article

Feature reduction and selection for EMG signal classification

Authors:

Angkoon Phinyomark,

Pornchai Phukpattaranont,

Chusak LimsakulAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 39, Issue 8

Pages 7420 - 7431

https://doi.org/10.1016/j.eswa.2012.01.102

Published: 01 June 2012 Publication History

Abstract

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.

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 39, Issue 8

June, 2012

920 pages

ISSN:0957-4174

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Copyright © Elsevier Ltd © 2012.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 June 2012

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Cited By

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https://dl.acm.org/doi/10.1109/TVCG.2023.3308940
Deng ZLiu JXun YQin J(2024)IdentifierIDS: A Practical Voltage-Based Intrusion Detection System for Real In-Vehicle NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332702619(661-676)Online publication date: 1-Jan-2024
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Becman EDriemeier LLevin OSwinnen SForner-Cordero A(2024)A comparison study of myoelectric regression performances when estimating different types of joint kinematic dataExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124345254:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124345
Wu YLiang SMa YLi B(2024)Prediction and classification of sEMG-based pinch force between different fingersExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121635237:PCOnline publication date: 1-Mar-2024
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Gehlot NJena AVijayvargiya AKumar R(2024)Surface electromyography based explainable Artificial Intelligence fusion framework for feature selection of hand gesture recognitionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109119137:PAOnline publication date: 1-Nov-2024
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Liu YLi XYu HYang L(2024)A novel sEMG-based dynamic hand gesture recognition approach via residual attention networkMultimedia Tools and Applications10.1007/s11042-023-15748-583:3(9329-9349)Online publication date: 1-Jan-2024
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