원문정보
초록
영어
This paper proposes a novel 3D wrist gesture recognition method as the human-machine interface for mobile robot control. A sequence of depth maps was extracted from Kinect sensor on wrist gestures of an operator and a wrist gesture was specified by a sequence of 24-directional codes, which represents 3D motions with feature values of a single type, including directional and depth information. HMM algorithm was used to recognize wrist gestures, in which a fully connected hidden Markov model was designed and trained by Viterbi method and Baum-Welch method and the forward-backward procedure was used for the likelihood evaluation. The performance evaluation considering various factors showed above 96% of recognition rate and higher recognition rate on wrist gestures with complex motions.
목차
1. Introduction
2. 3D Feature Values for Wrist Gestures
2.1 Definition of Wrist Gestures for Control
2.2 24-Diectional Codes for Wrist Gestures
3. Recognition of Wrist Gestures Using HMM
3.1 Design of Hidden Markov Model
3.2 Training of HMM
3.3 Evaluation and Recognition of HMM
4. Performance Evaluation
5. Conclusions
References