Looking for some advice. I am playing around with an accelerometer, combined with the machine learning app in matlab. Clearly there are many ways to extract features from the received data, both in time and frequency domains. However, I have recently come across time-frequency analysis, specifically using wavelets.
Has anyone got any advice on using wavelet analysis for classifying accelerometer (or similar) data and the benefits of using it ? Or if indeed this would be a valid way of extracting features ? I'm not too sure what sort of data I should be extracting using this method ?
Thanks in advance.
Few points to note,
1)You can transform a number of samples (should be a dyadic number and depends on your sampling frequency) into wavelet domain and classify that data. (eg. if you transform 64 accelerometer samples then you also have 64 points in wavelet domain).
2) Apart from time-frequency information from wavelet transformation, wavelet transformation has sparsity property (https://en.wikipedia.org/wiki/Sparse_approximation) that would be useful for your classification model.
3) Also, you can try different wavelet basis functions (mother wavelets),
and try to figure out which basis is most suitable for your data. Maybe you can start with Haar basis function as it is more suitable to capture the singular behaviour of your data.