A Wearable Electromyography Method for Online Evaluation of Muscle Movement Fatigue

Abstract

The present invention discloses a wearable electromyography method for online evaluation of muscle movement fatigue in the technical field of human bioelectric signal analysis and evaluation, comprising the following steps: performing ankle rehabilitation training on a subject with a lower limb rehabilitation robot, and making preparations for the subject's rehabilitation training and electromyographic signal acquisition; selecting and extracting characteristic parameters for evaluating muscle fatigue degree; collecting online electromyographic signals; this method is based on a portable electromyographic signal collector, and secondary development of an online acquisition and online muscle fatigue evaluation system is carried out. Using the Labview 2020 platform, real-time data acquisition is completed through the device communication protocol, and combined with the advantages of fast data processing and simple operation of the Matlab platform for hybrid programming, the functions of data waveform display, data storage, online processing, and online evaluation are satisfied, so that the muscle fatigue situation during the movement production process can be conveniently and quickly understood, and timely processing can be carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of human bioelectrical signal analysis and evaluation, and particularly relates to a wearable electromyography method for online evaluation of muscle movement fatigue degree. Background Art

[0002] Muscle fatigue is a common physiological phenomenon in people's lives. In itself, the scope of muscle fatigue is very wide. With the increase of production work intensity and time, muscle fatigue will occur. The clinical manifestations are slowed movement speed, reduced coordination and flexibility. Prolonged accumulation will lead to muscle injury, manifested as muscle pain during production activities, and even muscle atrophy. In order to avoid muscle injury caused by muscle fatigue, the degree of muscle fatigue should be effectively evaluated. Traditional muscle fatigue assessment is a method proposed by Borg to estimate exercise intensity and fatigue. The indicators used are psychology and physiology, and fatigue detection cannot be carried out in a timely and effective manner.

[0003] When muscles are fatigued, metabolites in muscle tissues increase, and at the same time, certain physiological changes also occur, such as the conduction speed of muscle fibers. Since muscle contraction is accompanied by the generation of electromyographic signals, electromyographic signals contain muscle activity and physiological state information. At the same time, surface electromyographic signals can be obtained through surface electrodes. This method is convenient, effective, and harmless to the human body. Therefore, in recent years, the evaluation of muscle fatigue degree by analyzing electromyographic signals has received the attention of many scholars. At present, when evaluating the fatigue degree of muscles through surface electromyographic signals, generally, after data processing of sEMG, characteristic parameters are extracted to find out the characteristics that can accurately express muscle fatigue.

[0004] Since most current researchers use wired devices to save the collected electromyographic signals and then analyze the muscle fatigue degree offline, this method is not timely. In addition, factors such as each person's physical fitness, nutritional status, gender, age, etc. have a significant impact on muscle fatigue. Therefore, a fatigue assessment method for individuals should be established.

[0005] Therefore, there is an urgent need to design a wearable electromyography method for online evaluation of muscle movement fatigue degree. Summary of the Invention

[0006] The purpose of the present invention is to provide a wearable electromyography method for online evaluation of muscle movement fatigue degree to solve the problems raised in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solution: A wearable electromyography method for online evaluation of muscle movement fatigue degree, including the following steps:

[0008] S1: Conduct ankle rehabilitation training on the subject for the lower limb rehabilitation robot, and prepare well for the subject's rehabilitation training and electromyographic signal acquisition;

[0009] S2: Selection and extraction of characteristic parameters for evaluating muscle fatigue degree;

[0010] S3: Acquisition of online surface electromyography (EMG) signals.

[0011] Further, in the above wearable EMG method for online evaluation of muscle movement fatigue, the specific steps of S1 are as follows:

[0012] Select a number of different healthy subjects. Before collection, first clean the collection site with alcohol. Two sensors are placed at the same tibialis anterior muscle and rectus femoris muscle sites of each subject. However, the tibialis rectus muscle has little influence during single ankle movement, so fatigue assessment is not performed on it to reduce the interference of external uncertain factors. When the subject is undergoing rehabilitation training, open the animation in the upper computer at the same time to eliminate the influence of subjective fatigue, and then conduct rehabilitation training and EMG signal collection on the subject.

