Data analysis method combining muscle synergy analysis and muscle function network

Abstract

The application relates to the technical field of data analysis, and discloses a data analysis method combining muscle synergy analysis and muscle function network, which comprises the following steps: step S1: collecting surface electromyogram signals of each muscle block during movement; step S2: muscle synergy mode analysis, integrating the collected surface electromyogram signal data by using non-negative matrix decomposition, extracting a muscle synergy mode, and quantifying the quality of a reconstruction matrix by using a variance explanation rate; and step S3: muscle function network analysis, obtaining normalized mutual information between each muscle channel as a nonlinear correlation characteristic parameter of multi-channel surface electromyogram, and constructing a muscle function network. The data analysis method of the application combines muscle synergy analysis and muscle function network based on surface electromyogram signals, uses non-negative matrix decomposition for muscle synergy analysis, quantifies the mutual relationship between channels by using normalized mutual information, and constructs a muscle function network, so that potential and valuable structured and modularized information in electromyogram data can be found.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a data analysis method that combines muscle synergy analysis and muscle function networks. Background Technology

[0002] Electromyography (EMG) signals are important biomechanical information generated when muscles are stimulated, directly reflecting muscle electrical activity and providing a direct indicator of human movement intention. Among various EMG techniques, surface electromyography (sEMG) has become an important tool for research and application of motor nervous system function due to its non-invasive measurement, simple operation, and easy signal acquisition. sEMG signals are generated by placing electrode pads on the skin surface to record changes in skin potential difference during muscle contraction. Through signal processing steps such as amplification, filtering, and rectification, signals suitable for further analysis are produced. These signals are widely used in gesture recognition, prosthetic control, rehabilitation equipment design, muscle strength assessment, and remote-controlled robots, demonstrating their core role in modern sports science and engineering applications.

[0003] Although sEMG signals have wide applications in motion analysis, traditional analysis methods typically focus only on the electromyographic activity of a single muscle or under single movement conditions, especially changes in absolute force. This limited approach ignores the complex synergistic relationships between muscle groups and cannot fully assess the interactions and modular synergistic structures between multiple muscles during movement. This is extremely detrimental to understanding the holistic nature and coordination of movement, as well as optimizing movement efficiency. Summary of the Invention

[0004] The purpose of this invention is to solve the problems existing in the prior art by proposing a data analysis method that combines muscle synergy analysis and muscle function network.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] The data analysis method combining muscle synergy analysis and muscle function networks includes the following steps:

[0007] Step S1: Collect surface electromyographic signals of each muscle during movement;

[0008] Step S2: Muscle synergy pattern analysis. Non-negative matrix factorization is used to integrate the collected surface electromyography signal data, extract muscle synergy patterns, and use variance explanation rate to quantify the quality of the reconstructed matrix.

[0009] Step S3: Repeat steps S1-S2. For each repeated experiment, calculate the cosine similarity between the extracted muscle synergy patterns. Sort each experiment according to the cosine similarity between repeated experiments and calculate the average synergy pattern of the repeated experiments after sorting.

[0010] Step S4: Calculate the cosine similarity between the average cooperating pattern and the cooperating patterns of each repeated experiment to ensure that the average cooperating pattern can represent each repeated experiment.

[0011] Step S5: Analyze the correlation characteristics between electromyographic channels using normalized mutual information to construct a muscle function network and obtain a normalized mutual information matrix;

[0012] Step S6: Set a threshold on the normalized mutual information matrix to construct a weighted undirected network graph;

[0013] Step S7: Calculate the features of each node in the weighted undirected network to evaluate node importance, clustering coefficients to evaluate network connectivity, analyze network features under different motion conditions, and draw the network graph.

[0014] Preferably, step S1 further includes preprocessing the collected surface electromyography signals, wherein the preprocessing process includes removing motion artifacts, filtering and denoising, rectification, normalization and taking the linear envelope, and then the processed data is classified and stored according to the motion type.

[0015] Preferably, in step S2, the quality of the reconstructed matrix after nonnegative matrix decomposition is quantified by the variance explanation rate, so that the product of the two matrices approximates the original matrix, where the rank of one of the matrices is k, and the value of k is iterated until the variance explanation rate is greater than 90%, that is, the reconstructed matrix contains more than 90% of the information of the original matrix.

