WO2024113113A1 - Muscle group synergy analysis method and system - Google Patents

Muscle group synergy analysis method and system Download PDF

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WO2024113113A1
WO2024113113A1 PCT/CN2022/134779 CN2022134779W WO2024113113A1 WO 2024113113 A1 WO2024113113 A1 WO 2024113113A1 CN 2022134779 W CN2022134779 W CN 2022134779W WO 2024113113 A1 WO2024113113 A1 WO 2024113113A1
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synergy
muscle group
analysis
activation
vectors
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French (fr)
Chinese (zh)
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耿艳娟
陈子寅
龙昱丞
覃柳妮
窦铭扬
李光林
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中国科学院深圳先进技术研究院
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A61B5/397—Analysis of electromyograms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/10—Pre-processing; Data cleansing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques

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  • the present invention relates to the field of neurorehabilitation engineering and movement mechanism technology, and in particular to a muscle group coordination analysis method and system.
  • the main purpose of the present invention is to provide a time-space-frequency domain muscle group synergy analysis method based on high-density electromyography, which is applied to the study of muscle group synergy characteristics between movement patterns under different rehabilitation training modes. From the perspective of muscle collaborative work, the synergy characteristics between movement muscles are explored, aiming to provide a research method and scientific basis for motor function rehabilitation evaluation.
  • the present invention provides a muscle group synergy analysis method, comprising:
  • the envelope matrix is decomposed using non-negative matrix factorization algorithm to extract muscle group synergy;
  • the dimension of similarity evaluation includes at least one of Euclidean distance, cosine angle and similarity index.
  • Cluster all cooperative vectors in each motion mode into k classes define the sum of the distances between the sample and the center of the class to which it belongs as the loss function, select the centers of k classes, assign the samples one by one to the class with the closest center, and obtain a clustering result; then update the mean of the samples in each class as the new center of the class; repeat the above steps to minimize the loss function and reach convergence;
  • the classes with less than a predetermined number of collaborative vectors are eliminated, and the remaining cluster centers are representative collaborative vectors.
  • the representative collaborative vectors in the two motion modes are paired according to similarity.
  • the representative collaborative vectors with a similarity higher than a preset threshold are the common collaborative vectors between the two motion modes, and the others are unique collaborative vectors.
  • the convex hull algorithm is used to obtain the three-dimensional convex hulls of the three-dimensional arrays ⁇ A, t max , ⁇ corresponding to all muscle group synergies in the isometric and isotonic modes, and the degree of difference in the spatiotemporal characteristics of muscle group synergies in the two movement modes is determined by analyzing the overlap of the two three-dimensional convex hulls.
  • the effective activation time range of the synergy vectors of different actions is compared;
  • the maximum amplitude of the cooperative vector activation and the time point of its occurrence can be compared by the activation peak and its corresponding time point t max .
  • the convex hull algorithm is used to calculate the three-dimensional convex hulls of the three-dimensional arrays ⁇ S, D, ⁇ corresponding to all muscle group synergies in the isometric and isotonic modes, respectively.
  • the degree of difference in the frequency-space characteristics of muscle group synergies in the two movement modes is determined by analyzing the overlap of the two three-dimensional convex hulls.
  • the spectrum matrix corresponding to the synergy vector is divided into multiple segments according to the frequency
  • Spectral coherence is used to perform correlation analysis on the same frequency band corresponding to different cooperative vectors.
  • the present invention also provides a muscle group coordination analysis system, comprising
  • the electromyographic signal collector is used to collect the user's electromyographic signals in real time
  • a data storage device used for storing electromyographic signal data
  • the control terminal is used to execute the above analysis method.
  • the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions.
  • the program instructions When the program instructions are executed by a computer, the computer executes the above method.
  • the present invention also provides a computer-readable storage medium on which computer instructions are stored, and the computer instructions are executed by a processor to complete the above method
  • muscle group synergy is analyzed from two perspectives: time-space domain and frequency-space domain, involving synergy vector similarity evaluation, common synergy and unique synergy, time-space muscle group synergy characteristic analysis, activation coefficient analysis, frequency-space muscle group synergy characteristic analysis and different frequency band muscle group synergy correlation analysis.
  • the present invention performs muscle group synergy analysis from different dimensions of time-space-frequency, which can more accurately study the subtle changes in neuromuscular activity, help explore the motor control feedback mechanism and the pathological mechanism of movement disorders, establish a rehabilitation status evaluation index based on electromyographic signals, and provide guidance for clinical rehabilitation training.
  • FIG1 is a schematic diagram of a high-density electromyography acquisition device according to an embodiment of the present invention.
  • FIG2 is a schematic diagram of a process for analyzing the relationship between coordinated vectors of different motion modes according to an embodiment of the present invention
  • FIG3 is an activation coefficient analysis diagram of an embodiment of the present invention.
  • FIG4 is a schematic diagram of the overall solution flow of a muscle group coordination analysis method according to an embodiment of the present invention.
  • the central nervous system needs to coordinate multiple degrees of freedom of the musculoskeletal system.
  • the muscle group synergy hypothesis model can solve the problem of multiple degrees of freedom.
  • the central nervous system achieves the target movement by controlling a group of muscles that are activated together rather than a single muscle.
  • a muscle group synergy includes multiple muscles, and multiple muscle group synergies may include the same muscle. Muscle group synergies involving multiple groups of coordinated muscles may be a feasible method to reveal the control strategy of the central nervous system.
  • muscle group synergy has been used to study the regulatory mechanisms of motor learning and motor adaptation. Studies have found that changes in specific muscle group synergies are caused by motor learning, further indicating that there are mechanisms to change motor modules to regulate changes from biological mechanisms and effects of motor training. In addition, researchers have evaluated the effectiveness of rehabilitation training by comparing the muscle group synergy characteristics of patients with motor dysfunction before and after rehabilitation training. These studies have shown that muscle group synergy can explain the characteristics of muscle contraction under different training modes.
  • a muscle group synergy analysis method comprising:
  • a high-density electromyography acquisition device (Refa-128, TMS International BV, Netherlands) is used to collect electromyographic signals.
  • the high-density electromyography acquisition device includes an array composed of multiple electrodes.
  • the electrode array is composed of 48 electrodes, which are divided into 6 rows and 8 columns.
  • the method takes the analysis of the muscle group coordination characteristics of isometric and isotonic wrist movements as an example, and the high-density electromyography acquisition device is wrapped around the upper part of the forearm.
  • the electrodes are distributed from the elbow joint to the wrist, with 8 electrodes per circle, a total of 6 circles, and the electrodes in each circle are evenly spaced, and the interval between adjacent electrode circles is 2 cm.
  • the electromyography signal sampling frequency is 1024 Hz.
  • High-density EMG electrodes are small and numerous, covering a wide range of skin surface, and can capture subtle changes in neuromuscular activity. Especially for some actions that require the coordination of deep and small muscles in the forearm, high-density EMG signals can obtain more accurate information than traditional low-channel EMG signals.
  • the preprocessing is specifically to use a fourth-order Butterworth filter to bandpass filter the electromyographic signal, and further, the bandpass filter frequency of the fourth-order Butterworth filter is 30-450Hz; and then use the fourth-order Butterworth filter to low-pass filter the electromyographic signal to obtain the envelope signal of the electromyographic signal; further, the frequency of the low-pass filter is 20Hz.
  • the envelope signals of multiple channels form an envelope matrix.
  • the envelope matrix is decomposed using non-negative matrix factorization algorithm to extract muscle group synergy;
  • NMF non-negative matrix factorization
  • X is the high-density EMG envelope signal matrix
  • W is the muscle group synergy vector group
  • H is the activation coefficient matrix
  • M is the number of signal channels
  • N is the number of extracted muscle group synergies
  • T is the number of data points.
  • VAF When the number of muscle group synergies is determined to be n, ideally, VAF will not change as the number of muscle group synergies increases.
  • M is the number of signal channels
  • VAF the definition of VAF is as follows:
  • the dimension of similarity evaluation includes at least one of cosine of principal angles (CPA), Euclidian distance (ED) and similarity index SSIM.
  • the Euclidean distance ED is defined as the average Euclidean distance between the synergistic vectors of two synergistic vector groups.
  • the similarity index SSIM is defined as the average similarity index between the synergistic vectors of two synergistic vector groups.
  • the cosine angle CPA is defined as the average cosine value of the angle between the synergistic vectors of two synergistic vector groups. Since the number of synergistic vectors contained in the two synergistic vector groups is often different, it is necessary to calculate the average value twice, that is, once in the positive order and once in the reverse order. Finally, the two average values are divided by 2 to obtain CPA.
  • CPA Cosine of Principal Angles
  • ED Euclidian Distance
  • SSIM similarity index
  • w1i is the ith synergy of matrix W1
  • w2j is the jth synergy of matrix W2
  • p and q are the number of synergies of matrix W1 and W2 respectively.
  • X, Y are two vectors, L(X, Y) ⁇ , C(X, Y) ⁇ , S(X, Y) ⁇ are brightness term, contrast term and structure term respectively.
  • ⁇ , ⁇ , ⁇ are the default values 1.
  • the larger the CPA value the higher the similarity of the muscle group synergy vector.
  • the larger the ED value the lower the similarity of the muscle group synergy vector.
  • the larger the SSIM value the higher the similarity of the muscle group synergy vector.
