Hand Motion Pattern Classifier Based on EMG Using Wavelet Packet Transform and LVQ Neural Networks

摘要：

In this paper, a novel electromyographic (EMG) motion pattern classifier using wavelet packet transform (WPT) and Learning Vector Quantization (LVQ) Neural Networks is proposed This motion pattern classifier can successfidly identify wrist extension, wrist flexion, hand extension and hand grasp, by measuring the surface EMG signals through two electrodes mounted on forearm extensor carpi ulnaris and flexor carpi ulnaris. The experimental results show that the proposed method achieves a 98％ recognition accuracy. Furthermore, via quantitative comparison with other neural networks classifiers,LVQ method has a better performance. Consequently,the classifier is applicable to myoelectric hand control of 2 degrees of freedom (DOF) because of its high recognition capability.

作者: Zhihong Liu Zhizeng Luo

作者单位: Intelligent Control and Robotics Research Institute,Hangzhou Dianzi University,Hangzhou,310018,China

会议类型: 国际会议

会议名称: 2008 IEEE International Symposium on IT in Medicine and Education(2008信息技术在医学和教育中的应用国际研讨会)(ITME 2008)

会议地点: 厦门

会议语种:英文

页码: 28-32

在线出版日期: 2008-12-12（万方平台首次上网日期，不代表论文的发表时间）