Following up on Jin's posts

While I agree with Jin that we may need more than one metric, we need to be careful about just throwing ad-hoc measures up without justification. DTW on the path makes sense because it compares the similarity of the overall action.

Some sort of statistical measure of acceleration or velocity might also be good, but we need to think about why/what. Some sort of smoothness measure would probably be good. We could take a normalized FFT of the gesture and compare the amount of energy above the mean (or perhaps median or mode).

Here is what I would propose:

1. DTW (lower is better score)
2. 1-(HMM probability)
3. "Smoothness" metric

We don't want to propose too many or make the score an amalgamation of too many pieces until we know which scoring methods correlate to good/bad motion (which in the scope of this semester, we will never know since we can't work with stroke patients.) What we do want to do is propose a good set of plausible metrics that can be implemented on the phone by the end of the month.

Jin just posted his results regarding the HMM... so here are some suggestions:

Create some synthetic motion traces that have motion only in one direction, x, y or z. See if your code can learn those motions.

Use a MATLAB HMM library for now to see if you can get better results.

Train on the path data (double integrated acceleration data). If it performs best in DTW it likely means there are unique features in the data stream that an HMM might pick up on.

Try creating an alternate feature stream by using a windowed FFT with some overlap. Performing this FFT across the acceleration data will give a sequence of FFTs that can be fed to the HMM. This is similar to what speech recognizers do for HMM speech recognition.