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ref #3 - methode zum auswählen der achse hinzugefügt. nutze qualitätsmerkmale RMS, Intersections und GeoMean um die beste Achse zu suchen.

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toni
2017-10-16 00:20:59 +02:00
parent 019dc63594
commit a1f002537c
1 changed files with 48 additions and 22 deletions
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70
matlab/AutoCorrMethodNew_Watch.m
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@@ -27,7 +27,7 @@
%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_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', ';');
@@ -41,8 +41,8 @@
%measurements = dlmread('../measurements/wearR/recording_180bpm_4-4_177064915.csv', ';'); *
files = dir(fullfile('../../measurements/lgWear/', '*.csv'));
%files = dir(fullfile('../../measurements/wearR/', '*.csv'));
%files = dir(fullfile('../../measurements/lgWear/', '*.csv'));
files = dir(fullfile('../../measurements/wearR/', '*.csv'));
for file = files'
@@ -104,24 +104,24 @@ for file = files'
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', 100);
[peak_y, idx_y] = findpeaks(corr_y_pos, 'MinPeakHeight', 0.1,'MinPeakDistance', 100);
[peak_z, idx_z] = findpeaks(corr_z_pos, 'MinPeakHeight', 0.1,'MinPeakDistance', 100);
[peak_x, idx_x_raw] = findpeaks(corr_x_pos, 'MinPeakHeight', 0.1,'MinPeakDistance', 50, 'MinPeakProminence', 0.1);
[peak_y, idx_y_raw] = findpeaks(corr_y_pos, 'MinPeakHeight', 0.1,'MinPeakDistance', 50, 'MinPeakProminence', 0.1);
[peak_z, idx_z_raw] = findpeaks(corr_z_pos, 'MinPeakHeight', 0.1,'MinPeakDistance', 50, 'MinPeakProminence', 0.1);
idx_x = sort(idx_x);
idx_y = sort(idx_y);
idx_z = sort(idx_z);
idx_x_raw = sort(idx_x_raw);
idx_y_raw = sort(idx_y_raw);
idx_z_raw = sort(idx_z_raw);
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);
idx_x = findFalseDetectedPeaks(idx_x_raw, lag_x, corr_x);
idx_y = findFalseDetectedPeaks(idx_y_raw, lag_y, corr_y);
idx_z = findFalseDetectedPeaks(idx_z_raw, 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
plot(lag_x, corr_x, lag_x(idx_x), corr_x(idx_x), 'r*', lag_x(idx_x_raw), corr_x(idx_x_raw), 'g*') %z
hold ("on")
m_label_ms = strcat(" mean ms: ", num2str(Xwindow_mean_ts_diff));
m_label_bpm = strcat(" mean bpm: ", num2str(Xwindow_mean_bpm));
@@ -133,7 +133,7 @@ for file = files'
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
plot(lag_y, corr_y, lag_y(idx_y), corr_y(idx_y), 'r*', lag_y(idx_y_raw), corr_y(idx_y_raw), 'g*') %z
hold ("on")
m_label_ms = strcat(" mean ms: ", num2str(Ywindow_mean_ts_diff));
m_label_bpm = strcat(" mean bpm: ", num2str(Ywindow_mean_bpm));
@@ -145,12 +145,28 @@ for file = files'
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
plot(lag_z, corr_z, lag_z(idx_z), corr_z(idx_z), 'r*', lag_z(idx_z_raw), corr_z(idx_z_raw), 'g*') %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");
%breakpoints dummy for testing
if(length(idx_x) > length(idx_x_raw))
a = 0; %breakpointdummy
end
if(length(idx_y) > length(idx_y_raw))
a = 0; %breakpointdummy
end
if(length(idx_z) > length(idx_z_raw))
a = 0; %breakpointdummy
end
%Find the most proper axis. We use 3 quantities: mean of corr.
%value, sum of corr val. and number of peaks. Simple normalization
@@ -170,10 +186,14 @@ for file = files'
num_peaks_x = 1;%length(idx_x);
num_peaks_y = 1;%length(idx_y);
num_peaks_z = 1;%length(idx_z);
num_intersection_x = getNumberOfIntersections(corr_x, lag_x, 0.2);
num_intersection_y = getNumberOfIntersections(corr_y, lag_y, 0.2);
num_intersection_z = getNumberOfIntersections(corr_z, lag_z, 0.2);
quantity_matrix = [corr_mean_x corr_mean_y corr_mean_z;
corr_rms_x corr_rms_y corr_rms_z;
num_peaks_x num_peaks_y num_peaks_z];
num_intersection_x num_intersection_y num_intersection_z];
quantity_matrix_percent(1,:) = quantity_matrix(1,:) ./ sum(quantity_matrix(1,:));
quantity_matrix_percent(2,:) = quantity_matrix(2,:) ./ sum(quantity_matrix(2,:));
@@ -181,11 +201,13 @@ for file = files'
quantity_factors = sum(quantity_matrix_percent) / 3;
%quantity_x = quantity_factors(1);
%quantity_y = quantity_factors(2);
%quantity_z = quantity_factors(3);
%TODO: Wenn ein quantity wert NaN ist, sind alle NaN...
quantity_x = quantity_factors(1);
quantity_y = quantity_factors(2);
quantity_z = quantity_factors(3);
%choose axis with sum(corr) nearest to 0
%{
corr_sum_xyz = [sum(corr_x) sum(corr_y) sum(corr_z)];
[~,idx_nearest_zero] = min(abs(corr_sum_xyz));
@@ -198,9 +220,13 @@ for file = files'
else
window_mean_ts_diff = Zwindow_mean_ts_diff;
window_mean_bpm = Zwindow_mean_bpm;
end
end
%}
%quantity_x = num_intersection_x;
%quantity_y = num_intersection_y;
%quantity_z = num_intersection_z;
%{
if(quantity_x > quantity_y && quantity_x > quantity_z)
window_mean_ts_diff = Xwindow_mean_ts_diff;
window_mean_bpm = Xwindow_mean_bpm;
@@ -211,7 +237,7 @@ for file = files'
window_mean_ts_diff = Zwindow_mean_ts_diff;
window_mean_bpm = Zwindow_mean_bpm;
end
%}
if(isnan(window_mean_ts_diff) || isnan(window_mean_bpm))
%do nothing