Application Of Gabor Transform In The Classification Of Myoelectric Signal

Abdullah, Abdul Rahim and Mohd Ali, Nursabillilah and Tengku Zawawi, Tengku Nor Shuhada and Too, Jing Wei and Mohd Saad, Norhashimah (2019) Application Of Gabor Transform In The Classification Of Myoelectric Signal. Telkomnika (Telecommunication Computing Electronics and Control), 17 (2). pp. 873-881. ISSN 1693-6930

Text APPLICATIONOFGABORTRANSFORMINTHECLASSIFICATIONOFMYOELECTRICSIGNAL.PDF Download (682kB)

Abstract

In recent day, Electromyography (EMG) signal are widely applied in myoelectric control. Unfortunately, most of studies focused on the classification of EMG signals based on healthy subjects. Due to the lack of study in amputee subject, this paper aims to investigate the performance of healthy and amputee subjects for the classification of multiple hand movement types. In this work, Gabor transform (GT) is used to transform the EMG signal into time-frequency representation. Five time-frequency features are extracted from GT coefficient. Feature extraction is an effective way to reduce the dimensionality, as well as keeping the valuable information. Two popular classifiers namely k-nearest neighbor (KNN) and support vector machine (SVM) are employed for performance evaluation. The developed system is evaluated using the EMG data acquired from the publicy available NinaPro Database. The results revealed that the extracting GT features can achieve promising performance in the classification of EMG signals.

Item Type:	Article
Uncontrolled Keywords:	Electromyography, Gabor transform, K-nearest neighbor, Support vector machine, Myoelectric Signa
Divisions:	Faculty of Electronics and Computer Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	29 Jul 2020 14:29
Last Modified:	29 Jul 2020 14:29
URI:	http://eprints.utem.edu.my/id/eprint/24174
Statistic Details:	View Download Statistic

Actions (login required)

View Item