Low Cost Hand Gesture Learning and Recognition System Based on Hidden Markov Model
Focusing on recognizing some typical gestures based on 3-axis MEMS accelerometer to interact with an application of human-machine interactive game, the hand gesture problem is analyzed firstly, then theory basis of HMM is introduced. In order to obtain training gesture data for HMM, and also to provide a hardware basis for gesture recognition, a low cost data acquisition system hardware is researched and designed. The system can get the acceleration data of users gesture and transmit them wirelessly to a personal computer. In the hand gesture recognition approach, k-mean algorithms are applied to cluster and abstract the vector data from sensor. And then the quantized vectors are put into a hidden Markov model to learn and recognize users gestures. Finally the gesture recognition library is implemented in C# development environment, and is utilized in a humanmachine interactive game application. The results show that the typical gesture emerging in the game can be identified in a high rate, and the user can experience more interest and interaction.
Hidden markov model accelerometer gesture recognition virtual reality
Jinjun Rao Tongyue Gao Zhenbang Gong Zhen Jiang
Department of Precision Mechanical Engineering Shanghai University Shanghai, China
国际会议
Second International Symposium on Information Science and Engineering(第二届信息科学与工程国际会议)
上海
英文
433-438
2009-12-26（万方平台首次上网日期，不代表论文的发表时间）