CN112733721A - Surface electromyographic signal classification method based on capsule network - Google Patents

Surface electromyographic signal classification method based on capsule network Download PDF

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CN112733721A
CN112733721A CN202110034453.6A CN202110034453A CN112733721A CN 112733721 A CN112733721 A CN 112733721A CN 202110034453 A CN202110034453 A CN 202110034453A CN 112733721 A CN112733721 A CN 112733721A
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王万良
尤文波
赵燕伟
陈嘉诚
王铁军
钱宇彤
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Abstract

The invention discloses a surface electromyographic signal classification method based on a capsule network, which adopts a window analysis method to preprocess an original surface electromyographic signal; extracting features in the signals to form a feature sequence, and converting the feature sequence into a feature matrix by using a two-dimensional method; simply cutting and stacking the original signals to generate a two-dimensional signal matrix; convolving the two matrixes through convolution kernels with different sizes to obtain characteristic graphs with the same size and then overlapping channels; and sending the obtained abstract feature diagram into a capsule network for training, and storing the network weight. The invention improves the method for generating abstract characteristics, has high accuracy and strong robustness, and particularly aims at actions similar to muscle movement.

Description

Surface electromyographic signal classification method based on capsule network
Technical Field
The invention relates to a surface electromyographic signal classification problem.
Background
The hand plays an extremely important role in the daily life and work of human beings. In recent years, the number of elderly people with handicapped hands and feet and patients with physical disabilities has increased year by year as the degree of aging of the population has increased and various diseases, accidents, and natural disasters have occurred. The intelligent artificial limb can imitate the mode of a hand of a person, and basic help is provided for the old and the disabled in life. The hand movements of a person are governed by consciousness, which is transmitted to the whole body in the form of bioelectrical signals, and corresponding muscle groups perform corresponding movements after receiving the signals. For the control of prostheses, signal sources are also required, the surface electromyography (sEMG) being the most common at present.
The surface electromyographic signals are biological signals of the muscle movement recorded by sticking electrodes on the surface of the muscle. Different gestures correspond to different muscle movement patterns and therefore produce different signals. By analyzing the surface myoelectric signals, the gesture actions can be predicted. Meanwhile, the surface electromyogram signal also has the advantages of no wound, easy acquisition and the like, so the surface electromyogram signal is an ideal human-computer interaction signal source. The successful application of using surface electromyographic signals as a medium for human-computer interaction depends on accurate identification of the surface electromyographic signals. Therefore, the surface electromyogram signal has an application value to gesture action recognition, and the hand action recognition research based on the surface electromyogram signal has great social significance.
Most of the existing surface electromyographic signal classification methods adopt a feature extraction mode, and manually extracted features are sent to a machine learning or deep learning model for training to obtain a prediction result. There are also some studies that put raw data into deep learning models for training. However, the current method has the following problems: (1) the result of the method for training by using the characteristics depends on the selected characteristics and the combination mode to a great extent, the information of the original signal cannot be completely restored no matter how appropriate the characteristics are, the classification effect is good only for the actions with large differences, and when the action differences are small, the classification effect is not good, and the robustness of the algorithm is poor; (2) although the method for transmitting the original signal to the model for training can avoid extracting the features, the extracted abstract features are too wide due to too much information in the original signal, so the classification precision is low; (3) the machine learning or deep learning model used at present can only reflect whether certain features exist, but cannot reflect the relation between the features and the relative relationship between the features.
Disclosure of Invention
Aiming at the existing problems, the invention provides a classification method based on a capsule network and fusing original signals and extracted features, which adopts a novel deep learning model capsule network and fuses multi-level features in the network, and aims to solve the problems in the prior art and finally solve the problem of surface electromyographic signal classification with higher robustness and accuracy.
In order to achieve the purpose, the invention adopts the technical scheme that the surface electromyographic signal classification method based on the capsule network comprises the following steps:
s1: raw signal data preprocessing, i.e. window analysis. And intercepting the signals by using a window with the length of w aiming at the surface electromyographic signals. Wherein w is the window length, t is the increment interval, and τ is the time for signal processing and obtaining the prediction result. After each time interval t, the data is intercepted sequentially for all channels simultaneously.
S2: and extracting features from each window of each channel to obtain a one-dimensional multi-channel feature sequence.
S3: the signature sequence and the original signal are respectively two-dimensioned. The one-dimensional feature matrix is multiplied by the transpose matrix after being increased by "1" to obtain a two-dimensional feature matrix, which is specifically referred to as S31 to S32. Each windowed original signal is sliced and stacked to obtain a two-dimensional original signal matrix, which is described in detail in S33 to S34.
