An ICA-EBM-based sEMG classifier for recognizing lower limb movements in individuals with and without knee pathology

Naik, GR; Selvan, SE; Arjunan, SP; Acharyya, A; Kumar, DK; Ramanujam, A; Nguyen, HT

An ICA-EBM-based sEMG classifier for recognizing lower limb movements in individuals with and without knee pathology

Naik, GR

Selvan, SE Arjunan, SP Acharyya, A Kumar, DK Ramanujam, A Nguyen, HT

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26 (3), pp. 675 - 686
Issue Date:: 2018-03-01

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Field	Value	Language
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Selvan, SE	en_US
dc.contributor.author	Arjunan, SP	en_US
dc.contributor.author	Acharyya, A	en_US
dc.contributor.author	Kumar, DK	en_US
dc.contributor.author	Ramanujam, A	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2018-03-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26 (3), pp. 675 - 686	en_US
dc.identifier.issn	1534-4320	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129995
dc.description.abstract	© 2001-2011 IEEE. Surface electromyography (sEMG) data acquired during lower limb movements has the potential for investigating knee pathology. Nevertheless, a major challenge encountered with sEMG signals generated by lower limb movements is the intersubject variability, because the signals recorded from the leg or thigh muscles are contingent on the characteristics of a subject such as gait activity and muscle structure. In order to cope with this difficulty, we have designed a three-step classification scheme. First, the multichannel sEMG is decomposed into activities of the underlying sources by means of independent component analysis via entropy bound minimization. Next, a set of time-domain features, which would best discriminate various movements, are extracted from the source estimates. Finally, the feature selection is performed with the help of the Fisher score and a scree-plot-based statistical technique, prior to feeding the dimension-reduced features to the linear discriminant analysis. The investigation involves 11 healthy subjects and 11 individuals with knee pathology performing three different lower limb movements, namely, walking, sitting, and standing, which yielded an average classification accuracy of 96.1% and 86.2%, respectively. While the outcome of this study per se is very encouraging, with suitable improvement, the clinical application of such an sEMG-based pattern recognition system that distinguishes healthy and knee pathological subjects would be an attractive consequence.	en_US
dc.relation.ispartof	IEEE Transactions on Neural Systems and Rehabilitation Engineering	en_US
dc.relation.isbasedon	10.1109/TNSRE.2018.2796070	en_US
dc.subject.classification	Biomedical Engineering	en_US
dc.subject.mesh	Lower Extremity	en_US
dc.subject.mesh	Muscle, Skeletal	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Knee Injuries	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Walking	en_US
dc.subject.mesh	Discriminant Analysis	en_US
dc.subject.mesh	Movement	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Entropy	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Young Adult	en_US
dc.subject.mesh	Healthy Volunteers	en_US
dc.subject.mesh	Biomechanical Phenomena	en_US
dc.title	An ICA-EBM-based sEMG classifier for recognizing lower limb movements in individuals with and without knee pathology	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	26	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	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	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2001-2011 IEEE. Surface electromyography (sEMG) data acquired during lower limb movements has the potential for investigating knee pathology. Nevertheless, a major challenge encountered with sEMG signals generated by lower limb movements is the intersubject variability, because the signals recorded from the leg or thigh muscles are contingent on the characteristics of a subject such as gait activity and muscle structure. In order to cope with this difficulty, we have designed a three-step classification scheme. First, the multichannel sEMG is decomposed into activities of the underlying sources by means of independent component analysis via entropy bound minimization. Next, a set of time-domain features, which would best discriminate various movements, are extracted from the source estimates. Finally, the feature selection is performed with the help of the Fisher score and a scree-plot-based statistical technique, prior to feeding the dimension-reduced features to the linear discriminant analysis. The investigation involves 11 healthy subjects and 11 individuals with knee pathology performing three different lower limb movements, namely, walking, sitting, and standing, which yielded an average classification accuracy of 96.1% and 86.2%, respectively. While the outcome of this study per se is very encouraging, with suitable improvement, the clinical application of such an sEMG-based pattern recognition system that distinguishes healthy and knee pathological subjects would be an attractive consequence.

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http://hdl.handle.net/10453/129995