Gesture recognition based on bayesian inference of distributed arm trajectory
Wan Khairunizam, Wan Ahmad, Dr.
Mohd Azri, Abd Aziz
Siti Khadijah, Za'aba, Dr.
Shahriman, Abu Bakar, Dr.
Azian Azamimi, Abdullah
Zuradzman, Mohd Razlan
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The use of human gestures has become an important part of human-computer interaction (HCI) and is receiving more and more attentions in the recent years, which allows users to communicate with machines in the natural way, and provides an attractive communication tool that could archive goals of interacting humans and computers. This paper introduces a gesture recognition system algorithm based on the probabilistic distribution of the arm trajectory. In this study, by examining the characteristic of the arm trajectory of a signer, motion features are selected and classified by using the fuzzy technique. In the recognition part, the aggregation of the fuzzy information is employed based on inference of Bayesian networks of the distributed arm trajectory. Experimental results show that the use of Bayesian inference in the proposed algorithm effectively works on the recognition of various gesture patterns.
- Mohd Azri Abd Aziz, Mr. 
- Mohd Nasir Ayob, Dr. 
- Zuradzman Mohamad Razlan, Assoc. Prof. Ir. Dr. 
- Shahriman Abu Bakar, Assoc. Prof. Ir. Ts. Dr. 
- Siti Khadijah Za'aba, Assoc. Prof. Ts. Dr. 
- School of Mechatronic Engineering (Articles) 
- Azian Azamimi Abdullah 
- Wan Khairunizam Wan Ahmad, Assoc. Prof. Ir. Ts. Dr.