  • the present invention analyzes muscle group synergy in different movement modes according to the needs of clinical rehabilitation training. Isometric exercise, isotonic exercise, and isokinetic exercise are common clinical rehabilitation training movement modes, and analyzing their muscle group synergy characteristics has more clinical guiding significance.
  • the clustering method is K-Means clustering.
  • the number of clusters k is further determined using the elbow rule, and all cooperative vectors in each motion mode are clustered into k classes.
  • the loss function W(C) is defined as the sum of the distances between the sample and the center of the class to which it belongs.
  • the centers of k classes are selected, and the samples are assigned one by one to the class with the closest center to obtain a clustering result.
  • the mean of the samples in each class is updated as the new center of the class. The above steps are repeated to minimize the loss function W(C) and reach convergence.
  • the predetermined number is one third of the average number.
  • the remaining cluster centers are representative collaborative vectors.
  • the representative collaborative vectors under the two motion modes are paired by similarity.
  • the representative collaborative vectors with a similarity higher than a preset threshold are the common collaborative vectors between the two motion modes, and the others are unique collaborative vectors.
  • the preset threshold is 0.85, that is, when the similarity CPA>0.85, it is a common collaborative vector, and when the similarity is lower than the preset threshold 0.85, it is a unique collaborative vector.
  • the clustering method was used to extract representative synergy vectors, and the common synergy vectors were determined based on their similarities, further exploring the common spatial characteristics between muscle group synergies in different movement patterns.
  • the synergy vector W can represent the muscle activation weight.
  • the linear regression method is used to explore the change pattern and extract the spatial characteristics of the synergy vector.
  • the function of W can be expressed as:
  • M the mean vector of all such synergistic vectors
  • M ⁇ m 1 , m 2 , ..., m ⁇ ⁇
  • ⁇ the optimal change ratio to be determined by linear regression.
  • the loss function is defined as:
  • the optimal change ratio ⁇ can be obtained by the least squares method:
  • the activation coefficient H can represent the activation state of each cooperative vector over time. Its peak value A and its corresponding time t max indicate the time point when the cooperative vector reaches the maximum activation value.
  • each muscle group synergy can be represented as a three-dimensional array.
  • the convex hull algorithm is used to obtain the three-dimensional convex hulls of the three-dimensional arrays ⁇ A, t max , ⁇ corresponding to all muscle group synergies in the isometric and isotonic modes, and the degree of difference in the spatiotemporal characteristics of muscle group synergies in the two movement modes is determined by analyzing the overlap of the two three-dimensional convex hulls.
  • the larger the CPA value the higher the similarity of the muscle group synergy vector.
  • the larger the ED value the lower the similarity of the muscle group synergy vector.
  • the larger the SSIM value the higher the similarity of the muscle group synergy vector.
  • the effective activation time starting point t start , effective activation time end t end , activation peak A and peak corresponding time point t max are selected to analyze the time characteristics of muscle group coordinated activation.
  • the formula of the average activation value p is as follows:
  • h is the activation coefficient corresponding to the synergy vector
  • t0 is the action start time
  • t1 is the action end time
  • p is the average activation value.
  • the effective activation value means not less than the average activation value
  • the effective activation time start point and the effective activation time end point are determined according to the effective activation value; wherein the effective activation time start point t start is the earliest time point at which the effective activation value is reached, and the effective activation time start point t end is the latest time point at which the effective activation value is reached.
  • the maximum amplitude of the cooperative vector activation and the time point of its occurrence can be compared by the activation peak A and its corresponding time point t max .
  • a fourth-order Butterworth filter is used to perform bandpass filtering on the electromyographic signal, and further, the bandpass filtering frequency of the fourth-order Butterworth filter is 30 to 450 Hz; the fourth-order Butterworth filter is then used to perform low-pass filtering on the electromyographic signal to obtain the envelope signal of the electromyographic signal; further, the frequency of the low-pass filtering is 20 Hz. Then the signal of each channel is Fourier transformed, and the frequency f and power spectrum P corresponding to each data point are calculated, and the original envelope signal matrix is converted into a new frequency domain matrix Y.
  • the frequency domain matrix Y is decomposed into a muscle group synergy vector group Q and a spectrum matrix H using the non-negative matrix factorization (NMF) algorithm.
  • NMF non-negative matrix factorization
  • Y is the frequency domain signal matrix
  • Q is the synergy vector group
  • R is the spectrum matrix
  • m is the number of signal channels
  • n is the number of extracted muscle group synergies
  • l is the number of data points
  • the value of each data point is (f, P).
  • the number of muscle group synergies is determined. When the number of muscle group synergies is determined to be n, ideally, VAF will not change with the increase in the number of muscle group synergies.
  • the linear regression method is used to first calculate the corresponding VAF value under each synergy number. There are a total of m VAF values (m is the number of signal channels); the initial value of k is set to 1, and a linear fit is performed on the kth to mth VAFs to calculate the corresponding minimum fitting mean square error; when the minimum fitting mean square error is less than or equal to 0.001, the corresponding number of synergies is the minimum muscle group synergy number n.
  • the definition of VAF is as follows:
  • the synergy vector Q can represent the muscle activation weight.
  • the linear regression method is used to explore the change pattern and extract the spatial characteristics of the synergy vector.
  • the function of Q can be expressed as:
  • the optimal change ratio ⁇ can be obtained by the least squares method:
  • the spectrum matrix R can show the frequency amplitude corresponding to each synergy vector, and its centroid frequency and average frequency D are selected as spectrum features for analysis.
  • the centroid frequency is used to describe the frequency of the signal component with a larger component in the spectrum, reflecting the distribution of the signal power spectrum.
  • the average frequency is the average value of the power spectrum value.
  • the formula of the centroid frequency S is as follows:
  • P(k) is the corresponding power spectrum value
  • fk is the frequency amplitude of the corresponding point.
  • a three-dimensional array ⁇ S, D, ⁇ was constructed to describe the frequency-space characteristics of muscle group synergy, where S is the center of gravity frequency, D is the average frequency, and ⁇ is the optimal change ratio; each muscle group synergy can be represented as a three-dimensional array.
  • the convex hull algorithm was used to calculate two corresponding three-dimensional convex hulls for the three-dimensional arrays corresponding to all muscle group synergies in the isometric and isotonic modes. The degree of overlap of the two three-dimensional convex hulls was analyzed to study the difference in the frequency-space characteristics of muscle group synergy in the two movement modes.
  • the spectrum matrix corresponding to the cooperative vector is divided into multiple segments according to the frequency.
  • the spectrum matrix corresponding to the cooperative vector is divided into three segments according to the frequency: 0-50 Hz is a low frequency band, 50-150 Hz is a medium frequency band, and greater than 150 Hz is a high frequency band.
  • Spectral coherence SC is used to perform correlation analysis on the same frequency band corresponding to different synergy vectors.
  • Spectral coherence SC is defined as:
  • SCxy is the spectral coherence of signal x and signal y, which is used to measure the correlation between two signals in the frequency domain.
  • Pxy is the cross-spectral density
  • Pxx and Pyy are the autospectral densities.
  • the present invention also provides a muscle group coordination analysis system, comprising
  • the electromyographic signal collector is used to collect the user's electromyographic signal in real time and transmit the collected electromyographic signal data to a data storage device.
  • the data storage device is used to store electromyographic signal data; the electromyographic signals of multiple channels are stored separately for the control terminal to read and analyze and calculate.
  • the control terminal is used to execute the above-mentioned analysis method, which includes but is not limited to time-space domain and frequency-space domain analysis of muscle group synergy, synergy vector similarity evaluation, common synergy and unique synergy analysis, activation coefficient analysis, spectrum analysis and muscle group synergy correlation analysis in different frequency bands.
  • analysis method includes but is not limited to time-space domain and frequency-space domain analysis of muscle group synergy, synergy vector similarity evaluation, common synergy and unique synergy analysis, activation coefficient analysis, spectrum analysis and muscle group synergy correlation analysis in different frequency bands.
  • the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions.
  • the program instructions When the program instructions are executed by a computer, the computer executes the above-mentioned method.
  • the present invention also provides a computer-readable storage medium on which computer instructions are stored. When the computer instructions are executed by a processor, the above method is completed.
  • the present invention uses a high-density electromyographic device to collect the electromyographic signals of isometric and isotonic wrist movements of 100 subjects, extracts muscle group synergy after preprocessing the signals, and analyzes the similarity.
  • the proportion of common muscle group synergies to all synergies is basically consistent with the similarity value.
  • the experimental results are still stable when 48 channels are changed to 24 or 16 channels.
  • the present invention analyzes the time-space domain and frequency-space domain of muscle group synergy under the two movement modes, and finds that there are overlapping parts in the time-space convex hull and frequency-controlled convex hull under the two movement modes, which shows that the muscle group synergy of the two movement modes has similar parts in time and space and frequency and space, which is consistent with the previous results of muscle group synergy similarity.
  • muscle group synergy is analyzed from two perspectives: time-space domain and frequency-space domain, involving synergy vector similarity evaluation, common synergy and unique synergy, time-space muscle group synergy characteristic analysis, activation coefficient analysis, frequency-space muscle group synergy characteristic analysis and different frequency band muscle group synergy correlation analysis.
  • the present invention performs muscle group synergy analysis from different dimensions of time-space-frequency, which can more accurately study the subtle changes in neuromuscular activity, help explore the motor control feedback mechanism and the pathological mechanism of movement disorders, establish a rehabilitation status evaluation index based on electromyographic signals, and provide guidance for clinical rehabilitation training.

