LSTM For classification of EMG sequences

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CHRISTOPHER MILLAR 2019-11-28

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https://ww2.mathworks.cn/matlabcentral/answers/493752-lstm-for-classification-of-emg-sequences

评论： CHRISTOPHER MILLAR 2023-2-11

hello,

I am trying to classify EMG signals with the movement that generated the original signal

I have tried using a patternnet but this gives very bad generalization (I think is due to my lack of knowledge in creating the ANN structure and input format) so I have been investigating LSTM networks using matlab,

am i right in following the sequence to label classification example that is provided in the documentation?

https://uk.mathworks.com/help/deeplearning/examples/classify-sequence-data-using-lstm-networks.html

Thanks

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bavi 2023-2-9

Hey ,@CHRISTOPHER MILLAR I am curious about your project.

I am also trying to classify the hand movement of data from semg sensor data.

I used the classification learer app whuch was failure.

Can you explain your approach?

CHRISTOPHER MILLAR 2023-2-11

In the end i followed 1 of the examples in the documentation to develop a script and used the Experiment Manager App to do the all parameter tuning

at the beginning it was difficult to figure out but i got there in the end. Getting the data in the correct format is VIP then just follow the structure for the networks in the samples.

For Example an LSTM is created like this

layers = [

sequenceInputLayer(numFeatures,'Name','sequence')

lstmLayer(1500,'OutputMode','last','Name','lstm')

dropoutLayer(0.5,'Name','drop')

fullyConnectedLayer(numClasses,'Name','fc')

softmaxLayer('Name','softmax')

classificationLayer('Name','classification')];

you just need to amend the parameters to match your requirements

Hope this helps

Chris

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Answer 1

Raunak Gupta 2019-12-2

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https://ww2.mathworks.cn/matlabcentral/answers/493752-lstm-for-classification-of-emg-sequences#answer_404211

Hi,

For using the example that is mentioned I assume you have several features from the EMG Signal and each EMG signal is a sequence of those features. Since EMG Signal is kind of signal that has temporal relationship between time frames, LSTMs will perform good in capturing the pattern across the data. The preparation of data according to the problem statement is the only concern while using LSTMs. So, I suggest setting up the data such that each datapoint corresponds to a sequence of features and its class.

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CHRISTOPHER MILLAR 2019-12-3

Hi Raunak,

Thanks for your reply, can i just advise you of the format of my data that I am inputting into my LSTM and ask you to confirm that I am following the correct format protocols?

The device I am using to collect the EMG data has 8 channels so for each movement I have a 200 data point sequence across 8 separate channels and I have 12 different movement classes that I have recorded 20 times each so for example:

For each movement I have 20x1 cell that holds each 8 * 200 movement sequence

8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double
8x200 double

this gives me a complete data set of 240 (12 movements X 20) x 1 cells with each cell containing an 8 * 200 sequence. I then label each cell in accordance with the movement class that it corresponds with and then input in the LSTM network that i created using the example provided to specify the layers and training options

inputSize = 8;

numHiddenUnits = 50;

numClasses = 12;

dropoutLayer(0.3,'Name','drop1');

layers = [ ...

sequenceInputLayer(inputSize)

bilstmLayer(numHiddenUnits,'OutputMode','last')

fullyConnectedLayer(numClasses)

softmaxLayer

classificationLayer];

Can you confirm to me that is indeed the correct way of going about formatting my data and configuring the layers of my LSTM?

Thanks in advance of any help you can provide

Chris

CHRISTOPHER MILLAR 2019-12-3

在 MATLAB Online 中打开

Hi Raunak,

This first section is how i extract the weights for each layer from the initial trained LSTM's

for i = 1:NNTrials
IW{i} = lstmNet{i}.Layers(2,1).InputWeights;
RW{i} = lstmNet{i}.Layers(2,1).RecurrentWeights;
B{i} = lstmNet{i}.Layers(2,1).Bias;
FCW{i} = lstmNet{i}.Layers(3,1).Weights;
FCB{i} = lstmNet{i}.Layers(3,1).Bias;
end

I then pass these weights to a function to train new LSTM's after changing some parameter so as to get a fair comparison between LSTMs

iw = IW{i,1}; b = B{i,1};
 rw = RW{i,1}; fcw = FCW{i,1}; fcb = FCB{i,1};

newLayers = [ ...
    sequenceInputLayer(inputSize)
    bilstmLayer(numHiddenUnits,'InputWeights',iw,'RecurrentWeights',rw,'Bias',b,'OutputMode','last')
    fullyConnectedLayer(numClasses,'Weights',fcw, 'Bias',fcb)
    softmaxLayer
    classificationLayer];

And this is the Error message that is generated when I try to execute

trainNetwork(XTrain,YTrain,newLayers,newOptions);
Error using nnet.cnn.layer.BiLSTMLayer.parseInputArguments (line 371)
'InputWeights' matches multiple parameter names: 'InputWeightsL2Factor', 'InputWeightsLearnRateFactor'. To avoid ambiguity, specify the complete name of the parameter.

Thanks in advance of any help you can provide

Regards

Chris

Raunak Gupta 2019-12-4

Hi,

From the error message I can see that this issue was there with R2018a and R2018b release and is fixed in R2019a and later release. So, I suggest installing the latest release.

CHRISTOPHER MILLAR 2019-12-5

Raunak,

I downloaded the newest version of Matlab and this code now works

Thank you very much for all your help with this

Regards

Chris

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