CN109598219B - Adaptive electrode registration method for robust electromyography control - Google Patents
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Abstract
The invention discloses a self-adaptive electrode registration method for robust electromyography control, which is based on a convolutional neural network structure and is used for completing the matching of local areas between training data (before electrode offset) and testing data (after electrode offset), thereby realizing the self-alignment or self-calibration of an electrode array; the invention uses a complete unsupervised learning mode, does not need any additional calibration data, and directly finishes the self-calibration process of the electrode in the test stage. On the basis, the data of the recording areas which are overlapped before and after the array deviation are identified, and the high accuracy of the motion pattern identification can be ensured.
Description
Technical Field
The invention relates to the field of biological signal processing, in particular to a self-adaptive electrode registration method for robust electromyography control.
Background
The electromyographic signals are bioelectric signals accompanying muscle contraction, which contain abundant movement control information and can be used for capturing movement or movement intentions and analyzing the movement or movement intentions into control instructions in a human-computer interface. Surface electromyographic signals are collected from the skin surface and are widely used in electromyographic control because of their advantage of being non-invasive. In particular, the electromyography control technology can be applied to devices such as artificial limbs, exoskeleton robots and the like. Surface electromyogram pattern recognition is a milestone technology in the field of electromyogram control, and can realize control of multiple degrees of freedom by identifying various muscle movement patterns through a training classifier. Although this technique ideally has a high action classification rate in a laboratory, when the measurement electrode is shifted due to re-wearing in actual use, the performance of the classifier trained based on the data before shifting is greatly reduced, or even the classifier is not suitable any more, and the re-training of the classifier brings a great use load. This problem is one of the difficulties that prevent the widespread application of electromyographic pattern recognition control technology.
In previous work, attempts were made to extend all possible offset position data to the training set to enhance the robustness of the recognizer, to increase the electrode spacing to make the classifier insensitive to the electrode position, and to adaptively change the classifier with a small number of samples to fit a new feature space, but the above studies either still accompanied a severe retraining burden or had difficulty achieving the desired recognition effect. In recent years, with the widespread use of high-density array electrodes, some researchers have utilized two-dimensional spatial information in high-density array signals to address the effects of electrode offset. However, these works only use high density data to simulate electrode deflection to give quantitative analysis, and the electrode was not re-worn in the experiment, which is still significantly different from the actual application.
Disclosure of Invention
The invention aims to provide a self-adaptive electrode registration method for robust electromyography control, which greatly improves the identification accuracy of action tasks after electrode deflection when an electrode is worn again in practical application by designing a model structure based on a convolutional neural network and a unique self-calibration method.
The purpose of the invention is realized by the following technical scheme:
an adaptive electrode registration method for robust electromyographic control, comprising:
constructing training data by using electromyographic signals collected by currently worn electrode equipment;
wearing electrode equipment again, enabling the placement position of the electrode to be consistent with that of the electrode as much as possible, and constructing test data by utilizing the collected myoelectric signals;
a model structure based on a convolutional neural network is constructed in advance to serve as a classifier, the network is trained by utilizing training data to obtain optimal network model parameters, and a classifier model is obtained by combining the optimal network model parameters and the training data;
constructing a template by using training samples in training data, respectively matching a certain number of test samples in the test data and outputting a classification result by using a classifier, thereby initializing an electrode offset calibration matrix at the current stage;
in a later stage, the electrode positions of other test samples in the test data are registered by using the electrode offset calibration matrix, a classification result is given by a classifier, and meanwhile, the electrode offset calibration matrix is updated by using other test samples in the test data in an iterative manner.
According to the technical scheme provided by the invention, the matching of the local area between the training data (before electrode offset) and the test data (after electrode offset) is completed based on the convolutional neural network structure, so that the self-alignment or self-calibration of the electrode array is realized; the invention uses a complete unsupervised learning mode, does not need any additional calibration data, and directly finishes the self-calibration process of the electrode in the test stage. On the basis, the data of the recording areas which are overlapped before and after the array deviation are identified, and the high accuracy of the motion pattern identification can be ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an adaptive electrode registration method for robust electromyography control according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a myoelectricity collecting device and an electrode placement position provided by an embodiment of the present invention;
fig. 3 is a diagram of a neural network structure according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides an adaptive electrode registration method for robust electromyography control, which comprises the following steps of:
1. and constructing training data by using the electromyographic signals collected by the currently worn electrode equipment.