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Abstract

A muscle group synergy analysis method and system, which relate to the technical fields of neurological rehabilitation engineering and movement mechanisms. For muscle group synergy, analysis is performed from two perspectives, i.e., a time-space domain and a frequency-space domain, and involves synergy vector similarity evaluation, common synergy and specific synergy analysis, time-space muscle group synergy characteristic analysis, activation coefficient analysis, frequency-space muscle group synergy characteristic analysis, and muscle group synergy relevancy analysis in respect of different frequency bands. Compared with a traditional muscle synergy analysis method, muscle group synergy analysis is performed from different dimensions, i.e., time, space and frequency, such that subtle changes in neuromuscular activities can be researched more accurately, exploration and research on a movement control feedback mechanism and a movement disorder pathomechanism are facilitated, a rehabilitation state evaluation index based on an electromyographic signal is established, and instructional significance can be provided for clinical rehabilitation training.

Description

一种肌群协同分析方法和***A muscle group synergy analysis method and system 技术领域Technical Field
本发明涉及神经康复工程及运动机制技术领域，尤其一种肌群协同分析方法和***。The present invention relates to the field of neurorehabilitation engineering and movement mechanism technology, and in particular to a muscle group coordination analysis method and system.
背景技术Background technique
目前，肌群协同分析已经被应用到运动功能障碍患者和健康成人的上肢和下肢运动控制研究中。但是，这些研究技术一般使用十余个肌电信号通道，电极定位主要依赖于解剖学经验，这会导致定位不准确。尤其对一些需要前臂的深且小块的肌肉协调作用的动作而言，稀疏的电极可能不能捕捉神经肌肉活动的细微变化。高密度肌电电极覆盖面广，能够解决上述一系列问题。At present, muscle group synergy analysis has been applied to the study of upper and lower limb motor control in patients with motor dysfunction and healthy adults. However, these research techniques generally use more than ten EMG signal channels, and electrode positioning mainly relies on anatomical experience, which can lead to inaccurate positioning. Especially for some actions that require the coordination of deep and small muscles in the forearm, sparse electrodes may not be able to capture subtle changes in neuromuscular activity. High-density EMG electrodes have a wide coverage area and can solve the above series of problems.
此外，现有的研究方法一般只局限于对单个动作或多个动作的时域肌群协同进行分析，以定量研究肌群协同结构矩阵的相似度为主，而缺少从空域、频域的进一步考量。In addition, existing research methods are generally limited to the analysis of time-domain muscle group coordination of a single movement or multiple movements, mainly focusing on quantitative research on the similarity of muscle group coordination structure matrices, but lack further consideration from the spatial and frequency domains.
发明内容Summary of the invention
本发明主要目的是提供一种基于高密度肌电的时-空-频域肌群协同分析方法，应用于不同康复训练模式下运动模式间的肌群协同特性研究，从肌肉协同工作的角度，探讨运动肌间协同特性，旨在为运动功能康复评估提供研究方法和科学依据。The main purpose of the present invention is to provide a time-space-frequency domain muscle group synergy analysis method based on high-density electromyography, which is applied to the study of muscle group synergy characteristics between movement patterns under different rehabilitation training modes. From the perspective of muscle collaborative work, the synergy characteristics between movement muscles are explored, aiming to provide a research method and scientific basis for motor function rehabilitation evaluation.
为实现上述目的，本发明提供一种肌群协同分析方法，包括To achieve the above object, the present invention provides a muscle group synergy analysis method, comprising:
获取多通道肌电信号；Acquire multi-channel electromyographic signals;
对多通道肌电信号进行预处理，形成多通道包络矩阵；Preprocess the multi-channel electromyographic signals to form a multi-channel envelope matrix;
采用非负矩阵分解算法对包络矩阵进行分解，提取肌群协同；The envelope matrix is decomposed using non-negative matrix factorization algorithm to extract muscle group synergy;
确定肌群协同个数；Determine the number of muscle group synergies;
对肌群协同进行相似度评估。Similarity assessment of muscle group synergy.
作为本发明的一种方案，所述相似度评估的维度至少包括欧式距离，余弦夹角和相似指数中的一种。As a solution of the present invention, the dimension of similarity evaluation includes at least one of Euclidean distance, cosine angle and similarity index.
作为本发明的一种方案，还包括共有协同和特有协同分析，As one solution of the present invention, it also includes common synergy and unique synergy analysis.
将每种运动模式下所有协同向量聚成k类，定义样本与其所属类的中心之间的距离的综合为损失函数，选择k个类的中心，将样本逐个指派到与其最近的中心的类中，得到一个聚类结果；然后更新每个类的样本的均值，作为类的新的中心；重复以上步骤，使损失函数极小化并达到收敛；Cluster all cooperative vectors in each motion mode into k classes, define the sum of the distances between the sample and the center of the class to which it belongs as the loss function, select the centers of k classes, assign the samples one by one to the class with the closest center, and obtain a clustering result; then update the mean of the samples in each class as the new center of the class; repeat the above steps to minimize the loss function and reach convergence;
把含有协同向量个数少于预定个数的类剔除，剩下的聚类中心为代表协同向量，将两种运动模式下的代表协同向量进行相似度配对，相似度高于预设阈值的代表协同向量为两种运动模式之间的共有协同向量，其他的为特有协同向量。The classes with less than a predetermined number of collaborative vectors are eliminated, and the remaining cluster centers are representative collaborative vectors. The representative collaborative vectors in the two motion modes are paired according to similarity. The representative collaborative vectors with a similarity higher than a preset threshold are the common collaborative vectors between the two motion modes, and the others are unique collaborative vectors.
作为本发明的一种方案，还包括时-空特性分析，As a solution of the present invention, it also includes time-space characteristic analysis,
构建三维数组{A，t max，ω}，其中A为激活峰值，t max为激活峰值对应的时间，ω为最优变化比率； Construct a three-dimensional array {A, t max , ω}, where A is the activation peak, t max is the time corresponding to the activation peak, and ω is the optimal change ratio;
采用凸包算法分别求出等长运动模式下和等张运动模式下所有肌群协同对应三维数组{A，t max，ω}的三维凸包，通过分析两个三维凸包的重叠程度来判断两种运动模式下的肌群协同的时空特性的差异程度。 The convex hull algorithm is used to obtain the three-dimensional convex hulls of the three-dimensional arrays {A, t max , ω} corresponding to all muscle group synergies in the isometric and isotonic modes, and the degree of difference in the spatiotemporal characteristics of muscle group synergies in the two movement modes is determined by analyzing the overlap of the two three-dimensional convex hulls.
作为本发明的一种方案，还包括激活系数分析，As a solution of the present invention, it also includes activation coefficient analysis,
计算平均激活值；Calculate the average activation value;
根据平均激活值确定有效激活值；Determine the effective activation value based on the average activation value;
根据有效激活值确定有效激活时间起点、有效激活时间终点；Determine the effective activation time starting point and effective activation time ending point according to the effective activation value;
通过分析有效激活时间起点、有效激活时间终点比较不同动作的协同向量有效激活的时间范围；By analyzing the effective activation time start point and effective activation time end point, the effective activation time range of the synergy vectors of different actions is compared;
由激活峰值及其对应时间点t max可以比较协同向量激活的最大幅值及其出现的时间点。 The maximum amplitude of the cooperative vector activation and the time point of its occurrence can be compared by the activation peak and its corresponding time point t max .
作为本发明的一种方案，还包括频-空特性分析，As a solution of the present invention, it also includes frequency-space characteristic analysis,
对多通道肌电信号进行傅里叶变换得到频域矩阵；Perform Fourier transform on multi-channel electromyographic signals to obtain a frequency domain matrix;
构建三维数组{S，D，β}，其中A为激活峰值，t max为激活峰值对应的时间，ω为最优变化比率； Construct a three-dimensional array {S, D, β}, where A is the activation peak, t max is the time corresponding to the activation peak, and ω is the optimal change ratio;
采用凸包算法分别求出等长运动模式下和等张运动模式下所有肌群协同对应三维数组{S，D，β}的三维凸包，通过分析两个三维凸包的重叠程度来判断 两种运动模式下的肌群协同的频空特性的差异程度。The convex hull algorithm is used to calculate the three-dimensional convex hulls of the three-dimensional arrays {S, D, β} corresponding to all muscle group synergies in the isometric and isotonic modes, respectively. The degree of difference in the frequency-space characteristics of muscle group synergies in the two movement modes is determined by analyzing the overlap of the two three-dimensional convex hulls.
作为本发明的一种方案，还包括频段肌群协同相关度分析，As a solution of the present invention, it also includes frequency band muscle group synergy correlation analysis,
将协同向量对应的频谱矩阵按照频率高低分为多段；The spectrum matrix corresponding to the synergy vector is divided into multiple segments according to the frequency;
采用波谱相干对不同协同向量对应的同一频带进行相关度分析。Spectral coherence is used to perform correlation analysis on the same frequency band corresponding to different cooperative vectors.
本发明还提供一种肌群协同分析***，包括The present invention also provides a muscle group coordination analysis system, comprising
肌电信号采集器，用于实时采集用户的肌电信号；The electromyographic signal collector is used to collect the user's electromyographic signals in real time;
数据存储器，用于存储肌电信号数据；A data storage device, used for storing electromyographic signal data;
控制终端，用于执行上述的分析方法。The control terminal is used to execute the above analysis method.