S31: to the sequence x, 1 is added as shown in equation (2) to give the sequence f.
f＝[1,x1,x2,...,xm] (1)
S32: and (4) performing feature combination on the feature vectors by using a matrix operation, and specifically converting as shown in formulas (3) and (4).
F＝G(α*(f×fT)β) (2)
Figure BDA0002893609720000021
Where α and β are transition parameters, both set to 0.5. F is the characteristic diagram obtained by conversion. G is a nonlinear activation function sigmoid for limiting the abstract eigenvalue between [0,1 ].
S33: each window with the length w is arranged according to the scale l1Is cut into n1Segment, n1Satisfying formula (4). In this experiment, set l1Is 20, n1Is 15.
n1＝w/l1 (4)
S34: n is to be1Short sequences of segments are vertically stacked to obtain1×n1Is used for the two-dimensional matrix of (1).
S4: and (3) convolving the two matrixes obtained by the two-dimensional method by convolution kernels with different sizes respectively to finally obtain two multi-channel two-dimensional matrixes with the same size. Both matrices are stacked on the channels.
S5: and sending the data to a capsule network for training, and storing the network weight.
Compared with the prior art, the invention has the following advantages:
(1) compared with the traditional feature extraction method, the method for generating the abstract features is simple, new abstract features can be generated in a network, only existing common features need to be selected, and the method for generating the new features does not need to be researched.
(2) The accuracy is high. Compared with the method of directly inputting the features, the method of fusing the original signals and the features is used, more original information can be contained in the input, and the extracted abstract features do not generate a large amount of redundant features like the method of directly inputting the original signals, so that the method is helpful for extracting more characteristic features.
(3) And the robustness is strong. The adopted capsule network model is helpful for extracting the relative relation between the features, so that the capsule network model can only identify the action with larger difference and can also accurately identify the action with smaller difference, and the accuracy rate is higher, unlike the convolutional neural network.
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FIG. 1 is an overall flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of a window analysis method of the present invention.
FIG. 3 is a diagram of a network model according to the present invention.
Fig. 4 is a dynamic routing mechanism for a capsule network.
Detailed Description
For a better understanding of the present invention, reference is made to the following detailed description taken in conjunction with the accompanying drawings and examples.
Example (c): an ELONXI myoelectricity acquisition instrument is adopted for research. The device supports a maximum of 16 bipolar channels, with a sampling resolution of 24 bits and a sampling frequency between 1000 hz and 2000 hz. In the experiment, 16 channels are selected, a sampling frequency is 1000 Hz, and a filter of the system is used for obtaining filtered signals. The data of 5 gesture actions of 1 person are collected in total, 10s of data are collected for each gesture action, and the data are collected for 4 times repeatedly. The signals collected 3 times before each action are taken as a training set, and the signals collected the last time are taken as a testing set.
S1: the raw signal data preprocessing, i.e. the window analysis method, as shown in fig. 2, intercepts the signal with a window for the surface electromyogram signal. Where w is the window length set to 300 milliseconds and t is the increment interval set to 50 milliseconds. After every 50 milliseconds, a window of 300 milliseconds length is sequentially intercepted for all channels simultaneously.
S2: and (3) extracting features from each window of each channel to obtain a one-dimensional multi-channel feature sequence x, as shown in formula (5). In this example, m features were collected, m having a value of 14, and the formula used is shown in Table 1, where x isiRepresenting the ith characteristic.
x＝[x1,x2,...,xm] (5)
TABLE 1 discrete characteristics taken
Figure BDA0002893609720000041
Figure BDA0002893609720000051
Wherein S isiIs defined as a time-varying signal value, SfFor the frequency spectrum of the signal S obtained by a fast Fourier transform, P (S)f) Is the power spectral strength of that band.
S3: the signature sequence and the original signal are respectively two-dimensioned. And operating the one-dimensional feature sequence according to S31 to S32 to obtain a two-dimensional feature matrix. And cutting and stacking the windowed original signals according to the steps from S33 to S34 to obtain a two-dimensional original signal matrix.
S31: adding 1 to the sequence x as shown in formula (1) to obtain the sequence f.
f＝[1,x1,x2,...,xm] (1)
S32: and (3) performing feature combination on the feature vectors by using a matrix operation, and specifically converting as shown in formulas (2) and (3).
F＝G(α*(f×fT)β) (2)
Figure BDA0002893609720000052
Where α and β are transition parameters, both set to 0.5. F is the characteristic diagram obtained by conversion. G is a nonlinear activation function sigmoid for limiting the abstract eigenvalue between [0,1 ].
