Grading Tai Chi Performance in Competition with RGBD Sensors

Abstract

In order to grade objectively, referees of Tai Chi practices always have to be very concentrated on every posture of the performer. This makes the referees easy to be fatigue and thus grade with occasional mistakes. In this paper, we propose using Kinect sensors to grade automatically. Firstly, we record the joint movement of the performer skeleton. Then we adopt the joint differences both temporally and spatially to model the joint dynamics and configuration. We apply Principal Component Analysis (PCA) to the joint differences in order to reduce redundancy and noise. We then employ non-parametric Nave-Bayes-Nearest-Neighbor (NBNN) as a classifier to recognize the multiple categories of Tai Chi forms. To give grade of each form, we study the grading criteria and convert them into decision on angles or distances between vectors. Experiments on several Tai Chi forms show the feasibility of our method.