[0013] Further, in the above wearable EMG method for online evaluation of muscle movement fatigue, the specific steps of S2 are as follows:

[0014] Select the waveform length (WL) from the time-domain features and the wavelet singular entropy (WSE) from the non-linear features respectively. After extracting the features of the collected surface EMG signals using the two characteristic parameters, perform feature addition, analyze the data change characteristics, determine the fatigue coefficient value corresponding to the individual when muscle fatigue occurs according to the data change law, and take this as the basis. When conducting rehabilitation training on the subject, a large number of sample data need to be collected. If the parameters of the EMG signals in a certain effective time period, after being extracted by WL and WSE, are lower than the fatigue coefficient determined by the individual in advance, it is judged as the fatigue state at this time, and the upper computer immediately reminds the rehabilitation training personnel to stop collection to ensure the reliability of the collected data and the safety of the patient himself.

[0015] Further, in the above wearable EMG method for online evaluation of muscle movement fatigue, the specific steps of S3 are as follows:

[0016] An upper computer system integrating EMG signal acquisition, data preprocessing, feature extraction, data storage, waveform display, and fatigue state reminder; during the rehabilitation process, the patient's healthy limb makes corresponding movements to actively control the lower limb rehabilitation robot, driving the ankle joint of the affected limb side of the subject to move up and down. Each time the ankle joint of the healthy limb moves, the tibialis anterior muscle contracts. At this time, the disposable electrode patch contacts the human skin, transmits the EMG signal to the portable EMG signal collector, and transmits the data to the multifunctional upper computer programmed by Labview2020 and Matlab through the Bluetooth receiver to realize the real-time storage, display, data processing, and online fatigue assessment of the EMG signal.

[0017] Furthermore, in the above wearable electromyography method for online evaluation of muscle movement fatigue, healthy subjects are selected with different ages, genders, heights, weights, etc. to verify the stability of the wearable method for online muscle fatigue assessment; when testing the muscle fatigue that may be caused by different production activities, the muscle tissues with the strongest correlation should be selected, and this selection method is more persuasive for the assessment of the human muscle fatigue state.

[0018] Furthermore, in the above wearable electromyography method for online evaluation of muscle movement fatigue, the characteristic parameters for evaluating the muscle fatigue degree use the time-domain waveform length and the non-linear wavelet singular entropy.

[0019] Furthermore, in the above wearable electromyography method for online evaluation of muscle movement fatigue, the lower limb ankle pedal of the lower limb rehabilitation robot has an angular displacement range between (-7° and 7°). The subject's foot sole is fitted to the rehabilitation robot's pedal, and the pedal moves up and down, driving the subject's foot sole to move up and down. This action is mainly for the rehabilitation training of the ankle joint; when the subject performs ankle joint rehabilitation training on the lower limb rehabilitation robot, the electromyography signal is transmitted from the portable electromyography signal collector to the computer, and after the extraction of the characteristic parameters and comparison with the individual tibialis anterior muscle fatigue coefficient, the muscle fatigue degree evaluation result is obtained.

[0020] Furthermore, in the above wearable electromyography method for online evaluation of muscle movement fatigue, the portable electromyography signal collector is connected to two buttons of a disposable silver / silver chloride electrode, and the other side of the disposable silver / silver chloride electrode is connected to the human skin through conductive paste.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] 1. The present invention conducts an online fatigue degree assessment on the muscle tissues with strong correlation caused by specific actions. During the fatigue assessment process, a portable collector is selected to collect electromyography signals, liberating the detection range of muscle fatigue actions and getting rid of the limitation of wired electromyography signal collection. At the same time, through the analysis of the extracted eigenvalues, the fatigue characteristic coefficient of the tibialis anterior muscle for an individual is determined. Using this coefficient, it is possible to realize real-time fatigue detection of relevant muscle tissues when the subject performs online rehabilitation training. Compared with the traditional technology where muscle fatigue assessment is mostly offline, this method is more timely.

