Real-Time action detection and analysis in fencing footwork

This paper is devoted to real-time analysis of continuous footwork training routine in fencing. We propose a model-based adaptive filtering algorithm for accurate selection of segments of interest from a velocity signal acquired by the Kinect motion sensor. We remove false positives from the selected segments by extracting dedicated features and applying a SVM classifier. Finally, we compute parameters of the identified lunge actions, which constitute a feedback for the fencers. The proposed methods are evaluated on a dedicated dataset consisting of actions of eight fencers.