Part B: Multi Template Matching
In many cases, a cyclic data series may represent two different actions, mixed together in an unknown way. For instance, a subject jumping up and down. If their landing surface varies between a soft and hard platform, the accelerometer data from the experiment will reflect it, but subtlety. Even if a template for each action is identified, it will likely match against the opposite action, i.e. using action A as a template will produce matches in sections where action B is performed (false positives). The Template Matching - Discriminator calculation performs correlations independently for action A and action B, then examines their matches to determine which one matches better. The better match presides, and the worse match is discarded. The result is a series of matches for action A and a series of matches for action B. The matches do not overlap.
Step 1: Collecting cyclical data with
two distinct patterns