CN108388348A - A kind of electromyography signal gesture identification method based on deep learning and attention mechanism - Google Patents
A kind of electromyography signal gesture identification method based on deep learning and attention mechanism Download PDFInfo
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Abstract
The invention discloses a kind of electromyography signal gesture identification method based on deep learning and attention mechanism, steps are as follows：Noise reduction filtering is carried out to gesture electromyography signal；One classical feature set is extracted to each window data using sliding window, and builds the myoelectricity image of new feature based；A kind of deep learning frame based on convolutional neural networks, Recognition with Recurrent Neural Network and attention mechanism is designed, and its network architecture parameters is optimized；It trains to obtain sorter model using designed deep learning frame and training data；Test data is input in trained deep learning network model, according to the likelihood of last layer of output, the corresponding classification of maximum likelihood is the classification identified.The present invention is based on new feature image and myoelectricity hand signal is identified in the deep learning frame based on attention mechanism.A variety of different gestures of same subject can be accurately identified using the electromyography signal gesture identification method based on deep learning and attention mechanism.
Description
Technical field
The invention belongs to computers to be combined field with bio signal, more particularly to a kind of based on deep learning and attention
The electromyography signal gesture identification method of mechanism.
Background technology
Surface electromyogram signal (surface electromyography, sEMG) is a kind of electrode paste by non-intrusion type
The bio signal of muscle activity is recorded in skin surface.Can be auxiliary and rehabilitation by recording and analyzing surface electromyogram signal
Technology provides more effective information, has for Sports Scientific Research, human-computer interaction, medical science of recovery therapy clinic and basic research etc.
Important learning value and application value.In such applications, the Gesture Recognition based on electromyography signal takes on important angle
Color.One classical electromyography signal gesture identification flow is made of data prediction, feature space structure and classification.Data are located in advance
Reason part mainly carries out rectification and filtering to reduce noise to signal, and feature space structure part converts pretreated signal
To feature space so that there is the discrimination of bigger between class, model finally is trained for classifying with a machine learning method.
The structure part of feature space and the other identification division of gesture class are to improve highly important two of recognition accuracy
Part.Therefore many researchers are dedicated to proposing new feature by their domain knowledge, such as Phinyomark spies
Collection.On the other hand, in research at home and abroad, many Machine learning classifiers are used in electromyography signal gesture identification, than
Such as artificial neural network, k nearest neighbor, linear judgment analysis, support vector machines and hidden Markov model.Wherein support vector machines and
Linear judgment analysis is two kinds of most common graders.
In progress both domestic and external in recent years, deep learning method all obtains table best at present in many fields
It is existing.Wherein foremost convolutional neural networks have also been successfully applied in the gesture identification of electromyography signal, are obtained at present most
Good recognition effect.Attention mechanism is a kind of method of highly effective enhancing Recognition with Recurrent Neural Network modeling ability, at present
Preferable effect is achieved in fields such as machine translation.But currently without using Recognition with Recurrent Neural Network combination convolutional neural networks
Electromyography signal gesture is identified in method, and the present invention by attention mechanism be added in Recognition with Recurrent Neural Network to model into
Row enhancing.
Invention content
In view of the above-mentioned deficiencies in the prior art, it is an object of the present invention to provide a kind of based on deep learning and attention mechanism
Electromyography signal gesture identification method, by designing the model based on convolutional neural networks, Recognition with Recurrent Neural Network and attention mechanism
Structure improves the accuracy of gesture identification.
