Abstract
This paper presents a study of using surface electromyography (SEMG) for hand gesture recognitions. A SEMG acquisition system is discussed in this paper. The characteristics of SEMG are introduced and the transformation characteristics are analysed as well. Then the selected gestures and the location for the SEMG sensor are determined. The process of pattern recognition and the reason of selecting SVM classifier are presented in detail, and the kernel function selection of SVM is discussed. Three optimisation methods of parameters are compared using the cross-validation method. Finally, the parameters obtained by the genetic algorithm are used to test the model and the recognition performance.
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