[0016] Preferably, in step S3, the experiments are sorted according to the cosine similarity between the repeated experiments, and the average cooperative pattern of the repeated experiments after sorting is calculated. This specifically includes the following steps:

[0017] Step S31: Calculate the cosine similarity matrix between multiple matrices in the repeated experiments, and extract the similarity results of each pair of matrices from it;

[0018] Step S32: Align the cooperative patterns based on the principle of maximum similarity, and rearrange the cooperative pattern matrix according to the alignment order;

[0019] Step S33: Calculate the cosine similarity between the rearranged collaborative pattern matrices and take the mean of the diagonal elements of the similarity matrix to quantify the collaborative consistency between the matrices;

[0020] Step S34: Calculate the average value of the rearranged cooperative pattern matrices for further analysis and comparison.

[0021] Preferably, in step S6, the specific process of setting the threshold is as follows: a series of thresholds are generated, and the average degree of the adjacency matrix under each threshold is calculated, thereby obtaining the relationship curve between the threshold and the average degree. This can help determine the connectivity and sparsity of the network under different connection strengths, and is suitable for analyzing the changes in the key characteristics of the network under different thresholds. Based on the changes in the key characteristics of the network under different thresholds, a suitable threshold is determined to ensure that the network has the expected average degree.

[0022] Preferably, in step S7, the node feature is degree, and the network feature is clustering coefficient, which are used to evaluate the importance of each node in the network and the network connectivity, so as to identify the key muscles in the action process and evaluate the overall coordination and connectivity of each muscle group under the action condition.

[0023] Preferably, step S5, which analyzes the correlation characteristics between electromyographic channels using normalized mutual information, further includes the following steps:

[0024] Step S51: Perform standard checks and preprocessing on the input data;

[0025] Step S52: Calculate the joint distribution matrix of the signals based on the defined histogram edges;

[0026] Step S53: Calculate the marginal probability distribution of the signal and the corresponding entropy value;

[0027] Step S54: Calculate the mutual information between signals by accumulating the information in the joint probability distribution, and normalize the mutual information;

[0028] Step S55: Obtain the mutual information matrix of each repeated experiment and take the mean matrix.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] This invention presents a data analysis method for muscle synergy analysis and muscle function networks based on surface electromyography (EMG) signals. It utilizes non-negative matrix factorization for muscle synergy analysis, employs normalized mutual information to quantify the relationships between muscle channels, and constructs a muscle function network. This method can uncover potentially valuable structured and modular information concentrated in EMG data, highlighting the holistic and coordinated nature of muscle groups in motion analysis. It not only helps improve the accuracy and efficiency of motion analysis but also optimizes movement patterns and improves motor performance, demonstrating broad application prospects. It provides new perspectives and tools for research in rehabilitation medicine, sports science, and neuroengineering, while also offering theoretical support and a technical foundation for practical applications such as prosthetic control and the design of intelligent rehabilitation equipment. Attached Figure Description

[0031] Figure 1This is a flowchart of the data analysis method combining muscle synergy analysis and muscle function network proposed in this invention. Detailed Implementation

[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0033] Reference Figure 1 This application provides a data processing method for muscle synergy analysis and muscle function network based on surface electromyography (EMG) signals. It employs non-negative matrix factorization to conduct in-depth analysis of muscle synergy and utilizes normalized mutual information to quantify the relationships between different muscle channels, thereby constructing a muscle function network. This method can reveal the potential structured and modular information in EMG data and emphasizes the importance of muscle group integrity and coordination in motion analysis, helping to discover deeper patterns in muscle synergy.

[0034] Step S1: Collect surface electromyographic signals of each muscle during movement;

[0035] Step S2: Muscle synergy pattern analysis. Non-negative matrix factorization is used to integrate the collected surface electromyography signal data, extract muscle synergy patterns, and use variance explanation rate to quantify the quality of the reconstructed matrix.

[0036] This step introduces coordinating modules, as these are the smallest functional units in which the central nervous system controls the musculoskeletal system to complete various complex movements. Muscle synergy theory, by analyzing the synergistic effects of multiple muscles contracting simultaneously, can identify key muscle groups—the coordinators—that perform specific motor tasks. Research shows that human movement can be modeled through the synergy of a few muscles with specific biomechanical functions. These coordinators construct the basic modules of movement, simplifying the complex control of multiple muscles by the nervous system.

