Human Motion Segmentation by Data Point Classification

Contemporary physiotherapy and rehabilitation practice uses subjective measures for motion evaluation and requires time-consuming supervision. Algorithms that can accurately segment patient movement would provide valuable data for progress tracking and on-line patient feedback. In this paper, we propose a two-class classifier approach to label each data point in the patient movement data as either a segment point or a non-segment point. The proposed technique was applied to 20 healthy subjects performing lower body rehabilitation exercises, and achieves a segmentation accuracy of 82%.