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Classification Of Myoelectric Signal Using Spectrogram Based Window Selection

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 ti...

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Main Authors: Abdullah, Abdul Rahim, Mohd Ali, Nursabillilah, Too, Jing Wei, Tengku Zawawi, Tengku Nor Shuhada, Mohd Saad, Norhashimah
Format: Article
Language:English
Published: Penerbit UTHM 2019
Online Access:http://eprints.utem.edu.my/id/eprint/24168/2/CLASSIFICATION%20OF%20MYOELECTRIC%20SIGNAL.PDF
http://eprints.utem.edu.my/id/eprint/24168/
https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/4694/2991
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spelling my.utem.eprints.241682020-07-29T12:50:50Z http://eprints.utem.edu.my/id/eprint/24168/ Classification Of Myoelectric Signal Using Spectrogram Based Window Selection Abdullah, Abdul Rahim Mohd Ali, Nursabillilah Too, Jing Wei Tengku Zawawi, Tengku Nor Shuhada Mohd Saad, Norhashimah 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. Penerbit UTHM 2019 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/24168/2/CLASSIFICATION%20OF%20MYOELECTRIC%20SIGNAL.PDF 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 https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/4694/2991 10.30880/ijie.2019.11.04.021
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description 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.
format Article
author Abdullah, Abdul Rahim
Mohd Ali, Nursabillilah
Too, Jing Wei
Tengku Zawawi, Tengku Nor Shuhada
Mohd Saad, Norhashimah
spellingShingle Abdullah, Abdul Rahim
Mohd Ali, Nursabillilah
Too, Jing Wei
Tengku Zawawi, Tengku Nor Shuhada
Mohd Saad, Norhashimah
Classification Of Myoelectric Signal Using Spectrogram Based Window Selection
author_facet Abdullah, Abdul Rahim
Mohd Ali, Nursabillilah
Too, Jing Wei
Tengku Zawawi, Tengku Nor Shuhada
Mohd Saad, Norhashimah
author_sort Abdullah, Abdul Rahim
title Classification Of Myoelectric Signal Using Spectrogram Based Window Selection
title_short Classification Of Myoelectric Signal Using Spectrogram Based Window Selection
title_full Classification Of Myoelectric Signal Using Spectrogram Based Window Selection
title_fullStr Classification Of Myoelectric Signal Using Spectrogram Based Window Selection
title_full_unstemmed Classification Of Myoelectric Signal Using Spectrogram Based Window Selection
title_sort classification of myoelectric signal using spectrogram based window selection
publisher Penerbit UTHM
publishDate 2019
url http://eprints.utem.edu.my/id/eprint/24168/2/CLASSIFICATION%20OF%20MYOELECTRIC%20SIGNAL.PDF
http://eprints.utem.edu.my/id/eprint/24168/
https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/4694/2991
_version_ 1675330909294821376
score 13.211869

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