The purpose of the present invention is achieved through the following technical solutions：It is a kind of based on deep learning and attention mechanism
Electromyography signal gesture identification method, includes the following steps：
(1) myoelectricity data, data prediction, including following sub-step are obtained：
(1.1) from public data collection NinaProDB1, NinaProDB2, BioPatRec subset, CapgMyo subsets and
Gesture motion myoelectricity data are obtained in csl-hdemg；
(1.2) noise reduction is filtered using different preprocess methods to different data collection respectively；
(2) division of original signal training dataset and original signal test data set, including following sub-step：
(2.1) according to the electromyography signal label got, the data in each electromyography signal file are divided into several
Electromyography signal gesture section, each gesture section are repeated comprising one-off；
(2.2) according to different appraisal procedures, the multiple action of gesture is repeated original signal training number is respectively divided
It is concentrated according to collection and original signal test data, completes original training and the division of test data set；
(3) data segmentation and feature extraction, including following sub-step：
(3.1) each gesture section is divided into the signal segment of multiple regular lengths with sliding window；
(3.2) feature extraction is carried out to each channel of the fixed length signal segment in each window, extracts various features；
(4) new myoelectricity image, including following sub-step are built：
(4.1) by the feature vector rearrangement in each channel in window so that each two channel can be adjacent；
(4.2) build new myoelectricity image, the width of new myoelectricity image is 1, it is a height of rearrange after port number, Color Channel
Number is characterized vector dimension；
(5) the electromyography signal multiclass gesture identification based on deep learning and attention mechanism, includes the following steps：
(5.1) model structure of projected depth study and attention mechanism, model structure is by convolutional neural networks, cycle god
It is constituted through network and primary attention power mechanism；Convolutional neural networks carry out high-level characteristic extraction to the new myoelectricity image of input, follow
Relationship of the ring neural network between the every frame of new myoelectricity image sequence models, and primary attention power mechanism is to Recognition with Recurrent Neural Network
Output carry out importance weighting, t moment attention weight αtCalculation formula be：
Mt=tanh (Whht)
αt=softmax (wTMt)
Wherein, htIt is the output of Recognition with Recurrent Neural Network, WhAnd wTIt is weight matrix to be trained, when T is a gesture section
Between length, r is the output of primary attention power machined part；Softmax functions are normalization exponential functions；
(5.2) original signal training data concentrates each sample to carry out the structure of new myoelectricity image, obtains new myoelectricity image
Input of the training dataset as whole network carries out the network parameter of convolutional neural networks and Recognition with Recurrent Neural Network excellent one by one
Change, obtains optimal model parameters；
(5.3) optimal model parameters and the training of new myoelectricity image training dataset obtained by step (5.2) training obtain
Disaggregated model；
(5.4) it concentrates each sample to carry out the structure of new myoelectricity image test data, obtains new myoelectricity image measurement number
According to collection, the disaggregated model that input step (5.3) obtains, output category result.
Further, right to NinaProDB1 using low pass butterworth filtering in the step (1.2)
NinaProDB2 filters and is downsampled to 100Hz using low pass butterworth, and BioPatRec subsets and CapgMyo subsets are not
It is filtered, rectification and low pass butterworth filtering is carried out to csl-hdemg.
Further, in the step (2.1), the division of original signal training dataset and original signal test data set
Using being assessed in subject；Different data collection uses different division methods：NinaProDB1 by the 1,3,4,6th of each subject the,
8,9 and 10 repetitions are as training data, and the 2nd, 5,7 time as test data；NinaProDB2 makees the 1,3,4,6th repetition
For training data, the 2nd, 5 time as test data；BioPatRec subsets repeat first time to be used as training data, twice
It repeats to be used as test data；CapgMyo subsets using the repetition of half as training data, i.e. 5 repetitions, make by addition 5 repetitions
For test data；The data of single-subject are divided into 10 parts by csl-hdemg data sets, and carry out 10 folding cross validations.
Further, in the step (3.1), different data sets uses different sliding window length and sliding step
It is long；The sliding window length of NinaProDB1 is 150ms and 200ms, sliding step 10ms；The sliding window of NinaProDB2
Length is 200ms, sliding step 100ms；The sliding window length of BioPatRec subsets is 50ms and 150ms, sliding step
For 50ms；The sliding window length of CapgMyo subsets is 40ms and 150ms, sliding step 1ms；The sliding window of csl-hdemg
Mouth length is 150ms and 170ms, sliding step 0.5ms.
Further, in the step (3.2), to the electromyography signal in window be based on classics feature set Phinyomark into
Row characteristic vector pickup, including characteristic signal amplitude absolute mean MAV, waveform length WL, autoregressive coefficient AR, absolute mean are oblique
Energy and gross energy ratio PSR and Willison amplitude WAMP near rate MAVSLP, average frequency MNF, power spectrum maximum value；
CapgMyo subsets and csl-hdemg are high density electromyography signals, without feature extraction, the structure figures directly in original signal
Picture.
Further, in the step (5.1), Recognition with Recurrent Neural Network part, the long mnemon (LSTM) in short-term of selection is come
Gradient is solved to disappear and gradient explosion issues.
Further, in the step (5.2), convolutional neural networks include level 2 volume lamination in optimal models, are followed by 2 layers
Local articulamentum finally connects 3 layers of full articulamentum, the length mnemon in short-term that Recognition with Recurrent Neural Network layer is 512 by output size
(LSTM) it constitutes, last identification division is made of the full articulamentums of a G-way and softmax layers.
