期刊
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
卷 3, 期 2, 页码 169-174出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2007.09.002
关键词
Multi-resolution analysis; Wavelet design; Multi-channel signal classification; Support vector machine; Electromyography; Myoelectric prostheses
The study proposes a method for supervised classification of multi-channel surface electromyographic signals with the aim of controlling myoelectric prostheses. The representation space is based on the discrete wavelet transform (DWT) of each recorded EMG signal using unconstrained parameterization of the mother wavelet. The classification is performed with a support vector machine (SVM) approach in a multi-channel representation space. The mother wavelet is optimized with the criterion of minimum classification error, as estimated from the learning signal set. The method was applied to the classification of six hand movements with recording of the surface EMG from eight locations over the forearm. Misclassification rate in six subjects using the eight channels was (mean S.D.) 4.7 +/- 3.7% with the proposed approach while it was 11.1 +/- 10.0% without wavelet optimization (Daubechies wavelet). The DWT and SVM can be implemented with fast algorithms, thus, the method is suitable for real-time implementation. (C) 2007 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据