%We are using a threshold-based version for bpm estimation %Only the z axis of the acc is used %NOTE: depending on the measurement device we have a highly different sample rate. the smartwatches are not capable of providing a constant sample rate. the xsens on the other hand is able to do this. %load file provided by the sensor readout app %% SMARTWATCH LG WEAR ------> 100 hz - 1000hz measurements = dlmread('../../measurements/lgWear/PR_recording_80bpm_4-4_177596720.csv', ';'); %* %measurements = dlmread('../measurements/lgWear/recording_48bpm_4-4_176527527.csv', ';'); %measurements = dlmread('../measurements/lgWear/recording_48bpm_4-4_176606785.csv', ';'); %measurements = dlmread('../measurements/lgWear/recording_48bpm_4-4_176696356.csv', ';'); %measurements = dlmread('../measurements/lgWear/recording_48bpm_4-4_176820066.csv', ';'); % %measurements = dlmread('../measurements/lgWear/recording_48bpm_4-4_176931941.csv', ';'); %double %measurements = dlmread('../measurements/lgWear/recording_72bpm_4-4_176381633.csv', ';'); %measurements = dlmread('../measurements/lgWear/recording_72bpm_4-4_176453327.csv', ';'); %* %measurements = dlmread('../measurements/lgWear/recording_100bpm_4-4_176073767.csv', ';'); %measurements = dlmread('../measurements/lgWear/recording_100bpm_4-4_176165357.csv', ';'); %measurements = dlmread('../measurements/lgWear/recording_100bpm_4-4_176230146.csv', ';'); %measurements = dlmread('../measurements/lgWear/recording_100bpm_4-4_176284687.csv', ';'); %* %measurements = dlmread('../measurements/lgWear/recording_100bpm_4-4_177368860.csv', ';'); %(besonders) %measurements = dlmread('../measurements/lgWear/recording_180bpm_4-4_177011641.csv', ';'); %* %measurements = dlmread('../measurements/lgWear/recording_180bpm_4-4_177064915.csv', ';'); %* ganz schlimm genau die hälfte %% SMARTWATCH G WATCH WEAR R ----> 100hz - 250hz %measurements = dlmread('../measurements/wearR/PR_recording_80bpm_4-4_177596720.csv', ';'); %* %measurements = dlmread('../measurements/wearR/recording_48bpm_4-4_176527527.csv', ';'); %measurements = dlmread('../measurements/wearR/recording_48bpm_4-4_176606785.csv', ';'); %measurements = dlmread('../measurements/wearR/recording_48bpm_4-4_176696356.csv', ';'); %* %measurements = dlmread('../measurements/wearR/recording_48bpm_4-4_176820066.csv', ';'); %measurements = dlmread('../measurements/wearR/recording_48bpm_4-4_176931941.csv', ';'); %double %measurements = dlmread('../measurements/wearR/recording_72bpm_4-4_176381633.csv', ';'); %measurements = dlmread('../measurements/wearR/recording_72bpm_4-4_176453327.csv', ';'); %measurements = dlmread('../measurements/wearR/recording_100bpm_4-4_176073767.csv', ';'); * %measurements = dlmread('../measurements/wearR/recording_100bpm_4-4_176165357.csv', ';'); %measurements = dlmread('../measurements/wearR/recording_100bpm_4-4_176230146.csv', ';'); %* 72? %measurements = dlmread('../measurements/wearR/recording_100bpm_4-4_176284687.csv', ';'); %*48? %measurements = dlmread('../measurements/wearR/recording_100bpm_4-4_177368860.csv', ';'); %(besonders) %measurements = dlmread('../measurements/wearR/recording_180bpm_4-4_177011641.csv', ';'); * %measurements = dlmread('../measurements/wearR/recording_180bpm_4-4_177064915.csv', ';'); * %draw the raw acc data m_idx = []; m_idx = (measurements(:,2)==2); m = measurements(m_idx, :); t = m(:,1); %timestamps %Interpolate to generate a constant sample rate to 250hz (4ms per sample) sample_rate_ms = 4;%ms t_interp = t(1):sample_rate_ms:t(length(t)); m_interp = interp1(t,m(:,3:5),t_interp); %put all together again m = [t_interp', t_interp', m_interp]; figure(1); plot(m(:,1),m(:,3)) %x legend("x", "location", "eastoutside"); figure(2); plot(m(:,1),m(:,4)) %y legend("y", "location", "eastoutside"); figure(3); plot(m(:,1),m(:,5)) %z legend("z", "location", "eastoutside"); %waitforbuttonpress(); %save timestamps timestamps = m(:,1); data = m(:,3); %only z %TODO: