clear all;
close all;
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'
window_len=0.75;
step_len=0.75;
case 'maintenance'
window_len=3.75;
step_len=3.75;
end
sig_start=0.25;% remove the first 0.25s startup duration.
zc_ssc_thresh=0.0004; %threshold to detect valid zero cross and slope sign change features
fs_emg=2048;
pca_active=1;
parfor i=1:20
for session=1:2
i
session
[data,label]=load_pr(path,subject{1,i},session,task_type,'preprocess');
Ntrial=length(data);
feature=[];
for j=1:Ntrial
emg=data{1,j}(sig_start*fs_emg+1:end,:);
rms_tmp=get_rms(emg,window_len,step_len,fs_emg);
rms=reshape(rms_tmp,[1,numel(rms_tmp)]);
wl_tmp=get_wl(emg,window_len,step_len,fs_emg);
wl=reshape(wl_tmp,[1,numel(wl_tmp)]);
zc_tmp=get_zc(emg,window_len,step_len,zc_ssc_thresh,fs_emg);
zc=reshape(zc_tmp,[1,numel(zc_tmp)]);
ssc_tmp=get_ssc(emg,window_len,step_len,zc_ssc_thresh,fs_emg);
ssc=reshape(ssc_tmp,[1,numel(ssc_tmp)]);
feature(:,j)=[rms';wl';zc';ssc'];
end
predict_label=zeros(1,Ntrial);
for j=1:Ntrial
feature_test=feature(:,j);
feature_train=feature;
feature_train(:,j)=[];
label_test=label(j);
label_train=label;
label_train(j)=[];
dim=size(feature_train,2)-1;
[feature_train_norm,feature_test_norm]=feature_normalize(feature_train,feature_test,pca_active,dim);
mdl = ClassificationDiscriminant.fit(feature_train_norm',label_train);
predict_label(j) = predict(mdl, feature_test_norm');
end
accuracy(i,session)=mean(double((predict_label==label)));
end
end
average_accuracy=mean(mean(accuracy))
save(['../results/accuracy_',task_type,'.mat'],'accuracy');