) from the muscle contraction sensor 6. In these embodiments, when the processing unit 12 receives the measurements, the processing unit 12 can process the measurements (e.g., in real-time or near real-time) to assess muscle fatigue. Alternatively, the processing unit 12 can retrieve the muscle contraction signal from the memory unit 14. In some embodiments, the processing unit 12 is capable of receiving a muscle contraction signal indicative of a muscle contraction of the at least one muscle during a first time period after the first time period has elapsed. Alternatively, the processing unit 12 can receive the muscle contraction signal during the measurement of the first period of time of the muscle contraction.

In some embodiments, the muscle contraction measurements include measurements of at least a minimum number of muscle contractions (e.g., at least 10 contractions). Thus, the first time period preferably has a duration that is long enough to cover at least a minimum number of muscle contractions. The duration of the first time period can be, for example, 1 minute (e.g., if the subject is engaged in strenuous physical activity), at least 10 minutes, 1 hour, 4 hours, one morning, one afternoon, one night, one entire day, etc. In some embodiments, the processing unit 12 can continue to obtain a set of measurements of muscle contraction via the muscle contraction sensors 6 until at least a minimum number of muscle contractions have been measured.

In some embodiments, the minimum number of required muscle contractions may be determined on a subject-by-subject basis. The minimum number of muscle contractions may be determined based on a typical or average number of muscle contractions of the subject over each time period (e.g., hourly, daily, etc.).

In step 103, the processing unit 12 forms a frequency distribution (referred to herein as a "first frequency distribution") based on the values of the muscle contraction characteristics determined from the first set of measurements. As is known, a frequency distribution is a function, e.g., a histogram or a plot, that indicates the frequency (i.e., the number of times) at which a value or range of values of a variable appears in a data set. In the present case, the frequency distribution indicates the number of times a value or range of values of a muscle contraction characteristic appears in the first set of measurements.

Thus, step 103 can include processing the first set of measurement data to determine a value of a muscle contraction characteristic for each muscle contraction occurring in the first time period and identifiable in the muscle contraction signal. A first frequency distribution is then formed from these values.

In some embodiments, the muscle contraction characteristic is any one of: the strength of the muscle contraction, the duration of the muscle contraction, and the tremor in the muscle contraction. In other embodiments, the muscle contraction characteristic is a muscle contraction plateau, a rising slope, or a decay. In a preferred embodiment, the muscle contraction characteristic is the strength of the muscle contraction, and this can be determined from the power of the muscle contraction signal (e.g., Root Mean Square (RMS)). In the case where the muscle contraction signal is obtained by a sEMB sensor, the strength of the muscle contraction can be determined by calculating the Root Mean Square (RMS) of the part of the sEMB signal (or part of the first set of measurements) that is related to the muscle contraction. In case the muscle contraction signal is obtained by an accelerometer, the strength of the muscle contraction can be determined by calculating the Root Mean Square (RMS) of the part of the acceleration signal related to the muscle contraction (or the part of the first set of measurements). The duration of the muscle contraction can be determined by identifying the beginning and end of the muscle contraction in the muscle contraction signal. As an example, the start and end of a muscle contraction can be identified from the RMS of the muscle contraction signal, and in particular where the RMS rises above (starts) and falls below (ends) a threshold. The threshold can be, for example, twice the standard deviation of the RMS of the muscle contraction signal. Muscle tremors can be identified from muscle contraction signals using frequency domain analysis (e.g., fast fourier transform, FFT), where the tremors correspond to the (higher) frequency components of the signal. A steady state of muscle contraction occurs when muscle contraction is maintained within a certain coefficient of variation. The muscle contraction stability state can be assessed in the muscle contraction signal using a moving mean and a moving standard deviation function. The rising slope (e.g. in case of an EMG signal) is the part of the signal where the EMG signal goes from the resting value to the contraction value of the muscle. The rising slope can be identified by analyzing the first derivative of the EMG signal. The attenuation is the peak of the muscle contraction to the opposite side of the rising slope and can also be identified by analyzing the first derivative of the EMG signal.

Step 103 can be performed once a minimum number of muscle contractions has been obtained, or step 103 can be performed when muscle contractions occur and a frequency distribution is gradually established over time from the measurements.

Fig. 3 is a graphical representation of an exemplary frequency distribution 50 in the form of a histogram formed from a first set of measurements of muscle contraction. In particular, the histogram 50 in fig. 3 is a frequency distribution of 13 values for the intensity of muscle contraction occurring in the first time period.

Next, in step 105, the processing unit 12 determines a distribution fit of the first frequency distribution. This distribution fit is referred to herein as a "first distribution fit". The first distribution fit is a probability density function fitted to the data in the first frequency distribution.

Fig. 4 is a graphical representation of an exemplary distribution fit 52 for the histogram 50 shown in fig. 3.

Next, in step 107, the processing unit 12 determines whether the first distribution fit is statistically stable. That is, in step 107, the processing unit 12 determines whether the first frequency distribution is formed to be "coherent" or "stable" according to sufficient data for the first frequency distribution (i.e. sufficient values of the muscle contraction characteristic), so adding further data to the frequency distribution does not substantially change the shape of the first frequency distribution or the first distribution fit.

