|
|||||
|
|
| 基于小波系数熵的表面肌电信号识别 | |
| 引用本文： | 胡晓,李莉,任小梅,王志中.基于小波系数熵的表面肌电信号识别[J].中国医学物理学杂志,2007,24(3):212-214. |
| 作者姓名： | 胡晓 李莉 任小梅 王志中 |
| 作者单位： | 1. 广州大学,信息与机电工程学院,广东,广州,510006 2. 云南师范大学,计算机科学与信息技术学院,云南,昆明,650092 3. 上海交通大学,生物医学工程系,上海,200030 |
| 基金项目： | 国家重点基础研究发展计划(973计划) |
| 摘 要： | 目的:对表面肌电信号进行分类识别。方法:30名健康的志愿者参加数据采集。每名志愿者用右手臂完成两个动作:前臂内旋和前臂外旋。在每个动作中,采集一组表面EMG信号。总共获得30组内旋和30组外旋的表面EMG信号。然后,运用小波包系数熵构成特征向量,用Bayes决策对两种模式信号进行分类识别。结果:当信号长度达350ms后,正确识别率达到100%。结论:采用小波包系数熵可以有效地提取表面EMG信号的特征信息,达到控制前臂假肢的目的。 |
| 关 键 词： | 表面肌电信号 小波变换 熵 Bayes决策 |
| 文章编号： | 1005-202X（2007）03-0212-03 |
| 收稿时间： | 2006-11-09 |
| 修稿时间： | 2006年11月9日 |
Identification of Surface EMG Signal Based on Wavelet Packet Coefficient Entropy |
|
| HU Xiao,LI Li,REN Xiao-mei,WANG Zhi-zhong.Identification of Surface EMG Signal Based on Wavelet Packet Coefficient Entropy[J].Chinese Journal of Medical Physics,2007,24(3):212-214. | |
| Authors: | HU Xiao LI Li REN Xiao-mei WANG Zhi-zhong |
| Abstract: | Objective: To identify Surface EMG (sEMG) signals. Methods: 30 healthy volunteers joined data acquisition during which every volunteer carried out two forearm actions: forearm pronation (FP) and forearm supination(FS). A set of surface EMG signal was acquired during every forearm action. 30 sets of FP sEMG signals and 30 sets of FS sEMG signals were collected. Then, the feature vector consisted of wavelet packet coefficient entropy to identify FP sEMG signal and FS sEMG signal through Bayes decision. Results: The correct identification rate arrived to 100% when the lasted time of signal was 350ms or longer. Conclusions: Wavelet packet coefficient entropy is an effective parameter by which sEMG signals can be correctly classified. |
| Keywords: | surface EMG signal wavelet transform entropy bayse decision |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |