[Research] Unit motions within time series (adaptative method)

Unit motions are independent segments of the time series that contains motion. And they are separated by intervals of "silence" (no motion). We assumed a fixed value for the length of this separation. That is, if the distance, in terms of frames, between two unit motions is more than 12 frames, we consider these two unit motions as different unit motions. However, this fixed value sometimes gives us incorrect unit motions (unit motions are were considered together or incorrectly split). A employed Statistics to adapt this value to the nature of the time series. First I evaluate the mean u and variance d of the lengths of segments with silence. Then, the minimum distance to consider independent unit motions is t = u + 0.7sqrt(d). I evaluate then the new unit motions for all the time series and make clustering of the unit motions. The new results are slightly better in some movements which seems to indicates the importance of setting values of external parameters with adaptive methods.

The new results are the following:

Hand-based Foot-based
Boxing 94.7369 5.2631
Hand clapping94.7368 5.2632
Hand waving 52.6316 47.3684
Jogging 26.8421 73.1579
Running 21.5790 78.4210
Walking 26.3159 73.6841

And this are the previous one:

Hand-based Foot-based
Boxing 89.5% 10.5%
Hand clapping89.5% 10.5%
Hand waving 78.9% 21.1%
Jogging 28.8% 71.1%
Running 21.1% 78.9%
Walking 22.3% 77.7%