Open Access Dataset and Toolbox of High-Density Surface Electromyogram Recordings 1.0.0

clear all;
close all;

subject='01';
session=1;

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;
        
[data,label]=load_pr(path,subject,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=mean(double((predict_label==label)));