Fuzzy EMG classification for prosthesis control

被引:243
作者
Chan, FHY [1 ]
Yang, YS
Lam, FK
Zhang, YT
Parker, PA
机构
[1] Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Hong Kong, Peoples R China
[2] Chinese Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
[3] Univ New Brunswick, Dept Elect Engn, Fredericton, NB, Canada
来源
IEEE TRANSACTIONS ON REHABILITATION ENGINEERING | 2000年 / 8卷 / 03期
关键词
classification; electromyography (EMG); fuzzy logic; neural network; prosthesis;
D O I
10.1109/86.867872
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification, In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters, Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.
引用
收藏
页码:305 / 311
页数:7
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