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Featureless EMG Pattern Recognition Based On Convolutional Neural Network

Feature extraction is important step to extract the useful and valuable information from the electromyography (EMG) signal. However, the process of feature extraction requires prior knowledge and expertise. In this paper, a featureless EMG pattern recognition technique is proposed to tackle the feat...

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Main Authors: Abdullah, Abdul Rahim, Too, Jing Wei, Mohd Saad, Norhashimah, Mohd Ali, Nursabillilah, Tengku Zawawi, Tengku Nor Shuhada
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2019
Online Access:http://eprints.utem.edu.my/id/eprint/24626/2/2019%20FEATURELESS%20EMG%20PATTERN%20RECOGNITION%20BASED%20ON%20CONVOLUTIONAL%20NEURAL%20NETWORK.PDF
http://eprints.utem.edu.my/id/eprint/24626/
http://ijeecs.iaescore.com/index.php/IJEECS/article/view/13787/12209#
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spelling my.utem.eprints.246262020-12-08T14:22:06Z http://eprints.utem.edu.my/id/eprint/24626/ Featureless EMG Pattern Recognition Based On Convolutional Neural Network Abdullah, Abdul Rahim Too, Jing Wei Mohd Saad, Norhashimah Mohd Ali, Nursabillilah Tengku Zawawi, Tengku Nor Shuhada Feature extraction is important step to extract the useful and valuable information from the electromyography (EMG) signal. However, the process of feature extraction requires prior knowledge and expertise. In this paper, a featureless EMG pattern recognition technique is proposed to tackle the feature extraction problem. Initially, spectrogram is employed to transform the raw EMG signal into time-frequency representation (TFR). The TFRs or spectrogram images are then directly fed into the convolutional neural network (CNN) for classification. Two CNN models are proposed to learn the features automatically from the spectrogram images without the need of manual feature extraction. The proposed CNN models are evaluated using the EMG data acquired from the publicly access NinaPro database. Our results show that CNN classifier can offer the best mean classification accuracy of 88.04% for the recognition of the hand and wrist movements. © 2019 Institute of Advanced Engineering and Science. Institute of Advanced Engineering and Science 2019-06 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/24626/2/2019%20FEATURELESS%20EMG%20PATTERN%20RECOGNITION%20BASED%20ON%20CONVOLUTIONAL%20NEURAL%20NETWORK.PDF Abdullah, Abdul Rahim and Too, Jing Wei and Mohd Saad, Norhashimah and Mohd Ali, Nursabillilah and Tengku Zawawi, Tengku Nor Shuhada (2019) Featureless EMG Pattern Recognition Based On Convolutional Neural Network. Indonesian Journal of Electrical Engineering and Computer Science, 14 (3). pp. 1291-1297. ISSN 2502-4752 http://ijeecs.iaescore.com/index.php/IJEECS/article/view/13787/12209# 10.11591/ijeecs.v14.i3.pp1291-1297
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 Feature extraction is important step to extract the useful and valuable information from the electromyography (EMG) signal. However, the process of feature extraction requires prior knowledge and expertise. In this paper, a featureless EMG pattern recognition technique is proposed to tackle the feature extraction problem. Initially, spectrogram is employed to transform the raw EMG signal into time-frequency representation (TFR). The TFRs or spectrogram images are then directly fed into the convolutional neural network (CNN) for classification. Two CNN models are proposed to learn the features automatically from the spectrogram images without the need of manual feature extraction. The proposed CNN models are evaluated using the EMG data acquired from the publicly access NinaPro database. Our results show that CNN classifier can offer the best mean classification accuracy of 88.04% for the recognition of the hand and wrist movements. © 2019 Institute of Advanced Engineering and Science.
format Article
author Abdullah, Abdul Rahim
Too, Jing Wei
Mohd Saad, Norhashimah
Mohd Ali, Nursabillilah
Tengku Zawawi, Tengku Nor Shuhada
spellingShingle Abdullah, Abdul Rahim
Too, Jing Wei
Mohd Saad, Norhashimah
Mohd Ali, Nursabillilah
Tengku Zawawi, Tengku Nor Shuhada
Featureless EMG Pattern Recognition Based On Convolutional Neural Network
author_facet Abdullah, Abdul Rahim
Too, Jing Wei
Mohd Saad, Norhashimah
Mohd Ali, Nursabillilah
Tengku Zawawi, Tengku Nor Shuhada
author_sort Abdullah, Abdul Rahim
title Featureless EMG Pattern Recognition Based On Convolutional Neural Network
title_short Featureless EMG Pattern Recognition Based On Convolutional Neural Network
title_full Featureless EMG Pattern Recognition Based On Convolutional Neural Network
title_fullStr Featureless EMG Pattern Recognition Based On Convolutional Neural Network
title_full_unstemmed Featureless EMG Pattern Recognition Based On Convolutional Neural Network
title_sort featureless emg pattern recognition based on convolutional neural network
publisher Institute of Advanced Engineering and Science
publishDate 2019
url http://eprints.utem.edu.my/id/eprint/24626/2/2019%20FEATURELESS%20EMG%20PATTERN%20RECOGNITION%20BASED%20ON%20CONVOLUTIONAL%20NEURAL%20NETWORK.PDF
http://eprints.utem.edu.my/id/eprint/24626/
http://ijeecs.iaescore.com/index.php/IJEECS/article/view/13787/12209#
_version_ 1687397253204934656
score 13.211869

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