Multiple Hand Gesture Recognition Based on Surface EMG Signal

For realizing a multi-DOF myoelectric control system with a minimal number of sensors, research work on the recognition of twenty-four hand gestures based on two-channel surface EMG signal measured from human forearm muscles has been carried out. Third-order AR model coefficients, Mean Absolute Value and Mean Absolute Value ratio of the sEMG signal segments were used as features and the recognition of gestures was performed with a linear Bayesian classifier. Our experimental results show that the proposed two sensors setup and the sEMG signal processing and recognition methods are well suited for distinguishing hand gestures consisting of various wrist motions and single finger extension.

[1]  Charles Jorgensen,et al.  Gestures as Input: Neuroelectric Joysticks and Keyboards , 2003, IEEE Pervasive Comput..

[2]  Katsunori Shimohara,et al.  EMG pattern recognition by neural networks for multi fingers control , 1992, 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Junuk Chu,et al.  A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand , 2006, IEEE Transactions on Biomedical Engineering.

[4]  Richard F. ff. Weir,et al.  CHAPTER 32 DESIGN OF ARTIFICIAL ARMS AND HANDS FOR PROSTHETIC APPLICATIONS , 2005 .

[5]  K. Englehart,et al.  Classification of the myoelectric signal using time-frequency based representations. , 1999, Medical engineering & physics.

[6]  K. Nagata,et al.  A Classification Method of Hand Movements Using Multi Channel Electrode , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[7]  G.F. Inbar,et al.  Classification of finger activation for use in a robotic prosthesis arm , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Alcimar Soares,et al.  The Development of a Virtual Myoelectric Prosthesis Controlled by an EMG Pattern Recognition System Based on Neural Networks , 2004, Journal of Intelligent Information Systems.

[9]  R.Fff. Weir,et al.  A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  E. Lamounier,et al.  A VIRTUAL PROSTHESIS CONTROL BASED ON NEURAL NETWORKS FOR EMG PARTTERN CLASSIFICATION , 2005 .

[11]  Z Z Wang,et al.  [The study advances and prospects of processing surface EMG signal in prosthesis control]. , 2001, Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation.

[12]  Mu-Seong Mun,et al.  A real-time EMG pattern recognition based on linear-nonlinear feature projection for multifunction myoelectric hand , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..