Dynamic Arm Gesture Recognition Using Spherical Angle Features and Hidden Markov Models. (16th November 2015)

• Record Type: Journal Article
• Authors: Kim, Hyesuk; Kim, Incheol
• Other Names: Mandl Thomas Academic Editor.
• Is Part Of: Advances in human-computer interaction. Volume 2015(2015)
• Publication Date: 2015-11-16
• Subjects: Human-computer interaction -- Periodicals; Human-computer interaction; Periodicals; Electronic journals; 004.019
• DOI: 10.1155/2015/785349
• Languages: English
• ISSNs: 1687-5893

Abstract

Abstract : 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.