Further, in the step (5.3), the training process of disaggregated model is：New myoelectricity image training dataset with
The each sample of the data set corresponds to input of the gesture label collectively as model, and obtaining model parameter by training is stored.
Further, in the step (5.4), the output of disaggregated model is label, that is, corresponds to the label of test sample, is used
Recognition accuracy weighs recognition result, and recognition accuracy is to identify correct sample number divided by all test sample numbers.
The beneficial effects of the invention are as follows：The present invention proposes a kind of myoelectricity based on convolutional neural networks and Recognition with Recurrent Neural Network
Signal gesture identification method can be extracted and be modeled to the room and time feature of electromyography signal simultaneously, with existing invention
In the method for being based purely on convolutional neural networks compare, this method can effectively promote discrimination.Attention mechanism is added
Into the model structure based on convolutional neural networks and Recognition with Recurrent Neural Network, the performance of model structure can be enhanced.Extraction tradition
Electromyography signal builds input of the new myoelectricity image as model, can effectively promote the accuracy rate of electromyography signal gesture identification.
Description of the drawings
Fig. 1 is the method for the present invention flow chart；
Fig. 2 is inventive network structure chart.
Specific implementation mode
Invention is further described in detail with reference to the accompanying drawings and detailed description.
As shown in Figure 1, a kind of electromyography signal gesture identification based on deep learning and attention mechanism provided by the invention
Method, specific implementation step are as follows：
Step (1) from public data collection NinaProDB1, NinaProDB2, BioPatRec subset, CapgMyo subsets and
Gesture motion myoelectricity data are obtained in csl-hdemg；It is right to NinaProDB1 using low pass butterworth filtering
NinaProDB2 filters and is downsampled to 100Hz using low pass butterworth, and BioPatRec subsets and CapgMyo subsets are not
It is filtered, rectification and low pass butterworth filtering is carried out to csl-hdemg.
The division of step (2) original signal training dataset and original signal test data set, according to the myoelectricity got
Data in each electromyography signal file are divided into electromyography signal gesture section one by one by signal label, and each gesture section includes
One-off repeats；Our test uses different division methods using assessment in subject, different data collection：NinaProDB1
By the 1st, 3,4,6,8,9 and 10 repetition of each subject as training data, the 2nd, 5,7 time as test data；
NinaProDB2 is by the 1st, 3,4,6 repetition as training data, and the 2nd, 5 time as test data；BioPatRec subsets are by
It is primary to repeat to be used as training data, it repeats to be used as test data twice；CapgMyo subsets are using the repetition of half as training
Data, i.e. 5 repetitions, in addition 5 repetitions are as test data；The data of single-subject are divided by csl-hdemg data sets
10 parts, and carry out 10 folding cross validations.
Step (3) is split to data and feature extraction, different data sets using different sliding window length and
Sliding step.The sliding window length of NinaProDB1 is 150ms and 200ms, sliding step 10ms；The cunning of NinaProDB2
Dynamic length of window is 200ms, sliding step 100ms；The sliding window length of BioPatRec subsets is 50ms and 150ms, sliding
Dynamic step-length is 50ms；The sliding window length of CapgMyo subsets is 40ms and 150ms, sliding step 1ms；Csl-hdemg's
Sliding window length is 150ms and 170ms, sliding step 0.5ms.Classical feature set is based on to the electromyography signal in window
Phinyomark carries out characteristic vector pickup, including characteristic signal amplitude absolute mean (MAV), waveform length (WL), autoregression
Energy and gross energy ratio near coefficient (AR), absolute mean slope (MAVSLP), average frequency (MNF), power spectrum maximum value
(PSR) and Willison amplitudes (WAMP).
