Abstract To recognize hand motions based on the surface electromyography (SEMG), a neural network classifier is put forward by using wavelet packet transform (WPT) and learning vector quantization (LVQ) algorithms. The decomposition coefficients of each node for SEMG are gained by optimal wavelet package decomposition based on entropy criterion. The coefficient energy corresponding to sub-band of each node is calculated. Then the feature vectors via normalization are inputted into LVQ neural networks to realize recognition of hand motions. The experimental results show that four motion patterns including wrist extension, wrist flexion, hand extension and hand grasp can be identified by the classifier using two-channel SEMG with the recognition accuracy up to 96%. Consequently, the classifier is applicable to myoelectric prosthetic hand control of 2 degrees of freedom (DOF) because of its superior recognition capability.
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2010, Vol. 23 
