Isometric Finger Pose Recognition with Sparse Channel SpatioTemporal EMG Imaging

Stephenson, RM; Chai, R; Eager, D

Isometric Finger Pose Recognition with Sparse Channel SpatioTemporal EMG Imaging

Stephenson, RM

Chai, R

Eager, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, 2018-July pp. 5232 - 5235
Issue Date:: 2018-10-26

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Field	Value	Language
dc.contributor.author	Stephenson, RM https://orcid.org/0000-0002-8129-6725	en_US
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024	en_US
dc.contributor.author	Eager, D https://orcid.org/0000-0003-1926-7867	en_US
dc.date.available	2020-11-01T18:05:13Z
dc.date.issued	2018-10-26	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, 2018-July pp. 5232 - 5235	en_US
dc.identifier.isbn	9781538636466	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129324
dc.description.abstract	© 2018 IEEE. High fidelity myoelectric control of prostheses and orthoses isparamount to restoring lost function to amputees and neuro-muscular disease sufferers. In this study we prove that patio-temporal imaging can be used to allow convolutional neural networks to classify sparse channel EMG samples from a consumer-grade device with over 94% accuracy. 10,572 images are generated from 960 samples of simple and complex isometric finger poses recorded from 4 fully intact subjects. Real-time classification of 12 poses is achieved with a 250ms continuous overlapping window.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2018.8513445	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.mesh	Fingers	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Artificial Limbs	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.subject.mesh	Amputees	en_US
dc.title	Isometric Finger Pose Recognition with Sparse Channel SpatioTemporal EMG Imaging	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	en_US
utslib.for	0903 Biomedical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/e-Press
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/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-July	en_US

Abstract:

© 2018 IEEE. High fidelity myoelectric control of prostheses and orthoses isparamount to restoring lost function to amputees and neuro-muscular disease sufferers. In this study we prove that patio-temporal imaging can be used to allow convolutional neural networks to classify sparse channel EMG samples from a consumer-grade device with over 94% accuracy. 10,572 images are generated from 960 samples of simple and complex isometric finger poses recorded from 4 fully intact subjects. Real-time classification of 12 poses is achieved with a 250ms continuous overlapping window.

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http://hdl.handle.net/10453/129324