Closes #1 - Converted Octave code into matlab code.
This commit is contained in:
toni
204
matlab/AutoCorrMethodNew_Watch.m
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204
matlab/AutoCorrMethodNew_Watch.m
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%We are using a threshold-based version for bpm estimation
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%Only the z axis of the acc is used
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%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.
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%load file provided by the sensor readout app
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% SMARTWATCH LG WEAR ------> 100 hz - 1000hz
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%measurements = dlmread('../../measurements/lgWear/PR_recording_80bpm_4-4_177596720.csv', ';'); %*
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measurements = dlmread('../../measurements/lgWear/recording_48bpm_4-4_176527527.csv', ';');
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%measurements = dlmread('../../measurements/lgWear/recording_48bpm_4-4_176606785.csv', ';');
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%measurements = dlmread('../../measurements/lgWear/recording_48bpm_4-4_176696356.csv', ';');
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%measurements = dlmread('../../measurements/lgWear/recording_48bpm_4-4_176820066.csv', ';'); %
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%measurements = dlmread('../../measurements/lgWear/recording_48bpm_4-4_176931941.csv', ';'); %double
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%measurements = dlmread('../../measurements/lgWear/recording_72bpm_4-4_176381633.csv', ';');
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%measurements = dlmread('../../measurements/lgWear/recording_72bpm_4-4_176453327.csv', ';'); %*
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%measurements = dlmread('../../measurements/lgWear/recording_100bpm_4-4_176073767.csv', ';');
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%measurements = dlmread('../../measurements/lgWear/recording_100bpm_4-4_176165357.csv', ';');
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%measurements = dlmread('../../measurements/lgWear/recording_100bpm_4-4_176230146.csv', ';');
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%measurements = dlmread('../../measurements/lgWear/recording_100bpm_4-4_176284687.csv', ';'); %*
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%measurements = dlmread('../../measurements/lgWear/recording_100bpm_4-4_177368860.csv', ';'); %(besonders)
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%measurements = dlmread('../../measurements/lgWear/recording_180bpm_4-4_177011641.csv', ';'); %*
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%measurements = dlmread('../../measurements/lgWear/recording_180bpm_4-4_177064915.csv', ';'); %* ganz schlimm genau die hälfte
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% SMARTWATCH G WATCH WEAR R ----> 100hz - 250hz
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%measurements = dlmread('../measurements/wearR/PR_recording_80bpm_4-4_177596720.csv', ';'); %*
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%measurements = dlmread('../measurements/wearR/recording_48bpm_4-4_176527527.csv', ';');
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%measurements = dlmread('../measurements/wearR/recording_48bpm_4-4_176606785.csv', ';');
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%measurements = dlmread('../measurements/wearR/recording_48bpm_4-4_176696356.csv', ';'); %*
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%measurements = dlmread('../measurements/wearR/recording_48bpm_4-4_176820066.csv', ';');
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%measurements = dlmread('../measurements/wearR/recording_48bpm_4-4_176931941.csv', ';'); %double
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%measurements = dlmread('../measurements/wearR/recording_72bpm_4-4_176381633.csv', ';');
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%measurements = dlmread('../measurements/wearR/recording_72bpm_4-4_176453327.csv', ';');
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%measurements = dlmread('../measurements/wearR/recording_100bpm_4-4_176073767.csv', ';'); *
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%measurements = dlmread('../measurements/wearR/recording_100bpm_4-4_176165357.csv', ';');
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%measurements = dlmread('../measurements/wearR/recording_100bpm_4-4_176230146.csv', ';'); %* 72?
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%measurements = dlmread('../measurements/wearR/recording_100bpm_4-4_176284687.csv', ';'); %*48?
