204 lines
7.9 KiB
Matlab
204 lines
7.9 KiB
Matlab
%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/2), "coeff");
|
|
[corr_y, lag_y] = xcov(m(i-window_size:i,4), (window_size/2), "coeff");
|
|
[corr_z, lag_z] = xcov(m(i-window_size:i,5), (window_size/2), "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)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|