1) The electrode equipment selects a flexible high-density electrode array with p row channels, q column channels and D density. As an example, it may be provided that: p is 10, q is 10, and D is 7 mm.
2) The electrode equipment is placed at a position to be measured, continuous electromyographic signals when K action tasks are executed are collected one by one, and then the electromyographic signals in a section of resting state are collected.
For example, the forearm extensor muscle group may be selected as the acquisition object, and at this time, K is set to 6, that is, 6 gesture motions are acquired: the index finger is stretched, the middle finger is stretched, the little finger is stretched, the index finger and the middle finger are stretched together, and the last three fingers are stretched together and stretched into the wrist. Each action was taken 5 times, each time with moderate force isometric contraction held for 5 seconds, and the subjects were given sufficient rest time between exercise sessions. Myoelectric signals in a resting state of 5 seconds can be collected.
3) And segmenting the continuous electromyographic signals by using the electromyographic signals in the rest state to obtain a series of sample data.
Dividing the continuous electromyographic signal stream into a certain number of analysis windows by using a sliding window technology, wherein the window length is W (for example, W is 512ms), and the sliding increment is L (for example, L is 128 ms); selecting an analysis window formed by electromyographic signal flows in a resting state, and calculating a resting state threshold as Th; and judging whether all analysis windows are active segments of muscle contraction or not by utilizing the resting state threshold Th, and if so, marking a corresponding action task label and taking the label as sample data.
For example, the resting state threshold Th may be obtained by adding three times of standard deviation to the average value of all the channel electromyogram signals in the sample in the resting state.
4) And performing multi-feature extraction on the electromyographic signals of each channel in each sample data, thereby constructing each sample into a preliminary two-dimensional electromyographic feature image.
The extracted multiple features comprise time domain features including waveform length WL, and f in the time-dependent power spectrum description features 1 And f 6 Characteristic; and then, constructing each sample into a preliminary two-dimensional electromyographic feature image, wherein the length is p, the width is q, and the feature number and the color channel dimension are the same.
5) And (3) independently extracting the median subregion of the preliminary two-dimensional electromyographic feature image, interpolating by S times by using an interpolation method, and adding the interpolation result to the training data.
In the embodiment of the invention, the length of the median subregion, namely the middle part, is p 0 Width of q 0 And (3) corresponding areas (defined as core recording areas) are interpolated by S times in a manner similar to the step 5), and then training data are added, wherein the length of the electromyographic feature image of the interpolated core recording area is M, and the width of the electromyographic feature image of the interpolated core recording area is N.
As an example, it may be provided that: p is a radical of formula 0 ＝8，q 0 ＝8，M＝224，N＝224。
For example, the interpolation method may be bicubic interpolation.
2. And re-wearing the electrode equipment, enabling the placement position of the electrode to be consistent with that of the electrode as far as possible, and constructing test data by utilizing the collected electromyographic signals.
The method for constructing the test data is similar to the method for constructing the training data, and the difference is that after the preliminary two-dimensional electromyographic feature image is obtained by utilizing the steps 1) to 4) mentioned in the step 1), the preliminary two-dimensional electromyographic feature image is interpolated by S times by utilizing an interpolation method, so that a high-resolution electromyographic feature image is constructed, wherein the length of the high-resolution electromyographic feature image is m, and the width of the high-resolution electromyographic feature image is n.
By way of example, it is possible to provide: m is 280 and n is 280.
For example, the interpolation method may be bicubic interpolation.
3. A model structure based on a convolutional neural network is constructed in advance to serve as a classifier, the network is trained by utilizing training data to obtain optimal network model parameters, and a classifier model is obtained by combining the optimal network model parameters and the training data.
In the embodiment of the invention, the underlying network structure of the model structure of the convolutional neural network is migrated from the image field to classify and identify the trained network (for example, VGG16), then a plurality of convolutional layers and pooling layers are connected, batch standardization technology is added, and the dropout technology enhances the generalization capability of the model; the full connected layer of convolution is used for realizing the sliding window method of convolution, and the last layer is a softmax layer used for classification and identification.