S33: each window with the length w is arranged according to the scale l1Is cut into n1Segment, n1Satisfying formula (4). In this experiment, set l1Is 20, n1Is 15 of。
n1＝w/l1 (4)
S34: n is to be1Short sequences of segments are vertically stacked to obtain1×n1Is used for the two-dimensional matrix of (1).
S4: and (4) respectively convolving the two matrixes with convolution kernels with different sizes to finally obtain two multichannel two-dimensional matrixes with the same size. Both matrices are stacked on the channels. For the resulting two-dimensional feature matrix, a convolution kernel of 3 × 8 × 128 with a step size of 2 is used. For the resulting two-dimensional original signal matrix, a convolution kernel of 3 × 3 × 128 with a step size of 2 is used. Stacking to obtain 7 × 7 × 256 characteristic diagram S1。
S5: according to the steps from S51 to S54, the capsule network is sent to train, and the network weight is stored.
S51: checking S by a convolution of 3X 2561Convolution to obtain a characteristic map S2。
S52: will S1And S2And connecting on channels, fusing multi-level features, and reducing the number of the channels by using a convolution kernel of 1 multiplied by 256 to obtain a 7 multiplied by 256 feature map.
S53: the 7 × 7 × 256 characteristic diagram is divided into 32 groups according to 1 group of 8 channels, namely 7 × 7 × 8 × 32, namely 32 primary capsules, and each primary capsule uses UiAnd (4) showing.
S54: the action capsule layer is 5 multiplied by 16, and the parameters between the action capsule and the primary capsule are updated by a dynamic routing mechanism. The dynamic routing mechanism can be specifically described by fig. 4, the number of updates of the dynamic routing is set to be 3, the specific formulas are (6), (7), (8), (9), (10), and the loss function is formula (11).
U′j|i＝WijUi (6)
Figure BDA0002893609720000061
Figure BDA0002893609720000062
Wherein, WijIs the connection weight of the ith low-level capsule to the jth high-level capsule; u'j|iIs the prediction vector of the ith low-level capsule to the jth high-level capsule; c. CijIs a coupling coefficient determined by dynamic routing; bijLogarithmic prior probability of being the ith low-level capsule to the jth high-level capsule
Figure BDA0002893609720000063
bij＝bij+U′j|i·vj (10)
The prediction vectors of all the low-grade capsules are multiplied by the coupling coefficients correspondingly and accumulated to obtain the input s of the high-grade capsulesjThen converted into the activation value v of the advanced capsule by a non-linear 'compression' activation function (Squash function)j. This activation function both preserves the direction of the input vector and compresses the modulus of the input vector between [0, 1). Finally, updating b by using log-likelihood functionij. A larger product represents a closer orientation of the low-grade capsules to the high-grade capsules. The final prediction result can be obtained by repeating the formula for a certain number of times. In this experiment, the number of iterations was set to 3.
Figure BDA0002893609720000064
Where N is the number of all actions, which is 5 in this experiment; t isk1 means that class k is present, otherwise 0; m is+Punishment false positive, and the value is 0.9; m is-Punishment false negative, and the value is 0.1; and lambda is a weight parameter used for reducing the influence caused by the prediction error label, and the value of lambda is 0.5.
In a word, the invention provides a surface electromyographic signal classification method based on a capsule network for solving the problem of surface electromyographic signal classification. The method of fusing the original signal with the manually extracted features is used, so that the input contains richer information, and more abstract features are extracted. In addition, the capsule network can well represent the connection among the characteristics, and the information which cannot be mined by the convolutional neural network is mined, so that the recognition rate can be effectively improved.
