Classification of upper limb motion trajectories using shape features
Document Type
Article
Publication Date
1-1-2012
Abstract
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. © 2012 IEEE.
Publication Title
IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Recommended Citation
Zhou, H.,
Hu, H.,
Liu, H.,
&
Tang, J.
(2012).
Classification of upper limb motion trajectories using shape features.
IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews,
42(6), 970-982.
http://doi.org/10.1109/TSMCC.2011.2175380
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/11122