[0037] Meanwhile, in muscle synergy analysis, non-negative matrix factorization (NMF) and principal component analysis (PCA) are commonly used dimensionality reduction methods. NMF decomposes a high-dimensional electromyographic (EMG) signal matrix into a set of non-negative basis matrices and coefficient matrices, thereby extracting feature patterns representing different synergistic effects. These patterns can reflect the contribution of each muscle in a specific action task. Furthermore, PCA, as another common data dimensionality reduction method, simplifies data complexity by extracting principal components of the signal and reveals the most representative variation patterns in the data. However, since the principal components of PCA may contain negative values, they are difficult to directly interpret as actual muscle activity patterns. The matrix factors obtained from NMF decomposition have a non-negativity constraint, making the resulting matrix more meaningful for actual EMG physiology. At the same time, NMF decomposition often produces sparse results, meaning that each gene pattern is mainly contributed by a few muscles. This sparsity is closer to the actual physiological situation, where in certain actions, usually only a small number of muscles have a major contribution. Therefore, non-negative matrix factorization is more suitable for dimensionality reduction analysis of EMG data.

[0038] Mutual information, as a nonlinear correlation measure, does not rely on a pre-defined model and can accurately assess linear or nonlinear dependencies between two or more random variables. Mutual information has been widely used in electroencephalography (EEG), primarily for analyzing associations, information transmission, and the construction of functional networks between different brain regions. In recent years, it has been increasingly adopted in muscle functional network analysis. Compared to the traditional Pearson correlation coefficient, mutual information has wider applicability and stronger robustness, thus finding increasing application in the analysis of complex biological signals. Through mutual information analysis, complex nonlinear relationships between different muscles can be revealed, and functional networks reflecting muscle correlations can be constructed.

[0039] Step S3: Repeat steps S1-S2. For each repeated experiment, calculate the cosine similarity between the extracted muscle synergy patterns. Sort each experiment according to the cosine similarity between repeated experiments and calculate the average synergy pattern of the repeated experiments after sorting.

[0040] Step S4: Calculate the cosine similarity between the average cooperating pattern and the cooperating patterns of each repeated experiment to ensure that the average cooperating pattern can represent each repeated experiment.

[0041] Step S5: Analyze the correlation characteristics between electromyographic channels using normalized mutual information to construct a muscle function network and obtain a normalized mutual information matrix;

[0042] Step S6: Set a threshold on the normalized mutual information matrix to construct a weighted undirected network graph;

[0043] Step S7: Calculate the features of each node in the weighted undirected network to evaluate node importance, clustering coefficients to evaluate network connectivity, analyze network features under different motion conditions, and draw the network graph.

[0044] In this embodiment, step S1 further includes:

[0045] Preprocessing of surface electromyography (EMG) signals: First, EMG signals of each muscle during movement are collected and preprocessed, including motion artifact removal, filtering and noise reduction, rectification, normalization, and linear envelope extraction. Then, the processed data are categorized and stored according to movement type. Specifically, a fourth-order Butterworth high-pass filter with a cutoff frequency of 20Hz is used to remove motion artifacts, and the absolute value of the EMG signal is taken. The maximum value during the movement is then taken for further normalization. Finally, the EMG data envelope is extracted for collaborative feature extraction or functional connectivity calculation.

[0046] In step S2, nonnegative matrix factorization is used to integrate the electromyography data, and the variance explained rate is used to quantify the quality of the reconstructed matrix. Specifically:

[0047] This code decomposes a nonnegative matrix H into two nonnegative matrices W and H. First, the input matrix V is checked to ensure that all elements are nonnegative and that the sum of each row is not zero. Then, matrices W and H are initialized with random nonnegative values, and an iterative optimization algorithm is used to update the values ​​of W and H so that matrix W×H approximates V. During iteration, the squared error (i.e., the loss function) between V and W×H is minimized according to an update rule, and the error value is recorded for each iteration until the maximum number of iterations is reached. The formula for the above calculation can be expressed as:

[0048] Given a nonnegative matrix V, the objective of NMF is to find two nonnegative matrices W and H such that:

[0049] (1)

[0050] In the formula: V is the original matrix, W is the characteristic matrix, and H is the coefficient matrix.

[0051] The formula for updating matrix W is:

[0052] (2)

[0053] In the formula: ⊙ indicates that the elements are multiplied one by one, H T Let H be the transpose of H.