Although the present invention has been described in connection with the accompanying drawings, the present invention is not limited to the above-described embodiments, the above-described examples and the description are only for illustrating the principle of the present invention, and the present invention may be further modified and improved without departing from the spirit and scope of the present invention, and the modifications and improvements fall within the scope of the claimed invention. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (5)

1. A surface electromyographic signal classification method based on a capsule network is characterized by comprising the following steps: comprises the following steps:
s1: preprocessing original signal data, namely a window analysis method; intercepting a signal by using a window with the length of w aiming at a surface electromyographic signal; wherein w is the window length, t is the increment interval, and τ is the time for signal processing and obtaining the prediction result; after every time interval t, sequentially intercepting data of all channels at the same time;
s2: extracting features from each window of each channel to obtain a one-dimensional multichannel feature sequence x, x ═ x1,x2,...,xm]M represents the number of features;
s3: respectively bidimensionalizing the characteristic sequence and the original signal; adding 1 to the one-dimensional feature matrix and then multiplying the one-dimensional feature matrix by the transpose matrix to obtain a two-dimensional feature matrix, which is specifically seen from S31 to S32; cutting and stacking each windowed original signal to obtain a two-dimensional original signal matrix, specifically seen in S33 to S34;
s31: adding 1 into the sequence x, and obtaining a sequence f as shown in a formula (2);
f＝[1,x1,x2,...,xm] (1)
s32: performing feature combination on the feature vectors by using matrix operation, wherein the specific conversion is shown as formulas (3) and (4);
F＝G(α*(f×fT)β) (2)
Figure FDA0002893609710000011
wherein, alpha and beta are conversion parameters which are set to be 0.5; f is a feature map obtained by conversion; g is a nonlinear activation function sigmoid and is used for limiting the abstract characteristic value to be between [0,1 ];
s33: each window with the length w is arranged according to the scale l1Is cut into n1Segment, n1Satisfies formula (4); in this experiment, set l1Is 20, n1Is 15;
n1＝w/l1 (4)
s34: n is to be1Short sequences of segments are vertically stacked to obtain1×n1A two-dimensional matrix of (a);
s4: performing convolution on two matrixes obtained by a two-dimensional method by convolution kernels with different sizes respectively to finally obtain two multi-channel two-dimensional matrixes with the same size; stacking the two matrixes on the channel;
s5: and sending the data to a capsule network for training, and storing the network weight.
2. The method for classifying surface electromyographic signals based on a capsule network according to claim 1, wherein: the features extracted in step 2 are shown in the following table:
Figure FDA0002893609710000021
wherein S isiIs defined as a time-varying signal value, SfFor the frequency spectrum of the signal S obtained by a fast Fourier transform, P (S)f) Is the power spectral strength of that band.
3. The method for classifying surface electromyographic signals based on a capsule network according to claim 1, wherein: the stacking method comprises the following specific steps: for the obtained two-dimensional feature matrix, a convolution kernel with the length of 3 multiplied by 8 multiplied by 128 and the step length of 2 is adopted; for the obtained two-dimensional original signal matrix, a convolution kernel with the length of 3 multiplied by 128 and the step length of 2 is adopted; stacking to obtain 7 × 7 × 256 characteristic diagram S1。
4. The method for classifying surface electromyographic signals based on a capsule network according to claim 1, wherein: the step 5 specifically comprises the following steps:
s51: checking S by a convolution of 3X 2561Convolution to obtain a characteristic map S2；
S52: will S1And S2Connecting channels, fusing multi-level features, and reducing the number of the channels by using 1 × 1 × 256 convolution kernels to obtain a 7 × 7 × 256 feature map;
s53: the 7 × 7 × 256 characteristic diagram is divided into 32 groups according to 1 group of 8 channels, namely 7 × 7 × 8 × 32, namely 32 primary capsules, and each primary capsule uses UiRepresents;
s54: the action capsule layer is 5 multiplied by 16, and the parameters between the action capsule and the primary capsule are updated by a dynamic routing mechanism; the dynamic routing mechanism can be specifically described by fig. 4, the number of updates of the dynamic routing is set to be 3, the specific formulas are (5), (6), (7), (8), (9), and the loss function is formula (10);
U′j|i＝WijUi (5)
Figure FDA0002893609710000031
Figure FDA0002893609710000032
wherein, WijIs the connection right of the ith low-level capsule and the jth high-level capsuleWeighing; u'j|iIs the prediction vector of the ith low-level capsule to the jth high-level capsule; c. CijIs a coupling coefficient determined by dynamic routing; bijIs the log prior probability of the ith low-grade capsule to the jth high-grade capsule;
Figure FDA0002893609710000033
bij＝bij+U′j|i·vj (9)
the prediction vectors of all the low-grade capsules are multiplied by the coupling coefficients correspondingly and accumulated to obtain the input s of the high-grade capsulesjThen converted into the activation value v of the advanced capsule by a non-linear 'compression' activation function (Squash function)j(ii) a This activation function both preserves the direction of the input vector and compresses the modulus of the input vector between [0, 1); finally, updating b by using log-likelihood functionij(ii) a The larger the product is, the closer the direction of the low-grade capsule and the high-grade capsule is; repeating the formula for a certain number of times to obtain the final prediction result; in this experiment, the number of iterations was set to 3;
Figure FDA0002893609710000041
where N is the number of all actions; t isk1 means that class k is present, otherwise 0; m is+Is a punishing false positive; m is-Is a punishing false negative; λ is a weight parameter for reducing the impact of prediction error labeling.
5. A surface electromyographic signal classification method based on a capsule network is characterized by comprising the following steps: in step S54, N is 5; m is+The value is 0.9; m is-The value is 0.1; lambda takes the value 0.5.
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