[0023] 2. The present invention uses two software programs, Labview 2020 and Matlab, for hybrid programming, which can realize the real-time change of electromyography signals during exercise. When muscle fatigue occurs, a kind reminder can be given to the subject, meeting the requirements of fast and accurate fatigue detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0025] Figure 1 It is the schematic diagram of the online recognition principle of the present invention;

[0026] Figure 2 It is the schematic diagram of the online fatigue assessment result of the tibialis anterior muscle of the subject of the present invention;

[0027] Figure 3 It is the online training host computer interface diagram of the personal wireless myoelectric acquisition instrument of the present invention;

[0028] Figure 4 It is the display diagram during the rehabilitation training of the present invention; Detailed implementation manners

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0030] The present invention provides a technical solution: a wearable myoelectric method for online evaluating muscle movement fatigue degree, including the following steps:

[0031] S1: Carry out ankle rehabilitation training on the subject by means of a lower limb rehabilitation robot, and prepare well for the subject's rehabilitation training and myoelectric signal acquisition;

[0032] Select a number of different healthy subjects (from the perspective of practical application, eight healthy subjects of different ages, heights and genders are selected. The eight subjects include 6 males and 2 females, with ages ranging from 23 to 30 years old and heights ranging from 160 cm to 180 cm). Before collection, the collection site is cleaned with alcohol. Two sensors are placed at the same tibialis anterior muscle and rectus femoris muscle sites of each subject. However, the tibialis rectus muscle has little influence during single ankle movement, so fatigue assessment is not performed to reduce the interference of external uncertain factors. When the subject is undergoing rehabilitation training, the animation in the upper computer is turned on simultaneously to eliminate the influence of subjective fatigue, and then the subject is subjected to rehabilitation training and EMG signal collection. Healthy subjects are selected with different ages, genders, heights, weights, etc. to verify the stability of the wearable method for online muscle fatigue assessment. When testing the muscle fatigue that may be caused by different production activities, the muscle tissue with the strongest correlation should be selected. This selection method is more persuasive for the assessment of the human muscle fatigue state.)

[0033] S2: Selection and extraction of characteristic parameters for evaluating muscle fatigue degree;

[0034] Select the waveform length (WL) from the time domain features and the wavelet singular entropy (WSE) from the non-linear features respectively. After using the two characteristic parameters to extract the features of the collected surface EMG signals, perform feature addition, analyze the data change characteristics, determine the fatigue coefficient value corresponding to the individual when muscle fatigue occurs according to the data change law, and use this as a basis. When the subject is undergoing rehabilitation training, a large number of sample data need to be collected. If the parameters of the EMG signals in a certain effective period, after being extracted by the two features of WL and WSE, are lower than the fatigue coefficient determined by the individual in advance, it is judged as the fatigue state at this time, and the upper computer immediately reminds the rehabilitation training personnel to stop collection to ensure the reliability of the collected data and the safety of the patient. The characteristic parameters for evaluating muscle fatigue degree use the time domain waveform length and the non-linear wavelet singular entropy.)

[0035] S3: Collection of online EMG signals;

[0036] A host computer system integrating surface electromyogram (sEMG) signal acquisition, data preprocessing, feature extraction, data storage, waveform display, and fatigue status reminder; during the rehabilitation process, the patient makes corresponding movements with the healthy limb on one side to actively control the lower limb rehabilitation robot, driving the ankle joint of the limb on the affected side of the subject to move up and down. Each time the ankle joint of the healthy limb moves, the tibialis anterior muscle contracts. At this time, the disposable electrode patch contacts the human skin and transmits the sEMG signal to the portable sEMG signal collector. The data is transmitted to the multifunctional host computer programmed by Labview 2020 and Matlab through a Bluetooth receiver, realizing real-time storage, display, data processing, and online fatigue assessment of the sEMG signal. The foot pedal of the ankle joint of the lower limb of the lower limb rehabilitation robot has an angular displacement range between (-7° and 7°). The sole of the subject fits with the foot pedal of the rehabilitation robot, and the foot pedal moves up and down, driving the sole of the subject to move up and down. This action is mainly for ankle joint rehabilitation training; when the subject performs ankle joint rehabilitation training on the lower limb rehabilitation robot, the sEMG signal is transmitted from the portable sEMG signal collector to the computer. After feature parameter extraction and comparison with the personal tibialis anterior muscle fatigue coefficient, the muscle fatigue degree assessment result is obtained. The portable sEMG signal collector is connected to two buttons of the disposable silver / silver chloride electrode, and the other side of the disposable silver / silver chloride electrode is connected to the human skin through conductive paste.

