LSTM 网络的序列到序列分类来检测 EMG 信号中的手臂运动matlab代码.zip

fs = 3000; localfile = matlab.internal.examples.downloadSupportFile("SPT","data/MyoelectricData.zip"); datasetFolder = fullfile(fileparts(localfile),"MyoelectricData"); if ~exist(datasetFolder,"dir") unzip(localfile,datasetFolder) end sds1 = signalDatastore(datasetFolder,IncludeSubFolders=true,SampleRate=fs); p = endsWith(sds1.Files,"d.mat"); sdssig = subset(sds1,p); sds2 = signalDatastore(datasetFolder,SignalVariableNames=["motion";"data_indx"],IncludeSubfolders=true); p = endsWith(sds2.Files,"i.mat"); sdslbl = subset(sds2,p); signal = preview(sdssig); for i = 1:8 x(i) = subplot(4,2,i); plot(signal(:,i)); title("Channel"+i); end linkaxes(x,"y"); lbls = {}; i = 1; while hasdata(sdslbl) label = read(sdslbl); idx_start = label{2}(2:end-1)'; idx_end = [idx_start(2:end)-1;idx_start(end)+(3*fs)]; val = categorical(label{1}(2:end-1)',[1 2 3 4 5 6 7], ... ["HandOpen" "HandClose" "WristFlexion" "WristExtension" "Supination" "Pronation" "Rest"]); ROI = [idx_start idx_end]; % In some cases, the number of label values and ROIs are not equal. % To eliminate these inconsistencies, remove the extra label value or ROI limits. if numel(val) < size(ROI,1) ROI(end,:) = []; elseif numel(val) > size(ROI,1) val(end) = []; end lbltable = table(ROI,val); lbls{i} = {lbltable}; i = i+1; end lblDS = signalDatastore(lbls); lblstable = preview(lblDS); lblstable {1} DS = combine(sdssig,lblDS); combinedData = preview(DS); figure; msk = signalMask(combinedData{2}); plotsigroi(msk,combinedData{1}(:,1)); tDS = transform(DS,@preprocess); transformedData = preview(tDS); rng default; [trainIdx,~,testIdx] = dividerand(30,0.8,0,0.2); trainIdx_all = {}; m = 1; for k = trainIdx if k == 1 start = k; else start = ((k-1)*24)+1; end l = start:k*24; trainIdx_all{m} = l; m = m+1; end trainIdx_all = cell2mat(trainIdx_all)'; trainDS = subset(tDS,trainIdx_all); testIdx_all = {}; m = 1; for k = testIdx if k == 1 start = k; else start = ((k-1)*24)+1; end l = start:k*24; testIdx_all{m} = l; m = m+1; end testIdx_all = cell2mat(testIdx_all)'; testDS = subset(tDS,testIdx_all); layers = [ ... sequenceInputLayer(8) lstmLayer(80,OutputMode="sequence") fullyConnectedLayer(4) softmaxLayer classificationLayer]; options = trainingOptions("adam", ... MaxEpochs=100, ... MiniBatchSize=32, ... Plots="training-progress",... InitialLearnRate=0.001,... Verbose=0,... Shuffle="every-epoch",... GradientThreshold=1e5,... DispatchInBackground=true); traindata = readall(trainDS,"UseParallel",true); rawNet = trainNetwork(traindata(:,1),traindata(:,2),layers,options); testdata = readall(testDS); predTest = classify(rawNet,testdata(:,1),MiniBatchSize=32); confusionchart([testdata{:,2}],[predTest{:}],Normalization="column-normalized") function Tsds = preprocess(inputDS) sig = inputDS{1}; roiTable = inputDS{2}; % Remove first and last rest periods from signal sig(roiTable.ROI(end,2):end,:) = []; sig(1:roiTable.ROI(1,1),:) = []; % Shift ROI limits to account for deleting start and end of signal roiTable.ROI = roiTable.ROI-(roiTable.ROI(1,1)-1); % Create signal mask m = signalMask(roiTable); L = length(sig); % Obtain sequence of category labels and remove pronation, supination, and rest motions mask = catmask(m,L); idx = ~ismember(mask,{'Pronation','Supination','Rest'}); mask = mask(idx); sig = sig(idx,:); % Create new signal mask without pronation and supination categories m2 = signalMask(mask); m2.SpecifySelectedCategories = true; % m2.SelectedCategories = [1 2 3 4 7]; m2.SelectedCategories = [1 2 3 4]; mask = catmask(m2); % Filter and downsample signal data sigfilt = bandpass(sig,[10 400],3000); downsig = downsample(sigfilt,3); % Downsample label data downmask = downsample(mask,3); targetLength = 12000; % Get number of chunks numChunks = floor(size(downsig,1)/targetLength); % Truncate signal and mask to integer number of chunks sig = downsig(1:numChunks*targetLength,:); mask = downmask(1:numChunks*targetLength); % Create a cell array containing signal chunks sigOut = {}; step = 0; for i = 1:numChunks sigOut{i,1} = sig(1+step:i*targetLength,:)'; step = step+targetLength; end % Create a cell array containing mask chunks lblOut = reshape(mask,targetLength,numChunks)'; lblOut = num2cell(lblOut,2); % Output a two-column cell array with all chunks Tsds = [sigOut,lblOut]; end