Step (5) projected depth learns and the model structure of attention mechanism, and model structure is by convolutional neural networks, cycle
Neural network and primary attention power mechanism are constituted.Convolutional neural networks carry out high-level characteristic extraction to the new myoelectricity image of input,
Relationship of the Recognition with Recurrent Neural Network between the every frame of new myoelectricity image sequence models, and primary attention power mechanism is to recycling nerve net
The output of network carries out importance weighting, to obtain final expression for electromyography signal gesture identification.To convolutional neural networks
It is optimized one by one with the network parameter of Recognition with Recurrent Neural Network, optimal network structure is as shown in the table：
| Layer | Title | Parameter |
| 1 | Convolutional layer 1 | 64 cores, core size (3*3) |
| 2 | Convolutional layer 2 | 64 cores, core size (3*3) |
| 3 | Local articulamentum 1 | 64 cores |
| 4 | Local articulamentum 2 | 64 cores |
| 5 | Full articulamentum 1 | 512 dimension outputs |
| 6 | Full articulamentum 2 | 512 dimension outputs |
| 7 | Full articulamentum 3 | 128 dimension outputs |
| 8 | Recycle nervous layer | Long short-term memory (LSTM) 512 ties up hidden state output |
| 9 | Full articulamentum 4 and softmax layers |
Recognition with Recurrent Neural Network part, we select long mnemon (LSTM) in short-term and disappear and gradient explosion to solve gradient
Problem.Attention mechanism increases behind Recognition with Recurrent Neural Network, i.e. the output of Recognition with Recurrent Neural Network is attention machined part
Input, calculation formula is：
Mt=tanh (Whht)
αt=softmax (wTMt)
Wherein, htIt is the output of Recognition with Recurrent Neural Network, WhAnd wTIt is weight matrix to be trained, when T is a gesture section
Between length, r is the output of primary attention power machined part；Softmax functions are normalization exponential functions.Training process is：It is former
Beginning signal training data concentrates each sample to carry out the structure of new myoelectricity image, obtains new myoelectricity image training dataset, will be new
Myoelectricity image training dataset gesture label corresponding with each sample of the data set collectively as model input, by trained
To model parameter and stored.Test process is：It concentrates each sample to carry out the structure of new myoelectricity image test data, obtains
To new myoelectricity image test data set, loads by the new trained model of myoelectricity image training dataset, input new myoelectricity image
Test data set is exported as gesture class label, is weighed to recognition result with recognition accuracy, and recognition accuracy is identification
Correct sample number divided by all sample numbers.
To NinaProDB1, NinaProDB2, BioPatRec subset, CapgMyo subsets and csl-hdemg data sets
Gesture complete or collected works are identified.NinaProDB1 includes 52 gestures, and NinaProDB2 includes 50 gestures, and BioPatRec subsets include
26 gestures, CapgMyo subsets include 8 gestures, and csl-hdemg includes 27 gestures.Using the present invention is based on deep learnings and attention
The discrimination result of the electromyography signal gesture identification method of power mechanism is：
Claims (9)
1. a kind of electromyography signal gesture identification method based on deep learning and attention mechanism, which is characterized in that including as follows
Step：
(1) myoelectricity data, data prediction, including following sub-step are obtained：
(1.1) from public data collection NinaProDB1, NinaProDB2, BioPatRec subset, CapgMyo subsets and csl-
Gesture motion myoelectricity data are obtained in hdemg；
(1.2) noise reduction is filtered using different preprocess methods to different data collection respectively；
(2) division of original signal training dataset and original signal test data set, including following sub-step：
(2.1) according to the electromyography signal label got, the data in each electromyography signal file are divided into several myoelectricities
Signal gesture section, each gesture section are repeated comprising one-off；
(2.2) according to different appraisal procedures, the multiple action of gesture is repeated original signal training dataset is respectively divided
It is concentrated with original signal test data, completes original training and the division of test data set；
(3) data segmentation and feature extraction, including following sub-step：
(3.1) each gesture section is divided into the signal segment of multiple regular lengths with sliding window；
(3.2) feature extraction is carried out to each channel of the fixed length signal segment in each window, extracts various features；
(4) new myoelectricity image, including following sub-step are built：
(4.1) by the feature vector rearrangement in each channel in window so that each two channel can be adjacent；
(4.2) build new myoelectricity image, the width of new myoelectricity image is 1, it is a height of rearrange after port number, Color Channel number is
Feature vector dimension；
(5) the electromyography signal multiclass gesture identification based on deep learning and attention mechanism, includes the following steps：
(5.1) model structure of projected depth study and attention mechanism, model structure is by convolutional neural networks, cycle nerve net
Network and primary attention power mechanism are constituted；Convolutional neural networks carry out high-level characteristic extraction, cycle god to the new myoelectricity image of input
Relationship through network between the every frame of new myoelectricity image sequence models, and primary attention power mechanism is to the defeated of Recognition with Recurrent Neural Network
Go out and carries out importance weighting, t moment attention weight αtCalculation formula be：
Mt=tanh (Whht)
αt=softmax (wTMt)
Wherein, htIt is the output of Recognition with Recurrent Neural Network, WhAnd wTIt is weight matrix to be trained, T is that the time of a gesture section is long
Degree, r is the output of primary attention power machined part；Softmax functions are normalization exponential functions；
(5.2) original signal training data concentrates each sample to carry out the structure of new myoelectricity image, obtains new myoelectricity image training
Input of the data set as whole network optimizes the network parameter of convolutional neural networks and Recognition with Recurrent Neural Network one by one,
Obtain optimal model parameters；
(5.3) optimal model parameters and the training of new myoelectricity image training dataset obtained by step (5.2) training are classified
Model；
(5.4) it concentrates each sample to carry out the structure of new myoelectricity image test data, obtains new myoelectricity image test data set,
The disaggregated model that input step (5.3) obtains, output category result.
