Automatic EMG feature evaluation for controlling a prosthetic hand using a supervised feature mining method: An intelligent approach

Han Pang Huang*, Yi Hung Liu, Chun Shin Wong

*此作品的通信作者

研究成果: Conference article同行評審

36 引文 斯高帕斯(Scopus)

摘要

Electromyograph (EMG) has the properties of large variations and nonstationarity. There are two issues in the classification of EMG signals. One is the feature selection, and the other is the classifier design. Subject to the first issue, we propose a supervised feature mining (SFM) method, which is an intelligent approach based on genetic algorithms (GAs), fuzzy measure, and domain knowledge on pattern recognition. The SFM can find the optimal EMG feature subset automatically and remove the redundant from a large amount of feature candidates without taking trial-and-error. In the experiments, all feature candidates and optimal feature subset are conducted to demonstrate the validity of the proposed SFM. Moreover, experimental results show that the optimal EMG feature subset obtained from SFM can obtain higher classification rates compared with using all feature candidates by K-NN method.

原文English
頁(從 - 到)220-225
頁數6
期刊Proceedings - IEEE International Conference on Robotics and Automation
1
出版狀態Published - 2003
事件2003 IEEE International Conference on Robotics and Automation - Taipei, Taiwan
持續時間: 14 9月 200319 9月 2003

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