中文摘要：

针对表面肌电信号（SEMG）的手部动作识别，提出一种采用小波包变换（w门）和学习向量量化（LVQ）算法的神经网络分类器．对SEMG信号进行基于熵准则的最优小波包基分解得到各个节点分解系数，计算信号各个节点相应子频段的系数能量，归一化处理后的特征向量输入LVQ神经网络，实现基于SEMG的手部动作识别．实验结果表明，采取两路SEMG信号，该分类器能有效识别伸腕、屈腕、展拳和握拳4种动作模式，达到96％的识别率，能可靠应用于2个自由度肌电假手的控制．

英文摘要：

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 reeognition accuraey up to 96%. Consequently, the elassifier is applieable to myoeleetrie prosthetic hand control of 2 degrees of freedom （DOF） because of its superior recognition capability.