2. a kind of electromyography signal gesture identification method based on deep learning and attention mechanism according to claim 1,
It is characterized in that, in the step (1.2), to NinaProDB1 using low pass butterworth filtering, NinaProDB2 is used
Low pass butterworth filter and be downsampled to 100Hz, BioPatRec subsets and CapgMyo subsets without filtering, it is right
Csl-hdemg carries out rectification and low pass butterworth filtering.
3. a kind of electromyography signal gesture identification method based on deep learning and attention mechanism according to claim 1,
It is characterized in that, in the step (2.1), the division of original signal training dataset and original signal test data set uses subject
Interior assessment；Different data collection uses different division methods：NinaProDB1 is by the 1,3,4,6,8,9th and 10 time of each subject
It repeats to be used as training data, the 2nd, 5,7 time as test data；NinaProDB2 is using the 1,3,4,6th repetition as training number
According to the 2nd, 5 time as test data；BioPatRec subsets repeat first time to be used as training data, twice repeatedly conduct
Test data；CapgMyo subsets are using the repetition of half as training data, i.e. 5 repetitions, and in addition 5 repetitions are as test number
According to；The data of single-subject are divided into 10 parts by csl-hdemg data sets, and carry out 10 folding cross validations.
4. a kind of electromyography signal gesture identification method based on deep learning and attention mechanism according to claim 1,
It is characterized in that, in the step (3.1), different data sets uses different sliding window length and sliding step；
The sliding window length of NinaProDB1 is 150ms and 200ms, sliding step 10ms；The sliding window of NinaProDB2 is long
Degree is 200ms, sliding step 100ms；The sliding window length of BioPatRec subsets is 50ms and 150ms, and sliding step is
50ms；The sliding window length of CapgMyo subsets is 40ms and 150ms, sliding step 1ms；The sliding window of csl-hdemg
Length is 150ms and 170ms, sliding step 0.5ms.
5. a kind of electromyography signal gesture identification method based on deep learning and attention mechanism according to claim 1,
Be characterized in that, in the step (3.2), to the electromyography signal in window be based on classical feature set Phinyomark carry out feature to
Amount extraction, including characteristic signal amplitude absolute mean (MAV), waveform length (WL), autoregressive coefficient (AR), absolute mean slope
(MAVSLP), energy and gross energy ratio (PSR) and Willison amplitudes near average frequency (MNF), power spectrum maximum value
(WAMP)；CapgMyo subsets and csl-hdemg are high density electromyography signals, without feature extraction, directly in original signal
Build image.
6. a kind of electromyography signal gesture identification method based on deep learning and attention mechanism according to claim 1,
It is characterized in that, in the step (5.1), Recognition with Recurrent Neural Network part selects long mnemon (LSTM) in short-term to solve gradient
It disappears and gradient explosion issues.
7. a kind of electromyography signal gesture identification method based on deep learning and attention mechanism according to claim 1,
It is characterized in that, in the step (5.2), convolutional neural networks include level 2 volume lamination in optimal models, are followed by 2 layers of part connection
Layer finally connects 3 layers of full articulamentum, length mnemon (LSTM) structure in short-term that Recognition with Recurrent Neural Network layer is 512 by output size
At last identification division is made of the full articulamentums of a G-way and softmax layers.
8. a kind of electromyography signal gesture identification method based on deep learning and attention mechanism according to claim 1,
It is characterized in that, in the step (5.3), the training process of disaggregated model is：New myoelectricity image training dataset and the data set
Each sample corresponds to input of the gesture label collectively as model, and obtaining model parameter by training is stored.
9. a kind of electromyography signal gesture identification method based on deep learning and attention mechanism according to claim 1,
It is characterized in that, in the step (5.4), the output of disaggregated model is label, that is, corresponds to the label of test sample, accurate with identification
Rate weighs recognition result, and recognition accuracy is to identify correct sample number divided by all test sample numbers.
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