A Machine Learning Method for Identification of Key Body Poses in Cyclic Physical Exercises

Motion segmentation plays an important role in human motion analysis. Understanding the intrinsic features of human activities represents a challenge for modern science. Current solutions usually involve computationally demanding processing and achieve the best results using expensive, intrusive motion capture devices. In this paper, a simple, affordable and effective method for human motion segmentation and alignment is presented. The approach follows a two-step process: first, the most salient principal components are calculated in order to reduce the dimension of the input motion data. Then, candidates of key body poses (landmarks) are inferred using multi-class, supervised machine learning techniques from a set of training samples. Finally, cluster analysis is used to refine the result. Predictions are guaranteed to be invariant to the repetitiveness and symmetry of the performance. Results show the effectiveness of the proposed approach by comparing it against the Dynamic Time Warping algorithm and Hierarchical Aligned Cluster Analysis.