本发明还提供一种计算机程序产品，所述计算机程序产品包括存储在非暂态计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行上述的方法。The present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the above method.
本发明还提供一种计算机可读存储介质，其上存储有计算机指令，所述计算机指令被处理器运行时，完成上述的方法The present invention also provides a computer-readable storage medium on which computer instructions are stored, and the computer instructions are executed by a processor to complete the above method
本发明的上述技术方案中，针对肌群协同从时-空域和频-空域两个角度进行分析，涉及协同向量相似度评估，共有协同和特有协同，时-空肌群协同特性分析，激活系数分析，频-空肌群协同特性分析和不同频段肌群协同相关度分析。与传统的肌肉协同分析方法相比，本发明从时-空-频不同维度进行肌群协同分析，能够更加精确地研究神经肌肉活动的细微变化，有助于探索研究运动控制反馈机制及运动障碍病理机制，建立基于肌电信号的康复状态评价指标，能够为临床康复训练提供指导意义。In the above technical scheme of the present invention, muscle group synergy is analyzed from two perspectives: time-space domain and frequency-space domain, involving synergy vector similarity evaluation, common synergy and unique synergy, time-space muscle group synergy characteristic analysis, activation coefficient analysis, frequency-space muscle group synergy characteristic analysis and different frequency band muscle group synergy correlation analysis. Compared with the traditional muscle synergy analysis method, the present invention performs muscle group synergy analysis from different dimensions of time-space-frequency, which can more accurately study the subtle changes in neuromuscular activity, help explore the motor control feedback mechanism and the pathological mechanism of movement disorders, establish a rehabilitation status evaluation index based on electromyographic signals, and provide guidance for clinical rehabilitation training.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明实施例一种高密度肌电采集设备示意图；FIG1 is a schematic diagram of a high-density electromyography acquisition device according to an embodiment of the present invention;
图2为本发明实施例不同运动模式协同向量关系分析流程示意图；FIG2 is a schematic diagram of a process for analyzing the relationship between coordinated vectors of different motion modes according to an embodiment of the present invention;
图3为本发明实施例激活系数分析图；FIG3 is an activation coefficient analysis diagram of an embodiment of the present invention;
图4为本发明实施例一种肌群协同分析方法整体方案流程示意图。FIG4 is a schematic diagram of the overall solution flow of a muscle group coordination analysis method according to an embodiment of the present invention.
具体实施方式Detailed ways
下面将结合本发明实施方式中的附图，对本发明实施方式中的技术方案进行清楚、完整地描述，显然，所描述的实施方式仅仅是本发明的一部分实施方 式，而不是全部的实施方式。基于本发明中的实施方式，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施方式，都属于本发明保护的范围。The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
需要说明，本发明实施方式中所有方向性指示(诸如上、下……)仅用于解释在某一特定姿态(如附图所示)下各部件之间的相对位置关系、运动情况等，如果该特定姿态发生改变时，则该方向性指示也相应地随之改变。It should be noted that all directional indications (such as up, down, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.
并且，本发明各个实施方式之间的技术方案可以相互结合，但是必须是以本领域普通技术人员能够实现为基础，当技术方案的结合出现相互矛盾或无法实现时应当认为这种技术方案的结合不存在，也不在本发明要求的保护范围之内。Furthermore, the technical solutions between the various embodiments of the present invention may be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in the field. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.
中枢神经***疾病如卒中、脑外伤、脑瘫等常常导致肌无力、肌痉挛、共济失调等典型的运动功能障碍。力量训练已经作为一种在临床上被用来恢复患者上肢功能的治疗方法之一。不同肌肉收缩类型的运动被应用于力量训练，在临床上，等长运动、等张运动、等速运动最为普遍。等长运动涉及肌肉收缩，但是不移动；等张运动除了肌肉收缩，还伴随移动；等速运动时，肌肉在整个关节运动范围内以恒定的速度进行最大用力收缩，又分为离心性训练和向心性训练等方式。因此，阐释不同运动模式下肌肉收缩尤为重要，特别是对于需要为不同运动功能损伤级别患者提供治疗方案的理疗师而言。Central nervous system diseases such as stroke, brain trauma, cerebral palsy, etc. often lead to typical motor dysfunctions such as muscle weakness, muscle spasm, and ataxia. Strength training has been used as one of the treatment methods to restore the upper limb function of patients in clinical practice. Different types of muscle contraction exercises are used in strength training. In clinical practice, isometric exercise, isotonic exercise, and isokinetic exercise are the most common. Isometric exercise involves muscle contraction but no movement; isotonic exercise is accompanied by movement in addition to muscle contraction; during isokinetic exercise, the muscle contracts with maximum force at a constant speed throughout the range of joint motion, which is divided into eccentric training and concentric training. Therefore, it is particularly important to explain muscle contraction in different movement modes, especially for physical therapists who need to provide treatment plans for patients with different levels of motor function impairment.
先前研究中，通常选择成对肌肉，通过使用简单的度量方法，如基于表面肌电信号的共同收缩指标，模糊近似熵，功率谱分析，被用来描述不同运动下神经肌肉调节。为进一步探索病理变化，一些研究者提取动作单位激励比率，募集阈值，通过分解高密度肌电来评估变化动作单位激励行为。值得注意的是，这些研究只涉及一对收缩肌和拮抗肌或特定的一块肌肉，不能阐明相关肌群的协调肌肉活动。In previous studies, pairs of muscles were usually selected and simple metrics such as co-contraction index based on surface electromyographic signals, fuzzy approximate entropy, and power spectrum analysis were used to describe neuromuscular regulation under different movements. To further explore pathological changes, some researchers extracted action unit excitation ratios, recruitment thresholds, and evaluated changes in action unit excitation behavior by decomposing high-density electromyography. It is worth noting that these studies only involved a pair of agonist and antagonist muscles or a specific muscle, and could not clarify the coordinated muscle activity of related muscle groups.
理解中枢神经***如何组织和控制运动动作和行为是神经科学研究的一个基础问题。事实上，几乎所有动作都需要多块肌肉协调配合才能完成。为了实现一系列动作目标，中枢神经***需要协调肌肉骨骼***的多个自由度。肌群协同假设模型能够解决多个自由度问题。在肌群协同理论中，中枢神经***通过控制一组共同激活的肌肉而非单块肌肉，来实现目标动作。一个肌群协同包括多块肌肉，多个肌群协同可能包含相同的一块肌肉。涉及多组协调的肌肉的肌群协同可能是一种可行的方法，揭示了中枢神经***的控制策略。特别地， 肌群的线性组合控制动作的产生，每个肌群协同激活一组肌肉。近年来，肌群协同被用来研究动作学习和动作适应的调节机制。研究发现特定肌群协同的变化源于动作学习，进一步表明存在机制改变运动模块来调节来自动作训练生物机制和影响的变化。此外，研究人员通过比较运动功能障碍患者进行康复训练前后的肌群协同特征来评估康复训练效果。这些研究表明肌群协同能够阐释不同训练模式下肌肉收缩的特征。Understanding how the central nervous system organizes and controls motor movements and behaviors is a fundamental problem in neuroscience research. In fact, almost all movements require the coordination of multiple muscles to complete. In order to achieve a series of movement goals, the central nervous system needs to coordinate multiple degrees of freedom of the musculoskeletal system. The muscle group synergy hypothesis model can solve the problem of multiple degrees of freedom. In the muscle group synergy theory, the central nervous system achieves the target movement by controlling a group of muscles that are activated together rather than a single muscle. A muscle group synergy includes multiple muscles, and multiple muscle group synergies may include the same muscle. Muscle group synergies involving multiple groups of coordinated muscles may be a feasible method to reveal the control strategy of the central nervous system. In particular, a linear combination of muscle groups controls the generation of movements, and each muscle group synergistically activates a group of muscles. In recent years, muscle group synergy has been used to study the regulatory mechanisms of motor learning and motor adaptation. Studies have found that changes in specific muscle group synergies are caused by motor learning, further indicating that there are mechanisms to change motor modules to regulate changes from biological mechanisms and effects of motor training. In addition, researchers have evaluated the effectiveness of rehabilitation training by comparing the muscle group synergy characteristics of patients with motor dysfunction before and after rehabilitation training. These studies have shown that muscle group synergy can explain the characteristics of muscle contraction under different training modes.
为此，参见图1-4，根据本发明的一方面提供一种肌群协同分析方法，包括To this end, referring to FIGS. 1-4 , according to one aspect of the present invention, a muscle group synergy analysis method is provided, comprising:
获取多通道肌电信号；Acquire multi-channel electromyographic signals;
使用高密度肌电采集设备(Refa-128,TMS International BV，Netherlands)采集肌电信号，如图1所示，高密度肌电采集设备包括多个电极组成的阵列。作为本发明的一个实施例，电极阵列由48个电极组成，分为6行8列。作为本发明的一个实施例，本方法以分析等长等张腕部运动的肌群协同特征为例，将高密度肌电采集设备缠绕前臂的上半部分，电极从肘关节至手腕方向分布，每圈8个电极，一共6圈，每一圈的电极间隔均匀，相邻电极圈的间隔为2厘米。作为本发明的一个实施例，肌电信号采样频率为1024Hz。A high-density electromyography acquisition device (Refa-128, TMS International BV, Netherlands) is used to collect electromyographic signals. As shown in FIG1 , the high-density electromyography acquisition device includes an array composed of multiple electrodes. As an embodiment of the present invention, the electrode array is composed of 48 electrodes, which are divided into 6 rows and 8 columns. As an embodiment of the present invention, the method takes the analysis of the muscle group coordination characteristics of isometric and isotonic wrist movements as an example, and the high-density electromyography acquisition device is wrapped around the upper part of the forearm. The electrodes are distributed from the elbow joint to the wrist, with 8 electrodes per circle, a total of 6 circles, and the electrodes in each circle are evenly spaced, and the interval between adjacent electrode circles is 2 cm. As an embodiment of the present invention, the electromyography signal sampling frequency is 1024 Hz.
