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Classification of Upper Limb Motion Trajectories Using Shape Features

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4 Author(s)
Huiyu Zhou ; School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, U.K. ; Huosheng Hu ; Honghai Liu ; Jinshan Tang

To understand and interpret human motion is a very active research area nowadays because of its importance in sports sciences, health care, and video surveillance. However, classification of human motion patterns is still a challenging topic because of the variations in kinetics and kinematics of human movements. In this paper, we present a novel algorithm for automatic classification of motion trajectories of human upper limbs. The proposed scheme starts from transforming 3-D positions and rotations of the shoulder/elbow/wrist joints into 2-D trajectories. Discriminative features of these 2-D trajectories are, then, extracted using a probabilistic shape-context method. Afterward, these features are classified using a k-means clustering algorithm. Experimental results demonstrate the superiority of the proposed method over the state-of-the-art techniques.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)  (Volume:42 ,  Issue: 6 )