一种基于WPT和LVQ神经网络的手部动作识别方法  

Pattern Recognition of Hand Motions Based on WPT and LVQ

作　　者：罗志增[1] 熊静[1] 刘志宏[1] 

机构地区：[1]杭州电子科技大学智能控制与机器人研究所,杭州310018

出　　处：《模式识别与人工智能》2010年第5期695-700,共6页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(60874102);国家863计划项目(No.2008AA04Z212)资助

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(WPT)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.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.

关 键 词：表面肌电信号(SEMG) 小波包变换(WPT) 学习向量量化(LVQ) 神经网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]