A Detail Study Of Wavelet Families For EMG Pattern Recognition

Too, Jing Wei and Abdullah, Abdul Rahim and Mohd Saad, Norhashimah and Mohd Ali, Nursabillilah and Musa, Haslinda (2018) A Detail Study Of Wavelet Families For EMG Pattern Recognition. International Journal Of Electrical And Computer Engineering (IJECE), 8 (6). 4221 -4229. ISSN 2088-8708

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Abstract

Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in electromyography (EMG) recognition system. However, the optimal mother wavelet selection remains a challenge to the application of WT in EMG signal processing. This paper presents a detail study for different mother wavelet function in discrete wavelet transform (DWT) and continuous wavelet transform (CWT). Additionally, the performance of different mother wavelet in DWT and CWT at different decomposition level and scale are also investigated. The mean absolute value (MAV) and wavelength (WL) features are extracted from each CWT and reconstructed DWT wavelet coefficient. A popular machine learning method, support vector machine (SVM) is employed to classify the different types of hand movements. The results showed that the most suitable mother wavelet in CWT are Mexican hat and Symlet 6 at scale 16 and 32, respectively. On the other hand, Symlet 4 and Daubechies 4 at the second decomposition level are found to be the optimal wavelet in DWT. From the analysis, we deduced that Symlet 4 at the second decomposition level in DWT is the most suitable mother wavelet for accurate classification of EMG signals of different hand movements.

Item Type:	Article
Uncontrolled Keywords:	Continuous wavelet transform, Discrete wavelet transform, .Electromyography, Mother wavelet, Pattern recognition
Subjects:	T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Electrical Engineering
Depositing User:	Mohd Hannif Jamaludin
Date Deposited:	08 Aug 2019 03:55
Last Modified:	30 Aug 2021 02:58
URI:	http://eprints.utem.edu.my/id/eprint/23008
Statistic Details:	View Download Statistic

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