Pattern recognition of hand gesture based on LVQ neutral network

Authors:
Xiuping Zheng;Yina Guo;Huaxia Wang
Affiliations:
Taiyuan University of Science and Technology, College of Electronic Information and Engineering, Taiyuan Shanxi, China;Taiyuan University of Science and Technology, College of Electronic Information and Engineering, Taiyuan Shanxi, China;Taiyuan University of Science and Technology, College of Electronic Information and Engineering, Taiyuan Shanxi, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Year:
2011

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Abstract

Pattern recognition of hand gesture is currently research hot spot. It is important for rehabilitation training, human-computer interaction, prosthetic control and sports science research etc. The brachioradialis, extensor digitorum communis, flexor carpi ulnaris muscle and flexor carpi radialis muscle as signal acquisition points; this paper captures four channel sEMG signals. Aiming at the sEMG signals of hand gesture, this paper uses the eigenvalue processed by RMS and MOV as training data samples, which is regarded as the input of LVQ neural network. Through training and learning samples, the better training result is got. The results of the study indicate that the LVQ neural network can effectively identify three action modes, all fingers, relax and middle, by adopting the four channel sEMG signals. The simple algorithm, small calculation and more than 89 percent recognition rate shows that it is a very good method of pattern recognition.