Different window sizes for periods under 16.3 s window_size = 4096; %about 2 seconds using 2000hz, 16.3 s using 250hz overlap = 256; bpm_per_window_ms = []; bpm_per_window = []; for i = window_size+1:length(data) %wait until window is filled with new data if(mod(i,overlap) == 0) %measure periodicity of window and use axis with best periodicity [corr_x, lag_x] = xcov(m(i-window_size:i,3), (window_size/4), "coeff"); [corr_y, lag_y] = xcov(m(i-window_size:i,4), (window_size/4), "coeff"); [corr_z, lag_z] = xcov(m(i-window_size:i,5), (window_size/4), "coeff"); corr_x_pos = corr_x; corr_y_pos = corr_y; corr_z_pos = corr_z; corr_x_pos(corr_x_pos<0)=0; corr_y_pos(corr_y_pos<0)=0; corr_z_pos(corr_z_pos<0)=0; [peak_x, idx_x] = findpeaks(corr_x_pos, "MinPeakHeight", 0.1,"MinPeakDistance", 50); [peak_y, idx_y] = findpeaks(corr_y_pos, "MinPeakHeight", 0.1,"MinPeakDistance", 50); [peak_z, idx_z] = findpeaks(corr_z_pos, "MinPeakHeight", 0.1,"MinPeakDistance", 50); mean_x = length(peak_x); mean_y = length(peak_y); mean_z = length(peak_z); waitforbuttonpress(); idx_x = sort(idx_x); idx_y = sort(idx_y); idx_z = sort(idx_z); idx_x = findFalseDetectedPeaks(idx_x, lag_x, corr_x); idx_y = findFalseDetectedPeaks(idx_y, lag_y, corr_y); idx_z = findFalseDetectedPeaks(idx_z, lag_z, corr_z); Xwindow = m(i-window_size:i,3); Xwindow_mean_ts_diff = mean(diff(lag_x(idx_x) * sample_rate_ms)); %2.5 ms is the time between two samples at 400hz Xwindow_mean_bpm = (60000 / (Xwindow_mean_ts_diff)); figure(11); plot(lag_x, corr_x, lag_x(idx_x), corr_x(idx_x), ".r") %z hold ("on") m_label_ms = strcat(" mean ms: ", num2str(Xwindow_mean_ts_diff)); m_label_bpm = strcat(" mean bpm: ", num2str(Xwindow_mean_bpm)); title(strcat(" ", m_label_ms, " ", m_label_bpm)); hold ("off"); Ywindow = m(i-window_size:i,4); Ywindow_mean_ts_diff = mean(diff(lag_y(idx_y) * sample_rate_ms)); Ywindow_mean_bpm = (60000 / (Ywindow_mean_ts_diff)); figure(12); plot(lag_y, corr_y, lag_y(idx_y), corr_y(idx_y), ".r") %z hold ("on") m_label_ms = strcat(" mean ms: ", num2str(Ywindow_mean_ts_diff)); m_label_bpm = strcat(" mean bpm: ", num2str(Ywindow_mean_bpm)); title(strcat(" ", m_label_ms, " ", m_label_bpm)); hold ("off"); Zwindow = m(i-window_size:i,5); Zwindow_mean_ts_diff = mean(diff(lag_z(idx_z)* sample_rate_ms)); Zwindow_mean_bpm = (60000 / (Zwindow_mean_ts_diff)); figure(13); plot(lag_z, corr_z, lag_z(idx_z), corr_z(idx_z), ".r") %z hold ("on") m_label_ms = strcat(" mean ms: ", num2str(Zwindow_mean_ts_diff)); m_label_bpm = strcat(" mean bpm: ", num2str(Zwindow_mean_bpm)); title(strcat(" ", m_label_ms, " ", m_label_bpm)); hold ("off"); %window = data(i-window_size:i,:); %window_timestamps = timestamps(i-window_size:i,:); %choose axis with most points if(mean_x > mean_y && mean_x > mean_z) window_mean_ts_diff = Xwindow_mean_ts_diff; window_mean_bpm = Xwindow_mean_bpm; elseif(mean_y > mean_z) window_mean_ts_diff = Ywindow_mean_ts_diff; window_mean_bpm = Ywindow_mean_bpm; else window_mean_ts_diff = Zwindow_mean_ts_diff; window_mean_bpm = Zwindow_mean_bpm; endif bpm_per_window_ms = [bpm_per_window_ms, window_mean_ts_diff]; bpm_per_window = [bpm_per_window, window_mean_bpm]; %TODO: choose axis with highest correlation values at peaks %TODO: if correlation value is lower then a treshhold, we are not conducting TODO: change to a real classification instead of a treshhold. endif end %TODO: smooth the results using a moving avg or 1d kalman filter.(transition for kalman could be adding the last measured value) %remove the first 40% of the results, due to starting delays while recording. number_to_remove = abs(0.4 * length(bpm_per_window_ms)); num_all = length(bpm_per_window_ms); bpm_per_window_ms = bpm_per_window_ms(number_to_remove:num_all); bpm_per_window = bpm_per_window(number_to_remove:num_all); mean_final_ms = mean(bpm_per_window_ms) std_final_ms = std(bpm_per_window_ms) mean_final_bpm = mean(bpm_per_window) std_final_bpm = std(bpm_per_window)