[0054] The formula for updating matrix H is:

[0055] (3)

[0056] In the formula: ⊙ indicates that the elements are multiplied one by one, W T Let W be the transpose of W.

[0057] The loss function is calculated as the sum of squared Euclidean distances between matrix V and its approximate matrix WH:

[0058] (4)

[0059] The impact of the number of features decomposed on signal reconstruction accuracy is evaluated. The code first segments the signal data into different parts (left-right, top-bottom), applies NMF decomposition to each part, and calculates the total variance and reconstruction error, then calculates the variance explained (VAF), i.e., the accuracy of signal reconstruction. Finally, NMF decomposition is performed on the entire signal data, and the VAF is calculated in the same way to quantify the overall signal reconstruction quality. The formula for the above calculations can be expressed as:

[0060] Calculation of total variance:

[0061] (5)

[0062] In the formula, emg_envelope represents the envelope of electromyographic signals.

[0063] Calculation of reconstruction error (reconstruction_error):

[0064] (6)

[0065] Substituting equations (5) and (6) into equation (7) yields the variance explained (vaf):

[0066] (7)

[0067] In step S3, the experiments are sorted according to the cosine similarity between the cooperative patterns of repeated experiments, and the average cooperative pattern of the repeated experiments after sorting is calculated. This includes the following steps:

[0068] Taking a three-repeated experiment as an example:

[0069] Step S31: Calculate the cosine similarity matrix between the three cooperative pattern matrices and extract the similarity results of each pair of matrices.

[0070] The formula for calculating cosine similarity is:

[0071] (8)

[0072] In the formula, 'i' and 'j' are the indices of the column vectors in matrix1 and matrix2, respectively. For each column vector vec1 from matrix1 and column vector vec2 from matrix2, their cosine similarity is calculated.

[0073] Step S32: Align the cooperative patterns based on the principle of maximum similarity, and rearrange the second and third matrices according to the alignment order.

[0074] Step S33: Calculate the cosine similarity between the rearranged collaborative pattern matrices and take the mean of the diagonal elements of the similarity matrix to quantify the collaborative consistency between the matrices.

[0075] Step S34: Calculate the average value of the three aligned cooperative pattern matrices for further analysis and comparison.

[0076] In step S5, the normalized mutual information analysis algorithm includes:

[0077] This code combines histogram methods, joint probability distributions, entropy, and mutual information calculations. First, it calculates the joint distribution matrix of two input data using histograms, then normalizes it to obtain the joint probability distribution. Next, it calculates the marginal entropy of each input and the mutual information between them, finally normalizing the mutual information to obtain the normalized mutual information (NMI), used to quantify the interdependence between the two variables. First, the histogram method (histcounts2) is used to calculate the joint distribution matrix of the two input vectors, dividing the data into specified bins and counting frequencies. Then, this matrix is ​​normalized to calculate the joint probability distribution. Next, the marginal entropy (H(X) and H(Y)) of the two input vectors is calculated using information theory formulas, representing the individual uncertainty of each variable. Then, the mutual information (MI) of these two variables is calculated to measure their dependence. Finally, the mutual information is divided by the smaller marginal entropy to obtain the normalized mutual information (NMI), used to quantify the interdependence and degree of dependence between the two variables. The formulas for the above calculations are as follows:

[0078] Calculate the joint distribution matrix (Cxy), which is a histogram calculation based on two-dimensional data (x,y) under given boundaries eX and eY:

[0079] (9)

[0080] In the formula, eX is the boundary of the edge histogram of x, eY is the boundary of the edge histogram of y, Cxy(i,j) is the frequency corresponding to the i-th row and j-th column, and represents the number of samples of x in the i-th interval and y in the j-th interval.

[0081] Calculate the marginal probability distributions, specifically the marginal probability distributions px and py for x and y:

[0082] (10)

[0083] (11)

[0084] To calculate entropy, substitute equations (10) and (11) into equations (12) and (13) respectively to calculate the entropy H(X) and H(Y) of x and y:

[0085] (12)

[0086] (13)

[0087] Where ϵ is a very small constant used to prevent the logarithm from being zero (i.e. to prevent undefined cases from occurring in the calculation).

