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Dynamic Arm Gesture Recognition Using Spherical Angle Features and Hidden Markov Models
2015
Advances in Human-Computer Interaction
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
doi:10.1155/2015/785349
fatcat:af4ydw53ofb6ljzthewya2xifm