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Mapping Fuzzy Membership Functions to Normal Distributions to Understand Boxing Motions

We present a novel way to generate fuzzy membership functions that allows us to identify human activities from motion capture data. The system is able to recognise seven different boxing stances simultaneously with an accuracy superior to a GMM-based classifier. Experimental results seem to indicate that a template can be learned and a stance identified in under 10 milliseconds, which may allow recognition in real-time.