As shown in FIG. 3, the optimal network model parameters are shown exemplarily, wherein smallregion represents an electromyogram of a core record region, conv represents a convolution layer, Max Point 2D represents a 2-dimensional maximum pooling layer, and BatchNormal represents a batch normalization layer. The convolutional layer block contains the size of the convolutional kernel, and the number of kernels, e.g., "3 x3conv, 64", represents that the convolutional kernel of the convolutional layer has a size of 3x3 and the number of kernels is 64.
The main process of obtaining the classifier model by combining the optimal network model parameters and the training data is as follows: and (3) jointly transmitting the core recording area electromyographic feature image of the training data and the corresponding action task label to a convolutional neural network, and training the weight of the optimal network model parameter in a gradient descent mode to obtain a classifier model.
4. And constructing a template by using the training samples in the training data, respectively matching a certain number of test samples in the test data, and outputting a classification result by using a classifier, thereby initializing the electrode offset calibration matrix at the current stage.
Constructing an electromyographic characteristic image template for core recording area electromyographic characteristic images of K action tasks in the over-training data, and aiming at the front N c (e.g., N) c 50) test samples, searching the electromyographic feature image presented by each test sample in an image matching mode by taking the electromyographic feature image template as a target; the relative position of each test sample after image registration can be obtained in the process, the category identification result of each test sample is obtained through the relative position of the image registration and the classifier result, and N is synthesized c And initializing the electrode offset calibration matrix A at the moment according to the relative position and the classifier result of each test sample after image registration is completed. The specific implementation mode is as follows:
the image matching method comprises the following steps:
where(1≤i≤m-M+1,1≤j≤n-N+1,1≤c≤K)
s is a WL characteristic dimension matrix of a high-resolution myoelectric characteristic image in the test data, and m and n are image length and width of the high-resolution myoelectric characteristic image; the T matrix is a target to be matched, is derived from training data and is obtained by averaging WL characteristic dimension matrixes of all core recording area electromyographic characteristic images under the c-th category of the training data, and the length and the width of the matrix are M and N; i and j represent the image matching of the sub-region of the S matrix at the (i, j) position and the T matrix, which together identify all possible offset positions;
the prediction categories of the test samples are:
wherein the content of the first and second substances,
represents D matc h Coordinates of global minima in the matrix; the R matrix outputs the classification result for the classifier, the fix (-) function has the function of rounding the input value, d s Is the down sampling rate; illustratively, d may be set s ＝7.5。
The predicted position of the electrode offset is then recorded as:
initializing a D matrix when the algorithm is started, wherein the D matrix has the same dimensionality as the R matrix; then, the output result R of the classifier of each sample is used for carrying out iterative updating, and N is passed c After each test sample, initializing an electrode offset calibration matrix A through the D matrix, wherein the electrode offset calibration matrix A reflects the specific direction and distance of electrode offset:
wherein D (: c) represents the whole two-dimensional matrix under the condition that the third dimension of the D matrix is the value of c; the Top5 (-) function leaves the maximum 5 values of the input matrix unchanged and zeros other values, and the max (-) function returns the maximum value of the input matrix, with ε being a set coefficient to prevent the denominator being 0, and exemplary, ε may be set to 0.001.
5. In a later stage, the electrode positions of other test samples in the test data are registered by using the electrode offset calibration matrix, a classification result is given by a classifier, and meanwhile, the electrode offset calibration matrix is updated by using other test samples in the test data in an iterative manner.
In the embodiment of the invention, the electrode offset calibration matrix is used for registering the electrode positions of other test samples in the test data, and a classification result is given by a classifier; the classification result for a certain test sample is expressed as:
wherein R (: c) represents the whole two-dimensional matrix of the R matrix under the condition that the third dimension is c value, and the sum (-) function returns the sum of all values of the input matrix.
Then, every time N u After 50 test samples have been input, the electrode offset calibration matrix a is continuously updated according to the method in step 4.
To illustrate the performance of the above-described protocol of the present invention, comparative experiments were conducted using conventional methods with the above-described protocol of the present invention.