高密度肌电电极细小且数量多，覆盖皮肤表面面广，能捕捉神经肌肉活动的细微变化。尤其对一些需要前臂的深且小块的肌肉协调作用的动作而言，高密度肌电信号要比传统的少通道肌电信号获得的信息更加精确。High-density EMG electrodes are small and numerous, covering a wide range of skin surface, and can capture subtle changes in neuromuscular activity. Especially for some actions that require the coordination of deep and small muscles in the forearm, high-density EMG signals can obtain more accurate information than traditional low-channel EMG signals.
对多通道肌电信号进行预处理，形成多通道包络矩阵；Preprocess the multi-channel electromyographic signals to form a multi-channel envelope matrix;
作为本发明的一个实施例，所述预处理具体为对肌电信号，采用四阶巴特沃兹滤波器对肌电信号进行带通滤波，进一步的，所述四阶巴特沃兹滤波器的带通滤波频率为30～450Hz；再利用四阶巴特沃兹滤波器对肌电信号进行低通滤波，获得肌电信号的包络信号；进一步的，所述低通滤波的频率为20Hz。多个通道的包络信号形成包络矩阵。As an embodiment of the present invention, the preprocessing is specifically to use a fourth-order Butterworth filter to bandpass filter the electromyographic signal, and further, the bandpass filter frequency of the fourth-order Butterworth filter is 30-450Hz; and then use the fourth-order Butterworth filter to low-pass filter the electromyographic signal to obtain the envelope signal of the electromyographic signal; further, the frequency of the low-pass filter is 20Hz. The envelope signals of multiple channels form an envelope matrix.
采用非负矩阵分解算法对包络矩阵进行分解，提取肌群协同；The envelope matrix is decomposed using non-negative matrix factorization algorithm to extract muscle group synergy;
采用非负矩阵分解算法(non-negative matrix factorization，NMF)将经过预处理的高密度肌电包络信号矩阵X分解成肌群协同向量组W和激活系数矩阵H。公式如下：The non-negative matrix factorization (NMF) algorithm is used to decompose the preprocessed high-density electromyographic envelope signal matrix X into a muscle group coordination vector group W and an activation coefficient matrix H. The formula is as follows:
X M*T≈W M*N*H N*T#(1) X M*T ≈W M*N *H N*T #(1)
其中，X为高密度肌电包络信号矩阵，W为肌群协同向量组，H为激活系数矩阵，M为信号通道数目，N为提取的肌群协同个数，T为数据点个数。Among them, X is the high-density EMG envelope signal matrix, W is the muscle group synergy vector group, H is the activation coefficient matrix, M is the number of signal channels, N is the number of extracted muscle group synergies, and T is the number of data points.
确定肌群协同个数；Determine the number of muscle group synergies;
当肌群协同个数确定为n时，理想情况下，VAF不会随肌群协同个数增加而改变。采用线性回归的方法，先计算每一协同数量下对应的VAF值，共有M个VAF值(M为信号通道数目)；设置k的初始值为1，对第k个到第M个VAF进行线性拟合，求相应最小的拟合均方误差；当最小的拟合均方误差小于或等于0.001时，对应的协同个数为最小肌群协同数目n。其中，VAF的定义如下：When the number of muscle group synergies is determined to be n, ideally, VAF will not change as the number of muscle group synergies increases. Using the linear regression method, first calculate the VAF value corresponding to each synergy number, with a total of M VAF values (M is the number of signal channels); set the initial value of k to 1, perform linear fitting on the kth to Mth VAFs, and calculate the corresponding minimum fitting mean square error; when the minimum fitting mean square error is less than or equal to 0.001, the corresponding number of synergies is the minimum muscle group synergy number n. Among them, the definition of VAF is as follows:
Figure PCTCN2022134779-appb-000001
Figure PCTCN2022134779-appb-000001
对肌群协同进行相似度评估。Similarity assessment of muscle group synergy.
为了度量每种运动模式内和两种运动模式间肌群协同向量相似度，作为本发明的一种方案，所述相似度评估的维度至少包括角余弦(Cosine of Principal Angles，CPA)，欧式距离(Euclidian Distance,ED)和相似指数SSIM中的一种。In order to measure the similarity of muscle group coordination vectors within each movement mode and between two movement modes, as a solution of the present invention, the dimension of similarity evaluation includes at least one of cosine of principal angles (CPA), Euclidian distance (ED) and similarity index SSIM.
在本发明中，欧式距离ED被定义为两个协同向量组的协同向量之间的欧式距离平均值。相似指数SSIM被定义为两个协同向量组的协同向量之间的相似指数平均值。夹角余弦CPA被定义为两个协同向量组的协同向量之间的夹角余弦值平均值。由于两个协同向量组包含的协同向量个数往往不同，需要计算两次平均值，即正序和逆序各计算一次。最后，将这两个平均值除以2得到CPA。In the present invention, the Euclidean distance ED is defined as the average Euclidean distance between the synergistic vectors of two synergistic vector groups. The similarity index SSIM is defined as the average similarity index between the synergistic vectors of two synergistic vector groups. The cosine angle CPA is defined as the average cosine value of the angle between the synergistic vectors of two synergistic vector groups. Since the number of synergistic vectors contained in the two synergistic vector groups is often different, it is necessary to calculate the average value twice, that is, once in the positive order and once in the reverse order. Finally, the two average values are divided by 2 to obtain CPA.
其中，夹角余弦(Cosine of Principal Angles，CPA)，欧式距离(Euclidian Distance,ED)和相似指数SSIM的定义分别如下所示：Among them, the definitions of Cosine of Principal Angles (CPA), Euclidian Distance (ED) and similarity index SSIM are as follows:
Figure PCTCN2022134779-appb-000002
Figure PCTCN2022134779-appb-000002
Figure PCTCN2022134779-appb-000003
Figure PCTCN2022134779-appb-000003
ssim(X，Y)＝L(X，Y) α*C(X，Y) β*S(X，Y) γ#(5)# ssim(X,Y)=L(X,Y) α *C(X,Y) β *S(X,Y) γ #(5)#
Figure PCTCN2022134779-appb-000004
Figure PCTCN2022134779-appb-000004
其中，w 1i是矩阵W 1的第ith个协同,w 2j是矩阵W 2的第jth个协同,p和q分别为矩阵W 1和W 2的协同个数。X,Y为两个向量，L(X，Y) α，C(X，Y) β，S(X，Y) γ分别为亮度项、对比度项和结构项。α，β，γ为默认值1。 Where w1i is the ith synergy of matrix W1 , w2j is the jth synergy of matrix W2 , p and q are the number of synergies of matrix W1 and W2 respectively. X, Y are two vectors, L(X, Y) α , C(X, Y) β , S(X, Y) γ are brightness term, contrast term and structure term respectively. α, β, γ are the default values 1.
其中，CPA的值越大，说明肌群协同向量的相似度越高。ED的值越大，说明肌群协同向量的相似度越低。SSIM的值越大，说明肌群协同向量的相似度越高。Among them, the larger the CPA value, the higher the similarity of the muscle group synergy vector. The larger the ED value, the lower the similarity of the muscle group synergy vector. The larger the SSIM value, the higher the similarity of the muscle group synergy vector.
目前的大部分肌群协同分析方法局限于单个动作或者多个动作。本发明根据临床康复训练需要，分析不同运动模式下肌群协同。等长运动、等张运动、等速运动作为临床上常见的康复训练动作模式，分析它们的肌群协同特征更具有临床指导意义。Most of the current muscle group synergy analysis methods are limited to single movements or multiple movements. The present invention analyzes muscle group synergy in different movement modes according to the needs of clinical rehabilitation training. Isometric exercise, isotonic exercise, and isokinetic exercise are common clinical rehabilitation training movement modes, and analyzing their muscle group synergy characteristics has more clinical guiding significance.
作为本发明的一种方案，还包括共有协同和特有协同分析，As one solution of the present invention, it also includes common synergy and unique synergy analysis.
如图2所示，将每种运动模式下所有协同向量聚成k类，作为本发明的一种实施方式，所述聚类方法为K-Means聚类；进一步的用肘部法则确定聚类个数k，再将每种运动模式下所有协同向量聚成k类。定义样本与其所属类的中心之间的距离的综合为损失函数W(C)，选择k个类的中心，将样本逐个指派到与其最近的中心的类中，得到一个聚类结果；然后更新每个类的样本的均值，作为类的新的中心；重复以上步骤，使损失函数W(C)极小化并达到收敛；As shown in FIG2 , all cooperative vectors in each motion mode are clustered into k classes. As an implementation of the present invention, the clustering method is K-Means clustering. The number of clusters k is further determined using the elbow rule, and all cooperative vectors in each motion mode are clustered into k classes. The loss function W(C) is defined as the sum of the distances between the sample and the center of the class to which it belongs. The centers of k classes are selected, and the samples are assigned one by one to the class with the closest center to obtain a clustering result. Then the mean of the samples in each class is updated as the new center of the class. The above steps are repeated to minimize the loss function W(C) and reach convergence.
