Human Motion Capture Data Retrieval and Segmentation Technology for Professional Sports Training

Abstract

Human motion capturing is frequently used in sports research. The focus of this research is to help the user choose an appropriate motion capture system for their experimental setup for sports activities that addresses the challenges of linear size and acceptable fits with non-linear futures. In this paper, the eigenvalue combination was used to represent different motion postures, so as to construct an index space related to the motion sequence; and according to this index space, fast and accurate motion retrieval was done; at the same time, a motion data segmentation method based on MVU nonlinear dimensionality reduction was proposed. MVU can overcome the shortcomings that the linear size is reduced and difficult to deal with nonlinear features, and can better fit human motion data, and achieve higher accuracy.