71 lines
2.1 KiB
Matlab
71 lines
2.1 KiB
Matlab
display("Generating Features")
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#load and plot raw data
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#{
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data structure of cellarray classes:
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classes[1 to 5]
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samples[1 to trainsetPerClass]
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raw[1 to 3]
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Accel[start:pEnd]
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Gyro[start:pEnd]
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Magnet[start:pEnd]
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access the cells using classes{u}.samples{v}.raw{w}
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#}
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source("functions.m");
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source("settings.m")
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global setWindowSize;
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classes = {};
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classes = getRawTrainData();
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#outPath = "/home/toni/Documents/handygames/HandyGames/toni/img/raw"
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#plotData(classes, outPath);
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#calc and plot filtered data
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filteredClasses = filterData(classes);
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#outPath = "/home/toni/Documents/handygames/HandyGames/toni/img/filtered";
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#plotData(filteredClasses, outPath);
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#create sliding windows and add 6 additional signals pca and magnitude
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#{
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data structure of windowedClasses:
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classes[1 to 5]
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samples[1 to trainsetPerClass]
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raw[1 to 15] <--- 15 different signals
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3x WindowsAccel (X, Y, Z)
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Win, Win, Win, Win ... <--- single matrices
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3x WindowsGyro (X, Y, Z)
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Win, Win, Win, Win ... <--- single matrices
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3x WindowsMagnet (X, Y, Z)
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Win, Win, Win, Win ... <--- single matrices
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---> add 6 additional sensors: pca and magnitude
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3x WindowsPCA (Accel, Gyro, Magnet)
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Win, Win, Win, Win ... <--- single matrices
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3x WindowsMagnitude (Accel, Gyro, Magnet)
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Win, Win, Win, Win ... <--- single matrices
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access the cells using classes{u}.samples{v}.raw{signal, window)
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pca uses the eigenvector with the heighest eigenvalue as axis and projects the signals onto it for each sensor.
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magnitude is calculated using sqrt(x^2 + y^2 + z^2) for each sensor.
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#}
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windowedClasses = windowData(classes);
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#calculated features for the 5 signales (x, y, z, MG, PCA) of a sensor
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#{
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data structure of features
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[classLabel, binMeans, rms, psd, windowMean, windowSTD, windowVariance, windowKurtosis, windowIQR]
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#}
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features = featureCalculation(windowedClasses);
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#save features
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name = strcat("features_", num2str(setWindowSize),"_xyz_nomag_psd18.txt");
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save(name, 'features');
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display("saved features into features.txt");
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