R-CNN and wavelet feature extraction for hand gesture recognition with EMG signals

Shanmuganathan, V; Yesudhas, HR; Khan, MS; Khari, M; Gandomi, AH

R-CNN and wavelet feature extraction for hand gesture recognition with EMG signals

Shanmuganathan, V Yesudhas, HR Khan, MS Khari, M Gandomi, AH

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Publisher:: SPRINGER LONDON LTD
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2020, 32, (21), pp. 16723-16736
Issue Date:: 2020-11-01

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Field	Value	Language
dc.contributor.author	Shanmuganathan, V
dc.contributor.author	Yesudhas, HR
dc.contributor.author	Khan, MS
dc.contributor.author	Khari, M
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2021-03-23T04:25:55Z
dc.date.available	2021-03-23T04:25:55Z
dc.date.issued	2020-11-01
dc.identifier.citation	Neural Computing and Applications, 2020, 32, (21), pp. 16723-16736
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/147479
dc.description.abstract	© 2020, Springer-Verlag London Ltd., part of Springer Nature. This paper demonstrates the implementation of R-CNN in terms of electromyography-related signals to recognize hand gestures. The signal acquisition is implemented using electrodes situated on the forearm, and the biomedical signals are generated to perform the signals preprocessing using wavelet packet transform to perform the feature extraction. The R-CNN methodology is used to map the specific features that are acquired from the wavelet power spectrum to validate and train how the architecture is framed. Additionally, the real-time test is completed to reach the accuracy of 96.48% compared to the related methods. This kind of result proves that the proposed work has the highest amount of accuracy in recognizing the gestures.
dc.language	English
dc.publisher	SPRINGER LONDON LTD
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-020-05349-w
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	R-CNN and wavelet feature extraction for hand gesture recognition with EMG signals
dc.type	Journal Article
utslib.citation.volume	32
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-03-23T04:25:53Z
pubs.issue	21
pubs.publication-status	Published
pubs.volume	32
utslib.citation.issue	21

Abstract:

© 2020, Springer-Verlag London Ltd., part of Springer Nature. This paper demonstrates the implementation of R-CNN in terms of electromyography-related signals to recognize hand gestures. The signal acquisition is implemented using electrodes situated on the forearm, and the biomedical signals are generated to perform the signals preprocessing using wavelet packet transform to perform the feature extraction. The R-CNN methodology is used to map the specific features that are acquired from the wavelet power spectrum to validate and train how the architecture is framed. Additionally, the real-time test is completed to reach the accuracy of 96.48% compared to the related methods. This kind of result proves that the proposed work has the highest amount of accuracy in recognizing the gestures.

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http://hdl.handle.net/10453/147479