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%measurements = dlmread('../measurements/wearR/recording_100bpm_4-4_177368860.csv', ';'); %(besonders)
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%measurements = dlmread('../measurements/wearR/recording_180bpm_4-4_177011641.csv', ';'); *
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%measurements = dlmread('../measurements/wearR/recording_180bpm_4-4_177064915.csv', ';'); *
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%draw the raw acc data
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m_idx = [];
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m_idx = (measurements(:,2)==2);
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m = measurements(m_idx, :);
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%Interpolate to generate a constant sample rate to 250hz (4ms per sample)
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sample_rate_ms = 4;%ms
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[~, m_unique_idx] = unique(m(:,1)); %matlab requirs unique timestamps for interp
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m = m(m_unique_idx, :);
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t = m(:,1); %timestamps
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t_interp = t(1):sample_rate_ms:t(length(t));
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m_interp = interp1(t,m(:,3:5),t_interp);
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%put all together again
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m = [t_interp', t_interp', m_interp];
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figure(1);
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plot(m(:,1),m(:,3)) %x
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legend("x", "location", "eastoutside");
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figure(2);
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plot(m(:,1),m(:,4)) %y
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legend("y", "location", "eastoutside");
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figure(3);
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plot(m(:,1),m(:,5)) %z
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legend("z", "location", "eastoutside");
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%waitforbuttonpress();
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%save timestamps
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timestamps = m(:,1);
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data = m(:,3); %only z
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%TODO: Different window sizes for periods under 16.3 s
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window_size = 4096; %about 2 seconds using 2000hz, 16.3 s using 250hz
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overlap = 256;
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bpm_per_window_ms = [];
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bpm_per_window = [];
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for i = window_size+1:length(data)
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%wait until window is filled with new data
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if(mod(i,overlap) == 0)
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%measure periodicity of window and use axis with best periodicity
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[corr_x, lag_x] = xcov(m(i-window_size:i,3), (window_size/4), "coeff");
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[corr_y, lag_y] = xcov(m(i-window_size:i,4), (window_size/4), "coeff");
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[corr_z, lag_z] = xcov(m(i-window_size:i,5), (window_size/4), "coeff");
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corr_x_pos = corr_x;
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corr_y_pos = corr_y;
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corr_z_pos = corr_z;
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corr_x_pos(corr_x_pos<0)=0;
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corr_y_pos(corr_y_pos<0)=0;
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corr_z_pos(corr_z_pos<0)=0;
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[peak_x, idx_x] = findpeaks(corr_x_pos, "MinPeakHeight", 0.1,"MinPeakDistance", 50);
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[peak_y, idx_y] = findpeaks(corr_y_pos, "MinPeakHeight", 0.1,"MinPeakDistance", 50);
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[peak_z, idx_z] = findpeaks(corr_z_pos, "MinPeakHeight", 0.1,"MinPeakDistance", 50);
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mean_x = length(peak_x);
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mean_y = length(peak_y);
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mean_z = length(peak_z);
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waitforbuttonpress();
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idx_x = sort(idx_x);
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idx_y = sort(idx_y);
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idx_z = sort(idx_z);
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idx_x = findFalseDetectedPeaks(idx_x, lag_x, corr_x);
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idx_y = findFalseDetectedPeaks(idx_y, lag_y, corr_y);
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idx_z = findFalseDetectedPeaks(idx_z, lag_z, corr_z);
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Xwindow = m(i-window_size:i,3);
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Xwindow_mean_ts_diff = mean(diff(lag_x(idx_x) * sample_rate_ms)); %2.5 ms is the time between two samples at 400hz
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Xwindow_mean_bpm = (60000 / (Xwindow_mean_ts_diff));
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figure(11);
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plot(lag_x, corr_x, lag_x(idx_x), corr_x(idx_x), 'r*') %z
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hold ("on")
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m_label_ms = strcat(" mean ms: ", num2str(Xwindow_mean_ts_diff));
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m_label_bpm = strcat(" mean bpm: ", num2str(Xwindow_mean_bpm));
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title(strcat(" ", m_label_ms, " ", m_label_bpm));
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hold ("off");
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Ywindow = m(i-window_size:i,4);
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Ywindow_mean_ts_diff = mean(diff(lag_y(idx_y) * sample_rate_ms));
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Ywindow_mean_bpm = (60000 / (Ywindow_mean_ts_diff));
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figure(12);
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plot(lag_y, corr_y, lag_y(idx_y), corr_y(idx_y), 'r*') %z
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hold ("on")
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m_label_ms = strcat(" mean ms: ", num2str(Ywindow_mean_ts_diff));
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m_label_bpm = strcat(" mean bpm: ", num2str(Ywindow_mean_bpm));
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title(strcat(" ", m_label_ms, " ", m_label_bpm));
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hold ("off");
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Zwindow = m(i-window_size:i,5);
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Zwindow_mean_ts_diff = mean(diff(lag_z(idx_z)* sample_rate_ms));
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Zwindow_mean_bpm = (60000 / (Zwindow_mean_ts_diff));
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figure(13);
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plot(lag_z, corr_z, lag_z(idx_z), corr_z(idx_z), 'r*') %z
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hold ("on")
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m_label_ms = strcat(" mean ms: ", num2str(Zwindow_mean_ts_diff));
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m_label_bpm = strcat(" mean bpm: ", num2str(Zwindow_mean_bpm));
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title(strcat(" ", m_label_ms, " ", m_label_bpm));
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hold ("off");
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%window = data(i-window_size:i,:);
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%window_timestamps = timestamps(i-window_size:i,:);
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%choose axis with most points
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if(mean_x > mean_y && mean_x > mean_z)
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window_mean_ts_diff = Xwindow_mean_ts_diff;
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window_mean_bpm = Xwindow_mean_bpm;
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elseif(mean_y > mean_z)
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window_mean_ts_diff = Ywindow_mean_ts_diff;
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window_mean_bpm = Ywindow_mean_bpm;
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else
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window_mean_ts_diff = Zwindow_mean_ts_diff;
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window_mean_bpm = Zwindow_mean_bpm;
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end
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bpm_per_window_ms = [bpm_per_window_ms, window_mean_ts_diff];
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bpm_per_window = [bpm_per_window, window_mean_bpm];
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%TODO: choose axis with highest correlation values at peaks
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%TODO: if correlation value is lower then a treshhold, we are not conducting TODO: change to a real classification instead of a treshhold.
