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Advances in Human-Computer Interaction
Volume 2015, Article ID 785349, 7 pages
Research Article

Dynamic Arm Gesture Recognition Using Spherical Angle Features and Hidden Markov Models

Department of Compute Science, Kyonggi University, San 94-6, Yiui-Dong, Youngtong-Gu, Suwon-Si, Gyeonggi-Do 443-760, Republic of Korea

Received 7 June 2015; Accepted 21 October 2015

Academic Editor: Thomas Mandl

Copyright © 2015 Hyesuk Kim and Incheol Kim. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We introduce a vision-based arm gesture recognition (AGR) system using Kinect. The AGR system learns the discrete Hidden Markov Model (HMM), an effective probabilistic graph model for gesture recognition, from the dynamic pose of the arm joints provided by the Kinect API. Because Kinect’s viewpoint and the subject’s arm length can substantially affect the estimated 3D pose of each joint, it is difficult to recognize gestures reliably with these features. The proposed system performs the feature transformation that changes the 3D Cartesian coordinates of each joint into the 2D spherical angles of the corresponding arm part to obtain view-invariant and more discriminative features. We confirmed high recognition performance of the proposed AGR system through experiments with two different datasets.