Figure PCTCN2022134779-appb-000005
Figure PCTCN2022134779-appb-000005
其中，
Figure PCTCN2022134779-appb-000006
是第l个类的均值或中心，
Figure PCTCN2022134779-appb-000007
Figure PCTCN2022134779-appb-000008
I(C(i)＝l)是指示函数，取值为1或0。 in,
Figure PCTCN2022134779-appb-000006
is the mean or center of the lth class,
Figure PCTCN2022134779-appb-000007
Figure PCTCN2022134779-appb-000008
I(C(i)=l) is an indicator function, and its value is 1 or 0.
把含有协同向量个数少于预定个数的类剔除，作为本发明的一种实施例，所述预定个数为平均个数的三分之一；剩下的聚类中心为代表协同向量，将两种运动模式下的代表协同向量进行相似度配对，相似度高于预设阈值的代表协同向量为两种运动模式之间的共有协同向量，其他的为特有协同向量；作为本 发明的一种实施例，所述预设阈值为0.85，即当相似度CPA>0.85时为共有协同向量，相似度低于预设阈值0.85时为特有协同向量。Eliminate the classes with less than a predetermined number of collaborative vectors. As an embodiment of the present invention, the predetermined number is one third of the average number. The remaining cluster centers are representative collaborative vectors. The representative collaborative vectors under the two motion modes are paired by similarity. The representative collaborative vectors with a similarity higher than a preset threshold are the common collaborative vectors between the two motion modes, and the others are unique collaborative vectors. As an embodiment of the present invention, the preset threshold is 0.85, that is, when the similarity CPA>0.85, it is a common collaborative vector, and when the similarity is lower than the preset threshold 0.85, it is a unique collaborative vector.
采用聚类的方法提取具有代表性的协同向量，并根据它们之间的相似度来确定共有协同向量，进一步探索了不同运动模式之间肌群协同之间的共同的空间特征。The clustering method was used to extract representative synergy vectors, and the common synergy vectors were determined based on their similarities, further exploring the common spatial characteristics between muscle group synergies in different movement patterns.
作为本发明的一种方案，还包括时-空特性分析，As a solution of the present invention, it also includes time-space characteristic analysis,
协同向量W能够表现肌肉激活权重，为了找出协同向量W的变化趋势，用线性回归的方法来探索变化模式并提取出协同向量的空间特征。对每个协同向量，W的函数可以表示为：The synergy vector W can represent the muscle activation weight. In order to find out the change trend of the synergy vector W, the linear regression method is used to explore the change pattern and extract the spatial characteristics of the synergy vector. For each synergy vector, the function of W can be expressed as:
W(M)＝f(M；ω)#(8)W(M)＝f(M；ω)#(8)
其中，M代表所有该类协同向量的均值向量,M＝{m 1，m 2，...，m α},ω是需要用线性回归方法确定的最优变化比率。为了找出M和W的线性关系，建立线性回归模型为： Where M represents the mean vector of all such synergistic vectors, M = {m 1 , m 2 , ..., m α }, and ω is the optimal change ratio to be determined by linear regression. In order to find the linear relationship between M and W, a linear regression model is established as follows:
Figure PCTCN2022134779-appb-000009
Figure PCTCN2022134779-appb-000009
损失函数定义为：The loss function is defined as:
Figure PCTCN2022134779-appb-000010
Figure PCTCN2022134779-appb-000010
Figure PCTCN2022134779-appb-000011
Figure PCTCN2022134779-appb-000011
最优变化比率ω可由最小二乘法得到：The optimal change ratio ω can be obtained by the least squares method:
Figure PCTCN2022134779-appb-000012
Figure PCTCN2022134779-appb-000012
激活系数H能够表现每个协同向量随时间激活的状态，它的峰值A及其对应的时间t max表明协同向量达到最大的激活值及对应的时间点。 The activation coefficient H can represent the activation state of each cooperative vector over time. Its peak value A and its corresponding time t max indicate the time point when the cooperative vector reaches the maximum activation value.
构建三维数组{A，t max，ω}，其中A为激活峰值，t max为激活峰值对应的时间，ω为最优变化比率；每一个肌群协同都可以被表示为一个三维数组。 Construct a three-dimensional array {A, t max , ω}, where A is the activation peak, t max is the time corresponding to the activation peak, and ω is the optimal change ratio; each muscle group synergy can be represented as a three-dimensional array.
采用凸包算法分别求出等长运动模式下和等张运动模式下所有肌群协同对应三维数组{A，t max，ω}的三维凸包，通过分析两个三维凸包的重叠程度来判断两种运动模式下的肌群协同的时空特性的差异程度。 The convex hull algorithm is used to obtain the three-dimensional convex hulls of the three-dimensional arrays {A, t max , ω} corresponding to all muscle group synergies in the isometric and isotonic modes, and the degree of difference in the spatiotemporal characteristics of muscle group synergies in the two movement modes is determined by analyzing the overlap of the two three-dimensional convex hulls.
其中，CPA的值越大，说明肌群协同向量的相似度越高。ED的值越大， 说明肌群协同向量的相似度越低。SSIM的值越大，说明肌群协同向量的相似度越高。Among them, the larger the CPA value, the higher the similarity of the muscle group synergy vector. The larger the ED value, the lower the similarity of the muscle group synergy vector. The larger the SSIM value, the higher the similarity of the muscle group synergy vector.
作为本发明的一种方案，还包括激活系数分析，As a solution of the present invention, it also includes activation coefficient analysis,
计算平均激活值；Calculate the average activation value;
如图3所示，选取有效激活时间起点t start，有效激活时间终点t end,激活峰值A和峰值对应时间点t max等指标来分析肌群协同激活的时间特征。平均激活值p的公式如下： As shown in Figure 3, the effective activation time starting point t start , effective activation time end t end , activation peak A and peak corresponding time point t max are selected to analyze the time characteristics of muscle group coordinated activation. The formula of the average activation value p is as follows:
Figure PCTCN2022134779-appb-000013
Figure PCTCN2022134779-appb-000013
其中，h为协同向量对应的激活系数，t 0为动作开始时间，t 1为动作结束时间，p为平均激活值。 Among them, h is the activation coefficient corresponding to the synergy vector, t0 is the action start time, t1 is the action end time, and p is the average activation value.
根据平均激活值确定有效激活值；有效激活值指不小于平均激活值；Determine the effective activation value according to the average activation value; the effective activation value means not less than the average activation value;
根据有效激活值确定有效激活时间起点、有效激活时间终点；其中，有效激活时间起点t start为达到有效激活值的最早时间点，有效激活时间起点t end为达到有效激活值的最晚时间点。 The effective activation time start point and the effective activation time end point are determined according to the effective activation value; wherein the effective activation time start point t start is the earliest time point at which the effective activation value is reached, and the effective activation time start point t end is the latest time point at which the effective activation value is reached.
通过分析t start和t end比较不同动作的协同向量有效激活的时间范围； By analyzing t start and t end , we compared the time range of effective activation of the synergistic vectors of different actions;
由激活峰值A及其对应时间点t max可以比较协同向量激活的最大幅值及其出现的时间点。 The maximum amplitude of the cooperative vector activation and the time point of its occurrence can be compared by the activation peak A and its corresponding time point t max .
作为本发明的一种方案，还包括频-空特性分析，As a solution of the present invention, it also includes frequency-space characteristic analysis,
对多通道肌电信号进行傅里叶变换得到频域矩阵；Perform Fourier transform on multi-channel electromyographic signals to obtain a frequency domain matrix;
作为本发明的一个实施例，针对采集的肌电信号，采用四阶巴特沃兹滤波器对肌电信号进行带通滤波，进一步的，所述四阶巴特沃兹滤波器的带通滤波频率为30～450Hz；再利用四阶巴特沃兹滤波器对肌电信号进行低通滤波，获得肌电信号的包络信号；进一步的，所述低通滤波的频率为20Hz。再将每一通道的信号进行傅里叶变换，并计算出每一个数据点对应的频率f和功率谱P,则原来的包络信号矩阵被转换为一个新的频域矩阵Y。As an embodiment of the present invention, for the collected electromyographic signal, a fourth-order Butterworth filter is used to perform bandpass filtering on the electromyographic signal, and further, the bandpass filtering frequency of the fourth-order Butterworth filter is 30 to 450 Hz; the fourth-order Butterworth filter is then used to perform low-pass filtering on the electromyographic signal to obtain the envelope signal of the electromyographic signal; further, the frequency of the low-pass filtering is 20 Hz. Then the signal of each channel is Fourier transformed, and the frequency f and power spectrum P corresponding to each data point are calculated, and the original envelope signal matrix is converted into a new frequency domain matrix Y.