[0037] The working principle is as follows:

[0038] For the verification of the online muscle fatigue assessment method for the lower limb tibialis anterior muscle, ankle joint single movement that can cause human muscle fatigue is selected, and the tibialis anterior muscle with the strongest correlation with this movement is analyzed as the fatigue assessment object. To reduce the influence of other factors during data acquisition, different subjects are selected to perform the same movement and the same muscle tissue is detected, and the skin surface to be collected is treated with alcohol for cleaning purposes. During the experiment, the subjects are explained the key points and precautions of the movement to ensure that the subjects receive the experiment in a normal state. In addition, 3D animation is displayed to eliminate the incorrect influence brought by the subjective fatigue of the subjects. The time domain waveform length and the non-linear wavelet singular entropy are selected as the fatigue coefficients for determining personal muscle fatigue. The patient first performs active training to control the movement of the rehabilitation robot. At this time, the foot pedal of the subject's right leg does not move, and the foot pedal of the left leg drives the patient's ankle joint to move up and down. As Figure 4 shown, A is the foot pedal of the right leg, B is the foot pedal as, C is the healthy lower limb, D is the injured lower limb. The healthy lower limb collects the sEMG signal to actively control the left leg of the rehabilitation robot and performs rehabilitation training on the left limb of the patient. The range of up and down floating of the left foot pedal B is between -7 and 7 degrees. At this time, the sEMG signal of the patient's injured limb D can be collected for online assessment of the corresponding muscle fatigue. As Figure 3As shown in the figure, it is the upper computer interface for online evaluation. Ⅰ is the waveform display area. In Ⅰ, 1 is the waveform display of the first channel, and 2 is the waveform display of the second channel. Ⅱ is the serial port matching and selection area for the portable myoelectric signal collector. Ⅲ is the sampling rate setting area. The sampling rate set by the present invention is 1024. Ⅳ is the serial port serial number. Each serial port corresponds to a serial number. The middle digital box is for setting the number of cycles. However, when the subject does not reach the set number of times, a fatigue phenomenon occurs, that is, the green light in area Ⅴ is highlighted for reminder. As Figure 1 As shown in the figure, it is the schematic diagram of online fatigue evaluation. First, use the portable myoelectric signal collector to obtain data through the upper computer, perform online data preprocessing and feature extraction, and finally extract and superimpose the two types of features to determine the fatigue coefficient for an individual. During online fatigue evaluation, compare the feature values obtained by processing the myoelectric signals of the subject and performing feature extraction with the determined fatigue coefficient to determine whether there is a condition in the muscle at this time. As Figure 2 As shown in the figure, it is the online fatigue evaluation result graph of the subject's tibialis anterior muscle. After a series of data processing and analysis, the inflection point shown by the marked points in the figure is obtained, that is, X = 58, Y = 1.238. It can be seen that the waveform drops rapidly after this, indicating that the subject has a fatigue phenomenon. The fatigue coefficient for an individual is determined to be 1.238. When performing online fatigue evaluation, use the fatigue coefficient as the reference standard.

[0039] The sEMG preprocessing uses two types of eigenvalue in the time domain and non - linear domain.

[0040] The basic formula of the time - domain eigenvalue is as follows:

[0041] Waveform length pair (WL):

[0042]

[0043] Among them, x (i) represents the motion voltage amplitude of the i - th point of a certain action. i = 1, 2, …, N is the time - sample sequence of the myoelectric signal with length N.

[0044] The basic formula of the non - linear eigenvalue is as follows:

[0045] According to the theory of singular values, the matrix can be decomposed as shown below:

[0046] W m×n =U m×l ∧ l×l V l×n

[0047] Among them: m = 2 S , n = N / 2 S , Λ = diag(λ1, λ2, …λ l ) is the singular - value diagonal matrix, and the singular values satisfy λ1≥λ2…≥λl , in order to quantitatively describe the frequency and component characteristics of a signal, the wavelet singular entropy data processing method combining wavelet transform, singular value decomposition, and information entropy is as follows:

[0048]

[0049] where Δp i is the i-th order incremental wavelet singular entropy, defined as:

[0050]

