Nonnegative matrix factorization for the identification of EMG finger movements: Evaluation using matrix analysis

Naik, GR; Nguyen, HT

Nonnegative matrix factorization for the identification of EMG finger movements: Evaluation using matrix analysis

Naik, GR

Nguyen, HT

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Publication Type:: Journal Article
Citation:: IEEE Journal of Biomedical and Health Informatics, 2015, 19 (2), pp. 478 - 485
Issue Date:: 2015-03-01

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Field	Value	Language
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2015-03-01	en_US
dc.identifier.citation	IEEE Journal of Biomedical and Health Informatics, 2015, 19 (2), pp. 478 - 485	en_US
dc.identifier.issn	2168-2194	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34287
dc.description.abstract	© 2014 IEEE. Surface electromyography (sEMG) is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. The sEMG is a noninvasive, easy to record signal of superficial muscles from the skin surface. Considering the nonstationary characteristics of sEMG, recent feature selection of hand gesture recognition using sEMG signals necessitate designers to use nonnegative matrix factorization (NMF)-based methods. This method exploits both the additive and sparse nature of signals by extracting accurate and reliable measurements of sEMG features using a minimum number of sensors. The testing has been conducted for simple and complex finger flexions using several experiments with artificial neural network classification scheme. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to classify ten finger flexions (five simple and five complex finger flexions) recorded from two sEMG sensors up to 92% (95% for simple and 87% for complex flexions) accuracy. The recognition performances of simple and complex finger flexions are also validated with NMF permutation matrix analysis.	en_US
dc.relation.ispartof	IEEE Journal of Biomedical and Health Informatics	en_US
dc.relation.isbasedon	10.1109/JBHI.2014.2326660	en_US
dc.subject.mesh	Fingers	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Gestures	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Principal Component Analysis	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Nonnegative matrix factorization for the identification of EMG finger movements: Evaluation using matrix analysis	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	19	en_US
utslib.for	090303 Biomedical Instrumentation	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
pubs.embargo.period	Not known	en_US
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/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© 2014 IEEE. Surface electromyography (sEMG) is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. The sEMG is a noninvasive, easy to record signal of superficial muscles from the skin surface. Considering the nonstationary characteristics of sEMG, recent feature selection of hand gesture recognition using sEMG signals necessitate designers to use nonnegative matrix factorization (NMF)-based methods. This method exploits both the additive and sparse nature of signals by extracting accurate and reliable measurements of sEMG features using a minimum number of sensors. The testing has been conducted for simple and complex finger flexions using several experiments with artificial neural network classification scheme. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to classify ten finger flexions (five simple and five complex finger flexions) recorded from two sEMG sensors up to 92% (95% for simple and 87% for complex flexions) accuracy. The recognition performances of simple and complex finger flexions are also validated with NMF permutation matrix analysis.

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http://hdl.handle.net/10453/34287