提取肌群协同，用非负矩阵分解算法(non-negative matrix factorization，NMF)将频域矩阵Y分解成肌群协同向量组Q和频谱矩阵H。公式如下：To extract muscle group synergy, the frequency domain matrix Y is decomposed into a muscle group synergy vector group Q and a spectrum matrix H using the non-negative matrix factorization (NMF) algorithm. The formula is as follows:
Y m*l≈Q m*n*R n*l#(14) Y m*l ≈Q m*n *R n*l #(14)
其中，Y为频域信号矩阵，Q为协同向量组，R为频谱矩阵，m为信号通道数目，n为提取的肌群协同个数，l为数据点个数，每一个数据点的值为(f，P)。Among them, Y is the frequency domain signal matrix, Q is the synergy vector group, R is the spectrum matrix, m is the number of signal channels, n is the number of extracted muscle group synergies, l is the number of data points, and the value of each data point is (f, P).
肌群协同个数确定，当肌群协同个数确定为n时，理想情况下，VAF不会随肌群协同个数增加而改变。采用线性回归的方法，先计算每一协同数量下对应的VAF值，共有m个VAF值(m为信号通道数目)；设置k的初始值为1，对第k个到第m个VAF进行线性拟合，求相应最小的拟合均方误差；当最小的拟合均方误差小于或等于0.001时，对应的协同个数为最小肌群协同数目n。其中，VAF的定义如下：The number of muscle group synergies is determined. When the number of muscle group synergies is determined to be n, ideally, VAF will not change with the increase in the number of muscle group synergies. The linear regression method is used to first calculate the corresponding VAF value under each synergy number. There are a total of m VAF values (m is the number of signal channels); the initial value of k is set to 1, and a linear fit is performed on the kth to mth VAFs to calculate the corresponding minimum fitting mean square error; when the minimum fitting mean square error is less than or equal to 0.001, the corresponding number of synergies is the minimum muscle group synergy number n. Among them, the definition of VAF is as follows:
Figure PCTCN2022134779-appb-000014
Figure PCTCN2022134779-appb-000014
协同向量Q能够表现肌肉激活权重，为了找出协同向量Q的变化趋势，用线性回归的方法来探索变化模式并提取出协同向量的空间特征。对每个协同向量，Q的函数可以表示为：The synergy vector Q can represent the muscle activation weight. In order to find out the change trend of the synergy vector Q, the linear regression method is used to explore the change pattern and extract the spatial characteristics of the synergy vector. For each synergy vector, the function of Q can be expressed as:
Q(q)＝f(q；β)#(16)Q(q)＝f(q；β)#(16)
其中，q代表所有该类协同向量的均值向量,q＝{m 1，m 2，...，m α},ω是需要用线性回归方法确定的最优变化比率。为了找出Q和q的线性关系，建立线性回归模型为： Where q represents the mean vector of all such synergistic vectors, q = {m 1 , m 2 , ..., m α }, and ω is the optimal change ratio to be determined by linear regression. In order to find the linear relationship between Q and q, a linear regression model is established as follows:
Figure PCTCN2022134779-appb-000015
Figure PCTCN2022134779-appb-000015
最优变化比率β可由最小二乘法得到：The optimal change ratio β can be obtained by the least squares method:
Figure PCTCN2022134779-appb-000016
Figure PCTCN2022134779-appb-000016
频谱矩阵R能够表现每个协同向量对应的频率幅值，选取它的重心频率及平均频率D为频谱特征进行分析。重心频率用来描述信号在频谱中分量较大的信号成分的频率，反映信号功率谱的分布情况。平均频率为功率谱值平均值。重心频率S的公式如下：The spectrum matrix R can show the frequency amplitude corresponding to each synergy vector, and its centroid frequency and average frequency D are selected as spectrum features for analysis. The centroid frequency is used to describe the frequency of the signal component with a larger component in the spectrum, reflecting the distribution of the signal power spectrum. The average frequency is the average value of the power spectrum value. The formula of the centroid frequency S is as follows:
Figure PCTCN2022134779-appb-000017
Figure PCTCN2022134779-appb-000017
其中，P(k)为对应功率谱值，f k为对应点的频率幅值大小。 Among them, P(k) is the corresponding power spectrum value, and fk is the frequency amplitude of the corresponding point.
平均频率D的公式为：The formula for the average frequency D is:
Figure PCTCN2022134779-appb-000018
Figure PCTCN2022134779-appb-000018
构建三维数组{S，D，β}以描述肌群协同的频空特性，其中S为重心频率，D为平均频率，β为最优变化比率；每一个肌群协同都可以被表示为一个三维数组。用凸包算法将等长运动模式下和等张运动模式下所有肌群协同对应的三维数组分别求出两个对应的三维凸包。通过分析两个三位凸包的重叠程度来研究两种运动模式下的肌群协同的频空特性的差异程度。A three-dimensional array {S, D, β} was constructed to describe the frequency-space characteristics of muscle group synergy, where S is the center of gravity frequency, D is the average frequency, and β is the optimal change ratio; each muscle group synergy can be represented as a three-dimensional array. The convex hull algorithm was used to calculate two corresponding three-dimensional convex hulls for the three-dimensional arrays corresponding to all muscle group synergies in the isometric and isotonic modes. The degree of overlap of the two three-dimensional convex hulls was analyzed to study the difference in the frequency-space characteristics of muscle group synergy in the two movement modes.
作为本发明的一种方案，还包括频段肌群协同相关度分析，As a solution of the present invention, it also includes frequency band muscle group synergy correlation analysis,
将协同向量对应的频谱矩阵按照频率高低分为多段；作为本发明的一种实施例，将协同向量对应的频谱矩阵按照频率分为三段：0-50hz为低频带，50-150hz为中频带，大于150hz为高频带。The spectrum matrix corresponding to the cooperative vector is divided into multiple segments according to the frequency. As an embodiment of the present invention, the spectrum matrix corresponding to the cooperative vector is divided into three segments according to the frequency: 0-50 Hz is a low frequency band, 50-150 Hz is a medium frequency band, and greater than 150 Hz is a high frequency band.
采用波谱相干(Spectral Coherence,SC)对不同协同向量对应的同一频带进行相关度分析。波谱相干SC的定义为：Spectral coherence (SC) is used to perform correlation analysis on the same frequency band corresponding to different synergy vectors. Spectral coherence SC is defined as:
Figure PCTCN2022134779-appb-000019
Figure PCTCN2022134779-appb-000019
其中，SC xy为信号x和信号y的波谱相干，用于测量两个信号在频域内的相关程度。P xy为互谱密度，P xx和P yy为自谱密度。 Where SCxy is the spectral coherence of signal x and signal y, which is used to measure the correlation between two signals in the frequency domain. Pxy is the cross-spectral density, and Pxx and Pyy are the autospectral densities.
本发明还提供一种肌群协同分析***，包括The present invention also provides a muscle group coordination analysis system, comprising
肌电信号采集器，用于实时采集用户的肌电信号，并将采集的肌电信号数据传输至数据存储器。The electromyographic signal collector is used to collect the user's electromyographic signal in real time and transmit the collected electromyographic signal data to a data storage device.
数据存储器，用于存储肌电信号数据；多个通道的肌电信号分别进行存储，供控制终端读取并进行分析计算。The data storage device is used to store electromyographic signal data; the electromyographic signals of multiple channels are stored separately for the control terminal to read and analyze and calculate.
控制终端，用于执行上述的分析方法，所述分析方法包括但不限于肌群协同的时-空域和频-空域分析，协同向量相似度评估，共有协同和特有协同分析，激活系数分析，频谱分析以及不同频段肌群协同相关度分析。The control terminal is used to execute the above-mentioned analysis method, which includes but is not limited to time-space domain and frequency-space domain analysis of muscle group synergy, synergy vector similarity evaluation, common synergy and unique synergy analysis, activation coefficient analysis, spectrum analysis and muscle group synergy correlation analysis in different frequency bands.
本发明还提供一种计算机程序产品，所述计算机程序产品包括存储在非暂 态计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行上述的方法。The present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the above-mentioned method.
本发明还提供一种计算机可读存储介质，其上存储有计算机指令，所述计算机指令被处理器运行时，完成上述的方法。The present invention also provides a computer-readable storage medium on which computer instructions are stored. When the computer instructions are executed by a processor, the above method is completed.
本发明用高密度肌电设备采集了100位被试者等长等张腕部动作的肌电信号，将信号进行预处理后提取肌群协同并分析相似度。同一动作不同条件下肌群协同的相似度高(CPA>＝0.85)。不同动作模式之间的肌群协同相似度降低(0.75＝<CPA<＝0.82)。两种运动模式之间存在一些共有肌群协同。共有肌群协同占所有协同的比例与相似度值基本吻合。此外，将48个通道改为24或16个通道，实验结果依然稳定。这表明，本发明解决了传统少通道肌群协同分析方法存在的一系列问题，并且该方法可靠。本发明对两种运动模式下肌群协同的时-空域和频-空域分析，发现两种运动模式下时空凸包和频控凸包均存在重叠部分，这说明两种运动模式的肌群协同存在时空和频空相似的部分，与前面肌群协同相似度的结果吻合。The present invention uses a high-density electromyographic device to collect the electromyographic signals of isometric and isotonic wrist movements of 100 subjects, extracts muscle group synergy after preprocessing the signals, and analyzes the similarity. The similarity of muscle group synergy under different conditions of the same action is high (CPA>=0.85). The similarity of muscle group synergy between different action modes is reduced (0.75=<CPA<=0.82). There are some common muscle group synergies between the two movement modes. The proportion of common muscle group synergies to all synergies is basically consistent with the similarity value. In addition, the experimental results are still stable when 48 channels are changed to 24 or 16 channels. This shows that the present invention solves a series of problems existing in the traditional few-channel muscle group synergy analysis method, and the method is reliable. The present invention analyzes the time-space domain and frequency-space domain of muscle group synergy under the two movement modes, and finds that there are overlapping parts in the time-space convex hull and frequency-controlled convex hull under the two movement modes, which shows that the muscle group synergy of the two movement modes has similar parts in time and space and frequency and space, which is consistent with the previous results of muscle group synergy similarity.
