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Pattern Recognition of Gesture′s Surface Myoelectrogram Signal based on BP Neural Networks

YU Qing,YANG Jihai,CHEN Xiang,ZHANG Xu(Department of Electronic Science and Technology,University of Science and Technology of China,Hefei 230027,China)  
Sign language is widely used in our daily life.In this paper,some features are extracted,using surface myoelectrogram(SEMG)signals,which were generated on four muscles of forearm when gesture actions happened.Owing to stronger classification ability of BP networks and better separability of feature vectors(which include mean absolute value,AR model parameters,and zero-crossing rate) extracted from multichannel SEMG signals,the higher accuracy was obtained in the experiments.It shows that the method is efficient.
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