[0088] Substitute equations (10) and (11) into equation (14) to calculate the mutual information MI(X;Y) of x and y:

[0089] (14)

[0090] Substituting equations (14), (12), and (13) into equation (15) yields NMI(X;Y):

[0091] (15)

[0092] In step S6, the network topology is analyzed by systematically adjusting thresholds and calculating the corresponding average degree. Specifically, it generates a series of thresholds and calculates the average degree of the adjacency matrix under each threshold, thus plotting the relationship curve between the threshold and the average degree. This helps determine the connectivity and sparsity of the network under different connection strengths, and is particularly suitable for analyzing changes in key network characteristics under different thresholds, such as determining appropriate thresholds to ensure the network has the desired average degree. When calculating the average degree of each node, it iterates through each threshold, compares the elements in the matrix with the current threshold, generates a binary matrix, calculates the degree of each row (corresponding to each node), and takes the average as the average degree of the node under that threshold.

[0093] The formula for the above calculation is as follows:

[0094] Calculate point degree:

[0095] (16)

[0096] In the formula, the point degree k iThis represents the number of edges associated with node i.

[0097] Substitute equation (16) into equation (17) to calculate the average degree of all nodes, and record it as the average degree of the entire network:

[0098] (17)

[0099] In step S7, the importance of each node and the network connectivity are evaluated using vertex degree and clustering coefficient, respectively, to identify key nodes in the process and evaluate the overall network coordination and connectivity under the given conditions. The calculation of vertex degree is detailed in step S6. The clustering coefficient is calculated as the average clustering coefficient of the undirected binary graph. By traversing each node, identifying its neighboring nodes, and calculating the actual and potential edges between neighboring nodes, the clustering coefficient of each node is calculated. Finally, by averaging the clustering coefficients of all nodes, the average clustering coefficient of the entire graph is obtained, quantifying the local density of nodes in the graph, and ultimately drawing the network graph.

[0100] Step S7 is as follows:

[0101] Step S71: Threshold the adjacency matrix: Set the elements in the adjacency matrix avg_mi_matrix that are less than the specified threshold to zero, remove connections below the threshold, and set the diagonal elements to zero to exclude self-connections;

[0102] Step S72: Construct the network graph: Create an undirected graph G using the processed adjacency matrix;

[0103] Step S73: Visualize the network graph using the force layout algorithm. Set the color according to the node degree (number of connections). The higher the degree, the darker the color. Set the size of the node according to the node degree. Set the width and color of the edge according to the weight of the edge. The weight of the edge is the mutual information correlation index between two nodes. The edge width and color are related to the weight through a linear mapping.

[0104] Step S74: Calculate the degree of each node in the network. The degree is calculated as shown in Equation (16) to measure its importance in the network.

[0105] Step S75: Calculate the average clustering coefficient of the entire network to assess the local density of nodes in the network, observe the network connectivity under this action condition, and divide the muscle group modules by local network.

[0106] The formula for the above calculation is as follows:

[0107] Calculate the clustering coefficient (c) of a single node i ):

[0108] (18)

[0109] In the formula, E i k is the proportion of connections between the neighboring nodes of node i. i (k i -1) / 2 is E in an undirected network i The maximum value.

[0110] Substituting the result of equation (18) into equation (19), we can calculate the network clustering coefficient (c):

[0111] (19)

[0112] In the formula, n is the number of nodes.

[0113] Therefore, this invention provides a data processing method for muscle synergy analysis and muscle function network based on surface electromyography (EMG) signals. It uses nonnegative matrix factorization to conduct in-depth analysis of muscle synergy and normalized mutual information to quantify the relationships between different muscle channels, thereby constructing a muscle function network. This method can reveal the potential structured and modular information in EMG data and emphasizes the importance of muscle group integrity and coordination in motion analysis, helping to discover the deeper patterns of muscle synergy.

[0114] This invention provides a data processing method for muscle synergy analysis and muscle function network based on surface electromyography (EMG) signals. Combining muscle synergy analysis and muscle function network analysis, it can comprehensively quantify the key muscles and interactions between various muscle channels under a given movement condition, providing a deeper understanding of the overall characteristics of the movement. This method can be applied not only to the analysis of simple movements such as walking, standing, and sitting, but also to running, dance movements, and qigong movements, exploring synergy patterns and improving exercise efficiency. Simultaneously, in the field of rehabilitation medicine, it is helpful in exploring the therapeutic mechanisms of certain exercise therapies, interpreting the therapeutic effects of these therapies on patients from a biomechanical perspective, simplifying and optimizing the therapy, and analyzing the muscle coordination patterns of patients during daily activities based on the method provided by this invention, in order to conduct personalized training and treatment.