本发明的上述技术方案中，针对肌群协同从时-空域和频-空域两个角度进行分析，涉及协同向量相似度评估，共有协同和特有协同，时-空肌群协同特性分析，激活系数分析，频-空肌群协同特性分析和不同频段肌群协同相关度分析。与传统的肌肉协同分析方法相比，本发明从时-空-频不同维度进行肌群协同分析，能够更加精确地研究神经肌肉活动的细微变化，有助于探索研究运动控制反馈机制及运动障碍病理机制，建立基于肌电信号的康复状态评价指标，能够为临床康复训练提供指导意义。In the above technical scheme of the present invention, muscle group synergy is analyzed from two perspectives: time-space domain and frequency-space domain, involving synergy vector similarity evaluation, common synergy and unique synergy, time-space muscle group synergy characteristic analysis, activation coefficient analysis, frequency-space muscle group synergy characteristic analysis and different frequency band muscle group synergy correlation analysis. Compared with the traditional muscle synergy analysis method, the present invention performs muscle group synergy analysis from different dimensions of time-space-frequency, which can more accurately study the subtle changes in neuromuscular activity, help explore the motor control feedback mechanism and the pathological mechanism of movement disorders, establish a rehabilitation status evaluation index based on electromyographic signals, and provide guidance for clinical rehabilitation training.
以上仅为本发明的优选实施例，并非因此限制本发明的专利范围，凡是在本发明的发明构思下，利用本发明说明书及附图内容所作的等效结构变换，或直接/间接运用在其他相关的技术领域均包括在本发明的专利保护范围。The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. All equivalent structural changes made using the contents of the present invention's specification and drawings, or directly/indirectly applied in other related technical fields, are included in the patent protection scope of the present invention.

Claims (10)

  1. 一种肌群协同分析方法，其特征在于，包括A muscle group synergy analysis method, characterized by comprising:
    获取多通道肌电信号；Acquire multi-channel electromyographic signals;
    对多通道肌电信号进行预处理，形成多通道包络矩阵；Preprocess the multi-channel electromyographic signals to form a multi-channel envelope matrix;
    采用非负矩阵分解算法对包络矩阵进行分解，提取肌群协同；The envelope matrix is decomposed using non-negative matrix factorization algorithm to extract muscle group synergy;
    确定肌群协同个数；Determine the number of muscle group synergies;
    对肌群协同进行相似度评估。Similarity assessment of muscle group synergy.
  2. 如权利要求1所述的肌群协同分析方法，其特征在于，所述相似度评估的维度至少包括欧式距离，余弦夹角和相似指数中的一种。The muscle group synergy analysis method as described in claim 1 is characterized in that the dimension of similarity evaluation includes at least one of Euclidean distance, cosine angle and similarity index.
  3. 如权利要求1所述的肌群协同分析方法，其特征在于，还包括共有协同和特有协同分析，The muscle group synergy analysis method according to claim 1, characterized in that it also includes common synergy and unique synergy analysis,
    将每种运动模式下所有协同向量聚成k类，定义样本与其所属类的中心之间的距离的综合为损失函数，选择k个类的中心，将样本逐个指派到与其最近的中心的类中，得到一个聚类结果；然后更新每个类的样本的均值，作为类的新的中心；重复以上步骤，使损失函数极小化并达到收敛；Cluster all cooperative vectors in each motion mode into k classes, define the sum of the distances between the sample and the center of the class to which it belongs as the loss function, select the centers of k classes, assign the samples one by one to the class with the closest center, and obtain a clustering result; then update the mean of the samples in each class as the new center of the class; repeat the above steps to minimize the loss function and reach convergence;
    把含有协同向量个数少于预定个数的类剔除，剩下的聚类中心为代表协同向量，将两种运动模式下的代表协同向量进行相似度配对，相似度高于预设阈值的代表协同向量为两种运动模式之间的共有协同向量，其他的为特有协同向量。The classes with less than a predetermined number of collaborative vectors are eliminated, and the remaining cluster centers are representative collaborative vectors. The representative collaborative vectors in the two motion modes are paired according to similarity. The representative collaborative vectors with a similarity higher than a preset threshold are the common collaborative vectors between the two motion modes, and the others are unique collaborative vectors.
  4. 如权利要求1所述的肌群协同分析方法，其特征在于，还包括时-空特性分析，The muscle group synergy analysis method according to claim 1, further comprising a time-space characteristic analysis,
    构建三维数组{A，t max，ω}，其中A为激活峰值，t max为激活峰值对应的时间，ω为最优变化比率； Construct a three-dimensional array {A, t max , ω}, where A is the activation peak, t max is the time corresponding to the activation peak, and ω is the optimal change ratio;
    采用凸包算法分别求出等长运动模式下和等张运动模式下所有肌群协同对应三维数组{A，t max，ω}的三维凸包，通过分析两个三维凸包的重叠程度来判断两种运动模式下的肌群协同的时空特性的差异程度。 The convex hull algorithm is used to obtain the three-dimensional convex hulls of the three-dimensional arrays {A, t max , ω} corresponding to all muscle group synergies in the isometric and isotonic modes, and the degree of difference in the spatiotemporal characteristics of muscle group synergies in the two movement modes is determined by analyzing the overlap of the two three-dimensional convex hulls.
  5. 如权利要求1所述的肌群协同分析方法，其特征在于，还包括激活系数分析，The muscle group synergy analysis method according to claim 1, further comprising activation coefficient analysis,
    计算平均激活值；Calculate the average activation value;
    根据平均激活值确定有效激活值；Determine the effective activation value based on the average activation value;
    根据有效激活值确定有效激活时间起点、有效激活时间终点；Determine the effective activation time starting point and effective activation time ending point according to the effective activation value;
    通过分析有效激活时间起点、有效激活时间终点比较不同动作的协同向量有效激活的时间范围；By analyzing the effective activation time start point and effective activation time end point, the effective activation time range of the synergy vectors of different actions is compared;
    由激活峰值及其对应时间点t max可以比较协同向量激活的最大幅值及其出现的时间点。 The maximum amplitude of the cooperative vector activation and the time point of its occurrence can be compared by the activation peak and its corresponding time point t max .
  6. 如权利要求1所述的肌群协同分析方法，其特征在于，还包括频-空特性分析，The muscle group synergy analysis method according to claim 1, further comprising frequency-space characteristic analysis,
    对多通道肌电信号进行傅里叶变换得到频域矩阵；Perform Fourier transform on multi-channel electromyographic signals to obtain a frequency domain matrix;
    构建三维数组{S，D，β}，其中A为激活峰值，t max为激活峰值对应的时间，ω为最优变化比率； Construct a three-dimensional array {S, D, β}, where A is the activation peak, t max is the time corresponding to the activation peak, and ω is the optimal change ratio;
    采用凸包算法分别求出等长运动模式下和等张运动模式下所有肌群协同对应三维数组{S，D，β}的三维凸包，通过分析两个三维凸包的重叠程度来判断两种运动模式下的肌群协同的频空特性的差异程度。The convex hull algorithm is used to calculate the three-dimensional convex hulls of the three-dimensional arrays {S, D, β} corresponding to all muscle group synergies in the isometric and isotonic modes respectively. The degree of difference in the frequency-space characteristics of muscle group synergy in the two movement modes is determined by analyzing the overlap degree of the two three-dimensional convex hulls.
  7. 如权利要求2所述的肌群协同分析方法，其特征在于，还包括频段肌群协同相关度分析，The muscle group synergy analysis method according to claim 2, characterized in that it also includes frequency band muscle group synergy correlation analysis,
    将协同向量对应的频谱矩阵按照频率高低分为多段；The spectrum matrix corresponding to the synergy vector is divided into multiple segments according to the frequency;
    采用波谱相干对不同协同向量对应的同一频带进行相关度分析。Spectral coherence is used to perform correlation analysis on the same frequency band corresponding to different cooperative vectors.
  8. 一种肌群协同分析***，其特征在于，包括A muscle group synergy analysis system, characterized in that it includes
    肌电信号采集器，用于实时采集用户的肌电信号；The electromyographic signal collector is used to collect the user's electromyographic signals in real time;
    数据存储器，用于存储肌电信号数据；A data storage device, used for storing electromyographic signal data;
    控制终端，用于执行权利要求1-7任一所述的分析方法。A control terminal, used to execute the analysis method described in any one of claims 1-7.
  9. 一种计算机程序产品，其特征在于，所述计算机程序产品包括存储在非暂态计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行权利要求1-7任一所述的方法。A computer program product, characterized in that the computer program product comprises a computer program stored on a non-transitory computer-readable storage medium, the computer program comprises program instructions, and when the program instructions are executed by a computer, the computer executes any one of the methods described in claims 1-7.
  10. 一种计算机可读存储介质，其特征在于，其上存储有计算机指令，所述计算机指令被处理器运行时，完成权利要求1-7任一所述的方法。A computer-readable storage medium, characterized in that computer instructions are stored thereon, and when the computer instructions are executed by a processor, the method described in any one of claims 1 to 7 is performed.
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