期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 39, 期 8, 页码 7420-7431出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2012.01.102
关键词
Feature extraction; Electromyography (EMG) signal; Linear discriminant analysis; Pattern recognition; Man-machine interface; Multifunction myoelectric control; Prosthesis
类别
资金
- Thailand Research Fund (TRF) through the Royal Golden Jubilee Ph.D. Program [PHD/0110/2550]
- NECTEC-PSU Center of Excellence for Rehabilitation Engineering, Faculty of Engineering, Prince of Songkla University
Feature extraction is a significant method to extract the useful information which is hidden in surface electromyography (EMG) signal and to remove the unwanted part and interferences. To be successful in classification of the EMG signal, selection of a feature vector ought to be carefully considered. However, numerous studies of the EMG signal classification have used a feature set that have contained a number of redundant features. In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed to be studied their properties. The results, which were verified by scatter plot of features, statistical analysis and classifier, indicated that most time domain features are superfluity and redundancy. They can be grouped according to mathematical property and information into four main types: energy and complexity, frequency, prediction model, and time-dependence. On the other hand, all frequency domain features are calculated based on statistical parameters of EMG power spectral density. Its performance in class separability viewpoint is not suitable for EMG recognition system. Recommendation of features to avoid the usage of redundant features for classifier in EMG signal classification applications is also proposed in this study. (C) 2012 Elsevier Ltd. All rights reserved.
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