work on #3 - viele Dinge versucht, leider fehlt irgendwie die richtige Idee...
This commit is contained in:
toni
@@ -6,21 +6,21 @@
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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/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_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_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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%measurements = dlmread('../../measurements/lgWear/recording_180bpm_4-4_177011641.csv', ';'); %
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%measurements = dlmread('../../measurements/lgWear/recording_180bpm_4-4_177064915.csv', ';'); %
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% SMARTWATCH G WATCH WEAR R ----> 100hz - 250hz
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@@ -41,163 +41,208 @@ measurements = dlmread('../../measurements/lgWear/recording_48bpm_4-4_176527527.
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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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files = dir(fullfile('../../measurements/lgWear/', '*.csv'));
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%files = dir(fullfile('../../measurements/wearR/', '*.csv'));
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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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for file = files'
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filename = [file.folder '/' file.name];
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measurements = dlmread(filename, ';');
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%put all together again
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m = [t_interp', t_interp', m_interp];
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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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figure(1);
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plot(m(:,1),m(:,3)) %x
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legend("x", "location", "eastoutside");
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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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figure(2);
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plot(m(:,1),m(:,4)) %y
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legend("y", "location", "eastoutside");
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%put all together again
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m = [t_interp', t_interp', m_interp];
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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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figure(1);
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plot(m(:,1),m(:,3)) %x
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legend("x", "location", "eastoutside");
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%waitforbuttonpress();
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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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%save timestamps
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timestamps = m(:,1);
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data = m(:,3); %only z
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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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%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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%save timestamps
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timestamps = m(:,1);
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data = m(:,3); %only z
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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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%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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%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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%wait until window is filled with new data
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if(mod(i,overlap) == 0)
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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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%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_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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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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[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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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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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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[peak_x, idx_x] = findpeaks(corr_x_pos, 'MinPeakHeight', 0.1,'MinPeakDistance', 100);
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[peak_y, idx_y] = findpeaks(corr_y_pos, 'MinPeakHeight', 0.1,'MinPeakDistance', 100);
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[peak_z, idx_z] = findpeaks(corr_z_pos, 'MinPeakHeight', 0.1,'MinPeakDistance', 100);
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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 = 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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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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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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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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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(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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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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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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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(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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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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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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%Find the most proper axis. We use 3 quantities: mean of corr.
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%value, sum of corr val. and number of peaks. Simple normalization
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%to get the axis that fullfills the quantities the most.
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idx_noZero_x = idx_x(lag_x(idx_x) ~= 0);
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idx_noZero_y = idx_y(lag_x(idx_y) ~= 0);
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idx_noZero_z = idx_z(lag_x(idx_z) ~= 0);
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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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corr_mean_x = geomean(corr_x(idx_noZero_x(corr_x(idx_noZero_x)>0)));
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corr_mean_y = geomean(corr_y(idx_noZero_y(corr_y(idx_noZero_y)>0)));
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corr_mean_z = geomean(corr_z(idx_noZero_z(corr_z(idx_noZero_z)>0)));
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corr_rms_x = rms(corr_x(idx_x(lag_x(idx_x) ~= 0)));
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corr_rms_y = rms(corr_y(idx_y(lag_y(idx_y) ~= 0)));
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corr_rms_z = rms(corr_z(idx_z(lag_z(idx_z) ~= 0)));
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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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num_peaks_x = 1;%length(idx_x);
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num_peaks_y = 1;%length(idx_y);
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num_peaks_z = 1;%length(idx_z);
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quantity_matrix = [corr_mean_x corr_mean_y corr_mean_z;
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corr_rms_x corr_rms_y corr_rms_z;
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num_peaks_x num_peaks_y num_peaks_z];
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quantity_matrix_percent(1,:) = quantity_matrix(1,:) ./ sum(quantity_matrix(1,:));
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quantity_matrix_percent(2,:) = quantity_matrix(2,:) ./ sum(quantity_matrix(2,:));
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quantity_matrix_percent(3,:) = quantity_matrix(3,:) ./ sum(quantity_matrix(3,:));
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quantity_factors = sum(quantity_matrix_percent) / 3;
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%quantity_x = quantity_factors(1);
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%quantity_y = quantity_factors(2);
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%quantity_z = quantity_factors(3);
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%choose axis with sum(corr) nearest to 0
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corr_sum_xyz = [sum(corr_x) sum(corr_y) sum(corr_z)];
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[~,idx_nearest_zero] = min(abs(corr_sum_xyz));
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if(idx_nearest_zero == 1)
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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(idx_nearest_zero == 2)
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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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%{
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if(quantity_x > quantity_y && quantity_x > quantity_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(quantity_y > quantity_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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%}
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if(isnan(window_mean_ts_diff) || isnan(window_mean_bpm))
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%do nothing
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else
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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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end
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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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%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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%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(0.1 * 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);
|
||||
|
||||
mean_final_bpm = mean(bpm_per_window);
|
||||
std_final_bpm = std(bpm_per_window);
|
||||
|
||||
fprintf('%s: mean = %f bpm (%f ms) stddev = %f bpm (%f ms)\n', strrep(regexprep(filename,'^.*recording_',''),'.txt',''), mean_final_bpm, mean_final_ms, std_final_bpm, std_final_ms);
|
||||
|
||||
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 = round(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)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -19,8 +19,12 @@ function idx = findFalseDetectedPeaks(idx_orig, lag_orig, corr_orig)
|
||||
%3
|
||||
%factor_matrix = [1:length(factor_peaks); factor_peaks]; %we need this since octave doesn't have a repelem only repelemS.
|
||||
%diff_peaks_new = repelems(diff_peaks ./ factor_peaks, factor_matrix);
|
||||
diff_peaks_new = repelem(diff_peaks ./ factor_peaks, factor_peaks);
|
||||
|
||||
if(diff_peaks > 1)
|
||||
diff_peaks_new = repelem(diff_peaks ./ factor_peaks, factor_peaks);
|
||||
else
|
||||
break;
|
||||
end
|
||||
|
||||
%4
|
||||
idx_new = round([idx_orig(1), cumsum(diff_peaks_new) + idx_orig(1)]');
|
||||
sum_peaks_new = sum(corr_orig(idx_new));
|
||||
|
||||
Reference in New Issue
Block a user