Efficient body motion quantification and similarity evaluation using 3-D joints skeleton coordinates

Abstract

Evaluating whole-body motion is challenging because of the articulated nature of the skeleton structure. Each joint moves in an unpredictable way with uncountable possibilities of movements direction under the influence of one or many of its parent joints. This paper presents a method for human motion quantification via three-dimensional (3-D) body joints coordinates. We calculate a set of metrics that influence the joints movement considering the motion of its parent joints without requiring prior knowledge of the motion parameters. Only the raw joints coordinates data of a motion sequence are needed to automatically estimate the transformation matrix of the joints between frames. We also consider the angles between limbs as a fundamental factor to follow the joints directions. We classify the joints motion as global motion and local motion. The global motion represents the joint movement according to a fixed joint, and the local motion represents the joint movement according to its first parent joint. In order to evaluate the performance of the proposed method, we also propose a comparison algorithm between two skeletons motions based on the quantified metrics. We measured the comparative similarity between the 3-D joints coordinates on Microsoft Kinect V2 and UTD-MHAD dataset. User studies were conducted to evaluate the performance under different factors. Various results and comparisons have shown that our method effectively quantifies and evaluates the motion similarity.