In some embodiments of step 107, processing unit 12 determines a measure of variability of the first distribution fit. The measure of variability is then compared to a threshold to determine whether the first distribution fit is statistically stable. In some embodiments, the first distribution fit is determined to be statistically stable if the measure of variability is below a threshold, and the first distribution fit is determined to be statistically unstable if the measure of variability is above the threshold. The measure of variability can be a variability of the first frequency distribution, a variance of the first frequency distribution, a coefficient of variation of the first frequency distribution, or a standard deviation of the first frequency distribution. Those skilled in the art will appreciate the various types of variability means that can be used to determine whether a distribution fit is statistically stable.

The histogram 50 in fig. 4 comprises 13 values of the intensity of the muscle contraction that occurred in the first time period, and this is sufficient to consider the first distribution fit 52 in fig. 4 to be stable.

If it is determined in step 107 that the first distribution fit is not statistically stable, a value for a muscle contraction characteristic of the further muscle contraction is obtained (e.g. by measuring the further muscle contraction or by evaluating the further muscle contraction in already obtained measurements) and added to the first frequency distribution. That is, a further measurement of a muscle contraction of the at least one muscle is obtained (e.g. a measurement of a further muscle contraction in the first time period, a measurement of a muscle contraction in a time period immediately after the first time period, a measurement of a muscle contraction that continues to occur after the end of the first time period), and the first frequency distribution is updated to include values of a further muscle contraction characteristic determined from the further measurement. An updated distribution fit for the updated first frequency distribution is then determined according to step 105, and the stability of the updated distribution fit is then checked according to step 107. If stable, the updated distribution fit is analyzed according to step 109 below. If the updated distribution fit is still not statistically stable, the process iterates until stability is achieved.

For ease of illustration, while the distribution fit in fig. 4 is itself statistically stable, fig. 5 illustrates the effect of updating the histogram with additional values of the muscle contraction characteristic and the resulting effect of the distribution fit. Thus, fig. 5 shows an exemplary updated histogram 60, which exemplary updated histogram 60 is statistically stable and includes 13 values of the intensity of muscle contractions occurring in the first time period (shown in the histogram 60 by the "unfilled" column) present in the histogram 50 and 8 additional values of the intensity of muscle contractions in the first time period (or subsequent time periods) (as shown by the "shaded" portions in the columns in the histogram 60). Fig. 5 also shows the distribution fit 52 from fig. 4 for the 13 raw values (as described above, this fit 52 is statistically stable), and fig. 5 also shows the distribution fit 62 for the histogram 60 (i.e., the distribution fit for the complete set of 21 intensity values). It can be seen that while both distribution fits 52, 62 are statistically stable, the distribution fits 52, 62 differ primarily on the right side of the histogram, where the histogram 60 shows a bimodal distribution.

If the first distribution fit (whether updated or otherwise) is determined to be statistically stable, the first distribution fit can be used as a "template" of muscle contraction during the first time period for comparison with distribution fits formed from muscle contraction measurements obtained during different time periods. Such a comparison can provide an indication of muscle fatigue of the at least one muscle in the first time period compared to muscle fatigue of the at least one muscle in other time periods.

Thus, in step 109, the processing unit 12 determines a value for the distribution fitting feature from the statistically stable first distribution fitting. The value for the distribution-fitted feature determined from the statistically stable first distribution fit is referred to herein as the "first value". In some embodiments, the distribution fitting feature is based on a muscle contraction feature determined from the first set of measurements and used to form the first frequency distribution. In other embodiments, the distribution fitting feature is based on a different type of muscle contraction feature than the muscle contraction feature determined from the first set of measurements and used to form the first frequency distribution. In other embodiments, the profile-fitting feature is independent of any type of muscle contraction feature, but rather is dependent on the nature of the first profile fitting.

In some embodiments, the distribution fitting feature is any one of: maximum value of muscle contraction characteristic (e.g., maximum intensity); a minimum value (e.g., minimum intensity) of a muscle contraction characteristic; a scale of values of the distribution fit; a measure of dispersion of the distribution fit; a measure of the shape of the distribution fit; the number of muscle contractions in the predetermined portion of the distribution fit; and the number of muscle contractions in the distribution fit. The measure of dispersion of the distribution fit can be any of: a measure of standard deviation of the distribution fit, a quartile range (IQR), a range of the distribution fit, a mean absolute difference of the distribution fit, a median absolute difference of the distribution fit, a mean absolute deviation of the distribution fit, a mean deviation of the distribution fit, or a distance standard deviation. In a preferred embodiment, the distribution fit feature is a scale of values on the first distribution fit, e.g., 0% corresponds to the value of the muscle contraction feature at the lowest end (i.e., leftmost) of the first distribution fit, 100% corresponds to the value of the muscle contraction feature at the highest end (i.e., rightmost) of the first distribution fit, and the remainder of the scale (e.g., 50%, 80%, etc.) is positioned between these scales. A scale 54 of the values of the muscle contraction characteristic for the first distribution fit 52 is shown in fig. 4.