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end
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end
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%TODO: smooth the results using a moving avg or 1d kalman filter.(transition for kalman could be adding the last measured value)
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%remove the first 40% of the results, due to starting delays while recording.
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number_to_remove = round(abs(0.4 * length(bpm_per_window_ms)));
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num_all = length(bpm_per_window_ms);
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bpm_per_window_ms = bpm_per_window_ms(number_to_remove:num_all);
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bpm_per_window = bpm_per_window(number_to_remove:num_all);
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mean_final_ms = mean(bpm_per_window_ms)
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std_final_ms = std(bpm_per_window_ms)
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mean_final_bpm = mean(bpm_per_window)
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std_final_bpm = std(bpm_per_window)
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50
matlab/findFalseDetectedPeaks.m
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matlab/findFalseDetectedPeaks.m
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%TODO: Find false detected peaks. This could be a not detected or an additional detected peak. Simple algorithm:
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% 1) Choose smallest diff(peaks) value smallest_peak (e.g. 48, 97, 49, [45], ..)
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% 2) Divide all diff_peaks values with smallest_peak to get factor_peaks (e.g. 1.1, 1.8, 1.2, 1, ..) and to whole numbers (e.g. 1, [2], 1, 1, ..)
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% 3) Divide diff_peaks / factor_peaks and split (e.g. 48, [48.5, 48.5], 49, 45, ..) diff_peaks_new
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% 4) Start at first peak and cumsum the correlation values at the lag positions given by diff_peaks_new to sum_peaks_new and sum_peaks for diff_peaks
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% 5) If sum_diff_new > sum_diff finish, else remove smallest_peak from diff(peaks) and goto 1)
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function idx = findFalseDetectedPeaks(idx_orig, lag_orig, corr_orig)
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idx = idx_orig;
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while 1
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%1
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diff_peaks = diff(lag_orig(idx_orig));
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[smallest_peak, smallest_peak_idx] = min(diff_peaks);
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%2
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factor_peaks = round(diff_peaks / smallest_peak);
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%3
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%factor_matrix = [1:length(factor_peaks); factor_peaks]; %we need this since octave doesn't have a repelem only repelemS.
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%diff_peaks_new = repelems(diff_peaks ./ factor_peaks, factor_matrix);
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diff_peaks_new = repelem(diff_peaks ./ factor_peaks, factor_peaks);
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%4
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idx_new = round([idx_orig(1), cumsum(diff_peaks_new) + idx_orig(1)]');
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sum_peaks_new = sum(corr_orig(idx_new));
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sum_peaks = sum(corr_orig(idx_orig));
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%5
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if(sum_peaks_new < sum_peaks)
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% TODO: idx + 1 is not always the correct one. if diff is very
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% small, the false peak could be left or right from the real peak.
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% Solution: Remove peak with lowest correlation value in
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% this area
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if(corr_orig(idx_orig(smallest_peak_idx)) > corr_orig(idx_orig(smallest_peak_idx + 1)))
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idx_orig(smallest_peak_idx + 1) = [];
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else
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idx_orig(smallest_peak_idx) = [];
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end
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else
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idx = idx_new;
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end
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if (sum_peaks_new >= sum_peaks || length(idx_orig) <= 1)
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break;
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end
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%TODO: Warning: could this be an infinite loop?
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end
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end
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Block a user