Study Of EMG Feature Selection For Hand Motions Classification

Abdullah, Abdul Rahim and Mohd Saad, Norhashimah and Too, Jing Wei (2019) Study Of EMG Feature Selection For Hand Motions Classification. International Journal Of Human And Technology Interaction (IJHATI), 3 (1). pp. 19-24. ISSN 2590-3551

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Abstract

In recent days, electromyography (EMG) pattern recognition has becoming one of the major interests in rehabilitation area. However, EMG feature set normally consists of relevant, redundant and irrelevant features. To achieve high classification performance, the selection of potential features is critically important. Thus, this paper employs two recent feature selection methods namely competitive binary gray wolf optimizer (CBGWO) and modified binary tree growth algorithm (MBTGA) to evaluate the most informative EMG feature subset for efficient classification. The experimental results show that CBGWO and MBTGA are not only improves the classification performance, but also reduces the number of features.

Item Type:	Article
Uncontrolled Keywords:	Electromyography, Feature extraction, Time domain feature, Feature selection, Classification, Hand Motions Classification
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	29 Jul 2020 12:04
Last Modified:	29 Jul 2020 12:04
URI:	http://eprints.utem.edu.my/id/eprint/24153
Statistic Details:	View Download Statistic

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