In step 111, the processing unit 12 compares the first value of the distribution fitting feature with another value for the distribution fitting feature obtained from the measurements of the muscle contraction in another time period to determine a measure of fatigue of the at least one muscle. The "further value" for the distribution-fitted characteristic is referred to herein as a "second value" for the distribution-fitted characteristic, and the second value is determined from a second set of muscle contraction measurements relating to a different (e.g. earlier) time period than the first time period, as set forth in step 101-109. In some embodiments, the earlier time period can be immediately preceding the previous time period (e.g., the previous hour, the morning (when the first time period is afternoon)), or a corresponding time period of a different day (e.g., the first time period can be the morning and the earlier time period can be the morning of a different day). In some embodiments, the different time periods can be time periods that overlap with portions of the first time period (i.e., some values of the muscle contraction characteristic can appear in both distributions/distribution fits). For example, the different time period can be an earlier part of the first time period (e.g., the distribution fit characteristic can be determined in step 111 from the distribution fit 62 in fig. 5 and compared to the distribution fit characteristic determined from the distribution fit 52 corresponding to the measurements used to form the histogram 50 in fig. 4). The measure of muscle fatigue can be given by the difference between the first value and the second value. Additionally or alternatively, the measure of fatigue can be given by a ratio of the first value to the second value. In either case, the difference or ratio is indicative of muscle fatigue in the first time period relative to muscle fatigue in an earlier time period.

In some embodiments, step 111 can include comparing a scale of values for muscle contraction in a first time period to a scale of values for muscle contraction in an earlier time period. For example, a muscle contraction characteristic value (e.g., maximum muscle contraction strength) corresponding to a 100% value for a first time period can be compared to a muscle contraction characteristic value (e.g., maximum muscle contraction strength) corresponding to a 100% value for an earlier time period. If the 100% value for the first time period is higher than the 100% value for the earlier time period, this can indicate that the degree of fatigue of the at least one muscle in the first time period is less than in the earlier time period, and vice versa. Fig. 6 shows two statistically stable frequency distributions 50 and 60 from earlier figures, and shows a scale 54 (also shown in fig. 4) for distribution fit 52, and also shows a scale 64 for distribution fit 62. It can be seen in fig. 6 that while the distribution fits 52, 62 are similar at the lower end (left side) of the distribution fit, the distribution fit 62 is wider than the distribution fit 52. This means that at the lower end of the distribution fits 52, 62, the scale 54 is similar to the scale 64, but at higher values of the muscle contraction characteristic (right side of the histogram 60), there is a more significant difference between the distribution fits 52, 62. This means that the 100% value in scale 64 is higher than the 100% value in scale 54, indicating that the object has less muscle fatigue on the muscle contraction covered by histogram 60.

In some embodiments, comparing can include comparing the number of contractions that fall within a particular intensity range in a first time period to the number of contractions that fall within the particular intensity range in another time period. Other embodiments of comparison can be based on the absolute number of contractions. Other embodiments of comparison can be based on the shape of the first distribution fit relative to the shapes of the distribution fits for other time periods.

In some embodiments, step 111 can further include comparing the first frequency distribution (or first distribution fit) to a frequency distribution (or distribution fit) for another time period to determine a further measure of muscle fatigue. This comparison can use a stepwise decreasing t student test to analyze the difference between the distributions or fits, or use fitting techniques such as the moment method, maximum distance estimation, the L moment method, or the maximum likelihood method.

In some embodiments of step 111, the processing unit 12 can compare the first value of the distribution fit feature with values for the distribution fit feature obtained from measurements of muscle contraction in a plurality of other time periods to determine a measure of fatigue of the at least one muscle during the first time period. This comparison enables the trend of muscle fatigue over time to be observed or derived. For example, a muscle fatigue metric can be determined hourly or daily.

Finally, in step 113, the processing unit 12 outputs a signal representing the determined measure of fatigue. A signal representative of the determined measure of fatigue can be output to the user interface 18 for presentation to a subject or user (e.g., a caregiver) and/or another device or apparatus via the interface circuit 16.

Accordingly, the present disclosure provides improved techniques for assessing muscle fatigue. In particular, the technology enables assessment of muscle fatigue over time outside of a laboratory or clinical setting and especially during daily living activities of a subject. In addition, sensors other than sEMG can be used to assess muscle fatigue, but sEMG can still be used if desired. Furthermore, the measure of muscle fatigue provided by this technique is a relative measure of muscle fatigue (i.e., a measure relative to an earlier time period), and therefore does not require subject calibration, nor background information about any observed muscle contractions.

Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the principles and techniques disclosed herein, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. Although some measures are recited in mutually different dependent claims, this does not indicate that a combination of these measures cannot be used to advantage. A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the internet or other wired or wireless telecommunication systems. Any reference signs in the claims shall not be construed as limiting the scope.