In the comparison experiment, an LDA classifier is used for comparing the recognition rate change degree before and after the deviation. LDA is used as a classic classification method in the myoelectricity control field, and the classifier has good classification effect and good robustness. The method comprises the following specific steps: firstly, selecting a subject to acquire electromyographic signals through the step 1, then extracting multiple features from each channel of a sliding window sample, and then integrating each sample into a vector with the length of 300 to form training data; the training data is then used to train the LDA classifier. When the electrode is worn again and electrode offset occurs, the newly acquired electromyographic signals are converted into vectors with the length of 300 in the same way, and classification and identification are carried out by directly utilizing the LDA classifier trained before.
And measuring the identification result by using the identification accuracy, specifically, dividing the number of the correctly identified samples by the number of all the samples. The accuracy obtained for the comparative method and the method proposed by the present invention for 4 offset experiments is shown in table 1:
TABLE 1 results of comparison of accuracy
The beneficial effects of the scheme of the invention are as follows: the invention utilizes a two-dimensional high-density electrode array to collect surface electromyographic signals, and the surface electromyographic signals are regarded as the time-space mode imaging of skeletal muscle activity. The electromyographic characteristic image patterns associated with the task within the common coverage area of the electrode offset front and back arrays are assumed to be invariant. Based on the scientific hypothesis, the invention provides a method for detecting and identifying the target of the electromyographic feature image for the first time, which is used for solving the problem of motion recognition when the electromyographic electrode array is deviated. Specifically, the invention provides a convolutional neural network structure based on transfer learning, and the matching of a local area between training data (before electrode offset) and test data (after electrode offset) is searched and completed from a preprocessed high-resolution electromyographic feature image, so that the self-alignment or self-calibration of an electrode array is realized. The invention uses a complete unsupervised learning mode, does not need any additional calibration data, and directly finishes the self-calibration process of the electrode in the test stage. On the basis of this, the data of the recording area where the array shifts and overlaps before and after are recognized, and high accuracy of the motion pattern recognition can be ensured. The invention provides a beneficial solution for the problem of electrode array deviation in electromyographic pattern recognition.
Through the above description of the embodiments, it is clear to those skilled in the art that the above embodiments can be implemented by software, and can also be implemented by software plus a necessary general hardware platform. With this understanding, the technical solutions of the embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments of the present invention.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. An adaptive electrode registration method for robust electromyography control, comprising:
the method for constructing training data by using the electromyographic signals collected by the currently worn electrode equipment comprises the following steps: the electrode equipment selects a flexible high-density electrode array with p row channels, q column channels and D density; placing electrode equipment at a position to be measured, acquiring continuous electromyographic signals one by one when executing K action tasks, and then acquiring electromyographic signals in a section of rest state; segmenting the continuous electromyographic signals by using the electromyographic signals in the resting state to obtain a series of sample data; performing multi-feature extraction on the electromyographic signals of each channel in each sample data, and constructing each sample into a primary two-dimensional electromyographic feature image; independently extracting the median subregion of the preliminary two-dimensional electromyographic feature image, interpolating by an interpolation method by S' times, and adding the interpolation result to the training data; wherein the median subregion is defined as a core recording region;
re-wearing electrode equipment, enabling the placement position of the electrode to be consistent with the previous position as much as possible, and constructing test data by utilizing the collected myoelectric signals; in the test data construction process, a primary two-dimensional electromyographic feature image is constructed in the same way as the training data construction, and then the primary two-dimensional electromyographic feature image is interpolated by S' times by using an interpolation method, so that a high-resolution electromyographic feature image is constructed;
a model structure based on a convolutional neural network is constructed in advance to serve as a classifier, the network is trained by utilizing training data to obtain optimal network model parameters, and a classifier model is obtained by combining the optimal network model parameters and the training data;
constructing a template by using training samples in training data, respectively matching a certain number of test samples in the test data and outputting a classification result by using a classifier, thereby initializing an electrode offset calibration matrix at the current stage;
in the later stage, the electrode offset calibration matrix is used for registering the electrode positions of other test samples in the test data, a classification result is given through a classifier, and meanwhile, the electrode offset calibration matrix is updated by using other test samples in the test data in an iterative manner;
