Pattern classification of Myo-Electrical signal during different maximum voluntary contractions: A study using BSS techniques

Naik, GR; Kumar, DK; Arjunan, SP

Pattern classification of Myo-Electrical signal during different maximum voluntary contractions: A study using BSS techniques

Naik, GR

Kumar, DK Arjunan, SP

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Publication Type:: Journal Article
Citation:: Measurement Science Review, 2010, 10 (1), pp. 1 - 6
Issue Date:: 2010-01-01

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Field	Value	Language
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Kumar, DK	en_US
dc.contributor.author	Arjunan, SP	en_US
dc.date.issued	2010-01-01	en_US
dc.identifier.citation	Measurement Science Review, 2010, 10 (1), pp. 1 - 6	en_US
dc.identifier.issn	1335-8871	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26652
dc.description.abstract	The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when the level of muscle contraction is very low. Due to this the current applications of surface electromyogram (sEMG) are infeasible and unreliable in pattern classification. This research reports a new technique of sEMG using Independent Component Analysis (ICA). The technique uses blind source separation (BSS) methods to classify the patterns of Myo-electrical signals during different Maximum Voluntary Contraction (MVCs) at different low level finger movements. The results of the experiments indicate that patterns using ICA of sEMG is a reliable (p<0.001) measure of strength of muscle contraction even when muscle activity is only 20% MVC. The authors propose that ICA is a useful indicator of muscle properties and is a useful indicator of the level of muscle activity.	en_US
dc.relation.ispartof	Measurement Science Review	en_US
dc.relation.isbasedon	10.2478/v10048-010-0001-y	en_US
dc.title	Pattern classification of Myo-Electrical signal during different maximum voluntary contractions: A study using BSS techniques	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	10	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0999 Other 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
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when the level of muscle contraction is very low. Due to this the current applications of surface electromyogram (sEMG) are infeasible and unreliable in pattern classification. This research reports a new technique of sEMG using Independent Component Analysis (ICA). The technique uses blind source separation (BSS) methods to classify the patterns of Myo-electrical signals during different Maximum Voluntary Contraction (MVCs) at different low level finger movements. The results of the experiments indicate that patterns using ICA of sEMG is a reliable (p<0.001) measure of strength of muscle contraction even when muscle activity is only 20% MVC. The authors propose that ICA is a useful indicator of muscle properties and is a useful indicator of the level of muscle activity.

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http://hdl.handle.net/10453/26652