Signal processing for machine learning toolbox
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Has anyone used so far toolbox
I have an assignement to classify time series signals from my own data set.
I collected my own measurements with Myo armband and organized them according DataPreparation.m and PrepareData.m functions. I have 5 different classes of arm movements (drinking water, eating, moving glass, opening drawer and scratching). When I was labeling the data I used column vectors with a) 32 samples, b) 64 samples. I had 20 subjects and more than 50 labels per different kind of movement/ per subject.
I used the highpass filter from the toolbox and I calculated features with extractAllFeatures.m. I used ffd neural network with 18 neurons in hidden layer.
In both cases I got confusion matrix with clasiification accuracy of slightly over 50%.
Why I don't get better clasiification accuracy when the dataset is twice bigger (column vector of 64 samples compared with column vector of 32 samples)?
Should I make changes in calculation of features and in which direction?
I can attach all necessary files- confusion matrices, signal in time domain, changes of the functions of original toolbox etc.
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