[0051] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0052] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art in the relevant technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An online wearable electromyography method for evaluating muscle movement fatigue, characterized in that, It includes the following steps: S1: Conduct ankle joint rehabilitation training on the subject using a lower limb rehabilitation robot, and prepare well for the subject's rehabilitation training and electromyogram signal acquisition; S2: Selection and extraction of characteristic parameters for evaluating muscle fatigue degree; The specific steps are as follows: Select the waveform length (WL) from the time domain features and the wavelet singular entropy (WSE) from the non-linear features respectively. After extracting the features of the collected surface electromyogram signals using the two characteristic parameters, perform feature addition, analyze the data change characteristics, and determine the fatigue coefficient value corresponding to the individual when muscle fatigue occurs according to the data change law, and take this as the basis; When conducting rehabilitation training on the subject, a large number of sample data need to be collected. If the parameters of the electromyogram signals in a certain effective time period, after being extracted by the two features of WL and WSE, are lower than the fatigue coefficient determined by the individual in advance, it is judged as the fatigue state at this time, and the upper computer immediately reminds the rehabilitation training personnel to stop collecting to ensure the reliability of the collected data and the safety of the patient himself; S3: Online acquisition of electromyogram signals, the specific steps are as follows: An upper computer system integrating electromyogram signal acquisition, data preprocessing, feature extraction, data storage, waveform display, and fatigue state reminder; During the rehabilitation process, the patient's healthy limb makes corresponding movements to actively control the lower limb rehabilitation robot, driving the ankle joint of the subject's affected limb side to move up and down. Every time the ankle joint of the healthy limb moves, the tibialis anterior muscle contracts. At this time, the disposable electrode patch contacts the human skin, transmits the electromyogram signal to the portable electromyogram signal acquisition instrument, and transmits the data to the multifunctional upper computer programmed by mixing Labview2020 and Matlab through the Bluetooth receiver to realize real-time storage, display, data processing, and online fatigue evaluation of the electromyogram signal.

2. The wearable electromyography method for online evaluation of muscle movement fatigue according to claim 1, wherein: The specific steps of S1 are as follows: Select several different healthy subjects. Before collection, first clean the collection site with alcohol. Two sensors are placed at the same tibialis anterior muscle and rectus femoris muscle parts of each subject. However, the tibialis rectus muscle has little influence during single ankle joint movement, so fatigue evaluation is not carried out to reduce the interference of external uncertain factors; When the subject is undergoing rehabilitation training, open the animation in the upper computer at the same time to eliminate the influence of subjective fatigue, and then conduct rehabilitation training and electromyogram signal collection on the subject.

3. The wearable electromyography method for online evaluation of muscle movement fatigue according to claim 2, wherein: The selected healthy subjects have different ages, genders, heights, weights, etc., to verify the stability of the wearable method for online muscle fatigue evaluation; When testing the muscle fatigue that may be caused by different production activities, the muscle tissue with the strongest correlation should be selected. This selection method is more persuasive for evaluating the muscle fatigue state of the human body.

4. The wearable electromyography method for online evaluating muscle movement fatigue according to claim 1, wherein: The characteristic parameters for evaluating the muscle fatigue degree are the time domain waveform length and the non-linear wavelet singular entropy.

5. The wearable electromyography method for online evaluating muscle movement fatigue according to claim 1, characterized in that: The foot pedal of the lower limb rehabilitation robot for the lower limb ankle joint has an angular displacement range between (-7° and 7°). The sole of the subject's foot is fitted to the foot pedal of the rehabilitation robot, and the foot pedal moves up and down, driving the sole of the subject's foot to move up and down. This movement is mainly for the rehabilitation training of the ankle joint. When the subject performs ankle joint rehabilitation training on the lower limb rehabilitation robot, the myoelectric signal is transmitted from the portable myoelectric signal acquisition instrument to the computer. After the extraction of characteristic parameters and comparison with the fatigue coefficient of the subject's tibialis anterior muscle, the evaluation result of muscle fatigue degree is obtained.

6. The wearable electromyography method for online evaluating muscle movement fatigue according to claim 1, characterized in that: The portable myoelectric signal acquisition instrument is connected to two buttons of a disposable silver / silver chloride electrode, and the other side of the disposable silver / silver chloride electrode is connected to the human skin through conductive adhesive.

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