[0115] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A data analysis method combining muscle synergy analysis and muscle function networks, characterized by: Includes the following steps: Step S1: Collect surface electromyographic signals of each muscle during movement; Step S2: Muscle synergy pattern analysis. Non-negative matrix factorization is used to integrate the collected surface electromyography signal data, extract muscle synergy patterns, and use variance explanation rate to quantify the quality of the reconstructed matrix. Step S3: Repeat steps S1-S2. For each repeated experiment, calculate the cosine similarity between the extracted muscle synergy patterns. Sort each experiment according to the cosine similarity between repeated experiments and calculate the average synergy pattern of the repeated experiments after sorting. Step S4: Calculate the cosine similarity between the average cooperating pattern and the cooperating patterns of each repeated experiment to ensure that the average cooperating pattern can represent each repeated experiment. Step S5: Analyze the correlation characteristics between electromyographic channels using normalized mutual information to obtain a normalized mutual information matrix, thereby constructing a muscle function network; Step S6: Set a threshold on the normalized mutual information matrix to construct a weighted undirected network graph; Step S7: Calculate the features of each node in the weighted undirected network to evaluate node importance, clustering coefficients to evaluate network connectivity, analyze network features under different motion conditions, and draw the network graph; Step S1 further includes preprocessing the collected surface electromyography signals. The preprocessing process includes removing motion artifacts, filtering and denoising, rectification, normalization and taking the linear envelope. Then, the processed data is classified and stored according to the type of motion. In step S2, the variance explanation rate is used to quantify the quality of the reconstructed matrix after non-negative matrix decomposition, so that the product of the two matrices approximates the original matrix, where the rank of one of the matrices is k, and the value of k is iterated until the variance explanation rate is greater than 90%, that is, the reconstructed matrix contains more than 90% of the information of the original matrix. In step S3, the experiments are sorted according to the cosine similarity between the repeated experiments, and the average cooperative pattern of the repeated experiments after sorting is calculated. This specifically includes the following steps: Step S31: Calculate the cosine similarity between multiple matrices in repeated experiments, and extract the similarity results for each pair of matrices. Step S32: Align the cooperative patterns based on the principle of maximum similarity, and rearrange the cooperative pattern matrix according to the alignment order; Step S33: Calculate the cosine similarity between the rearranged collaborative pattern matrices and take the mean of the diagonal elements of the similarity matrix to quantify the collaborative consistency between the matrices; Step S34: Calculate the average value of the rearranged cooperative pattern matrices for further analysis and comparison.

2. The data analysis method combining muscle synergy analysis and muscle function network according to claim 1, characterized in that: In step S6, the specific process of setting the threshold is as follows: a series of thresholds are generated, and the average degree of the adjacency matrix under each threshold is calculated, thereby obtaining the relationship curve between the threshold and the average degree. This can help determine the connectivity and sparsity of the network under different connection strengths, and is suitable for analyzing the changes in the key characteristics of the network under different thresholds. Based on the changes in the key characteristics of the network under different thresholds, a suitable threshold is determined to ensure that the network has the expected average degree.

3. The data analysis method combining muscle synergy analysis and muscle function network according to claim 1, characterized in that: In step S7, the node feature is the degree, and the network feature is the clustering coefficient, which are used to evaluate the importance of each node in the network and the network connectivity, in order to identify the key muscles in the action process and evaluate the overall coordination and connectivity of each muscle group under the action condition.

4. The data analysis method combining muscle synergy analysis and muscle function network according to claim 1, characterized in that: In step S5, the correlation characteristics between electromyographic channels are analyzed by normalized mutual information, and the following steps are also included: Step S51: Perform standard checks and preprocessing on the input data; Step S52: Calculate the joint distribution matrix of the signals based on the defined histogram edges; Step S53: Calculate the marginal probability distribution of the signal and the corresponding entropy value; Step S54: Calculate the mutual information between signals by accumulating the information in the joint probability distribution, and normalize the mutual information; Step S55: Obtain the mutual information matrix of each repeated experiment and take the mean matrix.

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