clear all;
close all;
%% parameter setting
subject={'01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','20'};
path=['D:\jiangxinyu\Open Access HDsEMG DataSet Analysis\hyser_dataset']; % change path to the location of the dataset
task_type='dynamic'; % change to 'maintenance' if you need to classify maintenance tasks
switch task_type
case 'dynamic'
K=6; %6-fold cross-validation
case 'maintenance'
K=2; %2-fold cross-validation
end
sig_start=0.25;% remove the first 0.25s startup duration.
step_len=0.125;
window_len=0.25;
fs_emg=2048;
% layout: convert 256 electrodes to 16 * 16 array
for i=1:8
for j=1:8
layout_array(i,j)=64-(i-1)*8-(j-1);
loc1(i,j)=i;
loc2(i,j)=j;
end
end
layout=[[layout_array;64+layout_array],[128+layout_array;192+layout_array]];
%% data preparation
data_input=cell(2,K); % 2 sessions, K folds cross-validation in each session
label_input=cell(2,K);
for session=1:2 % index of experiment sessions
for i=1:20 % index of subjects
i
session
[data,label]=load_pr(path,subject{1,i},session,task_type,'preprocess');
INDICES = crossvalind('Kfold',label,K);
win=hamming(256);
for j=1:length(data)
for t=floor(sig_start*fs_emg):floor(step_len*fs_emg):length(data{1,j})-floor(window_len*fs_emg)
sig=data{1,j}(t+1:t+floor(window_len*fs_emg),:);
[sig_stft,f,t]=stft(sig,fs_emg,'Window',win,'OverlapLength',128,'FrequencyRange','onesided');
sig_tensor=reshape(abs(sig_stft(1:64,:,reshape(layout,[1,256]))),[64*3,16,16]);
sig_tensor_permute=permute(sig_tensor,[2 3 1]);
if(~length(data_input{session,INDICES(j)}))
data_input{session,INDICES(j)}=sig_tensor_permute;
label_input{session,INDICES(j)}=label(j);
else
data_input{session,INDICES(j)}(:,:,:,end+1)=sig_tensor_permute;
label_input{session,INDICES(j)}(end+1)=label(j);
end
end
end
end
end
%% define the network architecture
layers = [imageInputLayer([16,16,192])
convolution2dLayer(3,32,'Padding','same')
batchNormalizationLayer
leakyReluLayer(0.1)
dropoutLayer(0.2)
maxPooling2dLayer(3,'Stride',2)
convolution2dLayer(3,64,'Padding','same')
batchNormalizationLayer
leakyReluLayer(0.1)
dropoutLayer(0.2)
maxPooling2dLayer(3,'Stride',2)
fullyConnectedLayer(576)
fullyConnectedLayer(34)
softmaxLayer
classificationLayer];
options = trainingOptions('adam', ...
'InitialLearnRate',0.001, ...
'GradientDecayFactor',0.9, ...
'SquaredGradientDecayFactor',0.999, ...
'L2Regularization',0.0001, ...
'MaxEpochs',50, ...
'MiniBatchSize',512, ...
'Plots','none')
for session=1:2
for testFoldIdx=1:K
data_test=data_input{session,testFoldIdx};
label_test=label_input{session,testFoldIdx};
train_fold=[1:K];
train_fold(testFoldIdx)=[];
data_train=[];
label_train=[];
for i=1:length(train_fold)
data_train=cat(4,data_train,data_input{session,train_fold(i)});
label_train=[label_train,label_input{session,train_fold(i)}];
end
convnet = trainNetwork(data_train,categorical(label_train),layers,options);
[label_predict,score] = classify(convnet,data_test);
Naverage=(size(data{1,1},1)-ceil(sig_start*fs_emg)-ceil(window_len*fs_emg))/ceil(step_len*fs_emg)+1;
for i=1:length(label_test)/Naverage
score_window_average(i,:)=mean(score((i-1)*Naverage+1:i*Naverage,:));
label_predict_average(i)=find(score_window_average(i,:)==max(score_window_average(i,:)));
label_test_average(i)=label_test(i*Naverage);
end
accuracy(session,testFoldIdx) = sum(label_predict_average == label_test_average)./numel(label_test_average)
end
end
save(['../results/accuracy_',task_type,'_cnn.mat'],'accuracy');