the method includes the steps of constructing a template by using training samples in training data, respectively matching a certain number of test samples in the test data, and outputting a classification result by using a classifier, so that initializing an electrode offset calibration matrix at a current stage includes:
constructing an electromyographic feature image template through core recording area electromyographic feature images of K action tasks in training data, and aiming at the front N c The method comprises the following steps that (1) each test sample is searched in an electromyographic feature image presented by each test sample in an image matching mode by taking an electromyographic feature image template as a target; in the process, for each test sample, the relative position after image registration can be obtained, and the category identification result of each test sample is obtained through the relative position of image registration and the result of a classifier;
synthesis of N c Initializing a current electrode offset calibration matrix A according to the relative position and the classifier result of each test sample after image registration is completed;
the image matching method comprises the following steps:
where(1≤i≤m-M+1,1≤j≤n-N+1,1≤c≤K)
s is a WL characteristic dimension matrix of a high-resolution myoelectric characteristic image in the test data, and m and n are image length and width of the high-resolution myoelectric characteristic image; the T matrix is a target to be matched, is derived from training data and is obtained by averaging WL characteristic dimension matrixes of myoelectric characteristic images of all core recording areas under the c-th category of the training data, and the length and the width of the matrix are M and N;
the class prediction of the test sample is:
wherein the content of the first and second substances,
represents D match Coordinates of global minima in the matrix; the R matrix is the output result of the classifier, the function of the fix (-) is to round the input value, d s Is the down sampling rate;
the predicted position of the electrode offset is then recorded as:
initializing a D matrix when the algorithm is started, wherein the D matrix has the same dimensionality as the R matrix; then, the output result R of the classifier of each sample is used for carrying out iterative updating, and N is passed c After each test sample, initializing an electrode offset calibration matrix A through the D matrix, wherein the electrode offset calibration matrix A reflects the specific direction and distance of electrode offset:
wherein D (: c) represents the whole two-dimensional matrix under the condition that the third dimension of the D matrix is the value of c; the Top5 (-) function leaves the maximum 5 values of the input matrix unchanged and concatenates to zero other values, the max · function returns the maximum value of the input matrix, and ε is a set coefficient to prevent the denominator from being 0.
2. The adaptive electrode registration method for robust electromyography control according to claim 1, wherein the segmenting the continuous electromyography signal with the electromyography signal at rest state to obtain a series of sample data comprises:
dividing the continuous electromyographic signal flow into a certain number of analysis windows by using a sliding window technology, wherein the window length is W, and the sliding increment is L; selecting an analysis window formed by electromyographic signal flows in a resting state, and calculating a resting state threshold value as Th; and judging whether all analysis windows are active segments of muscle contraction or not by utilizing the resting state threshold Th, and if so, marking a corresponding action task label and taking the label as sample data.
3. The adaptive electrode registration method for robust electromyography control according to claim 1, wherein the extracted multi-features include time-domain features, including waveform length WL, and time-dependent power spectrum descriptive features.
4. The adaptive electrode registration method for robust electromyography control according to claim 1, wherein an underlying network structure of a model structure of a convolutional neural network is migrated from an image domain to classify and recognize a trained network, followed by a plurality of convolutional layers and pooling layers, and a batch standardization technique is added; the full connected layer of convolution is used for realizing the sliding window method of convolution, and the last layer is a softmax layer used for classification and identification.
5. The adaptive electrode registration method for robust electromyography control according to claim 1, wherein deriving a classifier model combining optimal network model parameters with training data comprises:
and (3) jointly transmitting the core recording area electromyographic feature images and the corresponding action task labels in the training data to a convolutional neural network, and training the weight of the optimal network model parameter in a gradient descent mode to obtain a classifier model.
6. The adaptive electrode registration method for robust electromyography control according to claim 1, wherein electrode positions of other test samples in the test data are registered by using an electrode offset calibration matrix, and a classification result is given by a classifier; the classification result for a certain test sample is expressed as:
wherein R (: c) represents the whole two-dimensional matrix of the R matrix under the condition that the third dimension is c value, and the sum (-) function returns the sum of all values of the input matrix.
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| CN111657941B (en) * | 2020-07-10 | 2022-04-19 | 中国科学技术大学 | Electrode correction and myoelectric pattern recognition method based on muscle core activation region |
| CN111857349B (en) * | 2020-07-28 | 2022-03-01 | 中国科学技术大学 | Wrist strap type gesture recognition equipment with self-repairing and self-calibrating functions and method |
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