Classification Of Myoelectric Signal Using Spectrogram Based Window Selection

Abdullah, Abdul Rahim and Mohd Ali, Nursabillilah and Too, Jing Wei and Tengku Zawawi, Tengku Nor Shuhada and Mohd Saad, Norhashimah (2019) Classification Of Myoelectric Signal Using Spectrogram Based Window Selection. International Journal of Integrated Engineering, 11 (4). pp. 192-199. ISSN 2229-838X

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Abstract

This paper presents a study of the classification of myoelectric signal using spectrogram with different window sizes. The electromyography (EMG) signals of 40 hand movement types are collected from 10 subjects through NinaPro database. By employing spectrogram, the EMG signals are represented in time-frequency representation. Ten features are extracted from spectrogram for performance evaluation. In this study, two classifiers namely support vector machine (SVM) and linear discriminate analysis (LDA) are used to evaluate the performance of spectrogram features in the classification of EMG signals. To determine the best window size in spectrogram, three different Hanning window sizes are examined. The experimental results indicate that by applying spectrogram with optimize window size and LDA, the highest mean classification accuracy of 91.29% is obtained.

Item Type:	Article
Uncontrolled Keywords:	Electromyography, Spectrogram, Support vector machine, Linear discriminate analysis and Pattern recognition.
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	29 Jul 2020 12:50
Last Modified:	29 Jul 2020 12:50
URI:	http://eprints.utem.edu.my/id/eprint/24168
Statistic Details:	View Download Statistic

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