Classification of low level surface electromyogram using independent component analysis

Naik, GR; Kumar, DK; Palaniswami, M

Classification of low level surface electromyogram using independent component analysis

Naik, GR

Kumar, DK Palaniswami, M

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Publication Type:: Journal Article
Citation:: IET Signal Processing, 2010, 4 (5), pp. 479 - 487
Issue Date:: 2010-10-01

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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	Palaniswami, M	en_US
dc.date.issued	2010-10-01	en_US
dc.identifier.citation	IET Signal Processing, 2010, 4 (5), pp. 479 - 487	en_US
dc.identifier.issn	1751-9675	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26644
dc.description.abstract	There is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer-assisted devices. Surface electromyogram (SEMG) is a non-invasive measure of the muscle activities but is not reliable because there are a multiple simultaneously active muscles. This study proposes the use of independent component analysis (ICA) for SEMG to separate activity from different muscles. A mitigation strategy to overcome shortcomings related to order and magnitude ambiguity related to ICA has been developed. This is achieved by using a combination of unmixing matrix obtained from FastICA analysis and weight matrix derived from training of the supervised neural network corresponding to the specific user. This is referred to as ICANN (independent component analysis neural network combination). Experiments were conducted and the results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user. © 2010 The Institution of Engineering and Technology.	en_US
dc.relation.ispartof	IET Signal Processing	en_US
dc.relation.isbasedon	10.1049/iet-spr.2007.0211	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Classification of low level surface electromyogram using independent component analysis	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	4	en_US
utslib.for	090609 Signal Processing	en_US
utslib.for	090302 Biomechanical Engineering	en_US
utslib.for	090399 Biomedical Engineering not elsewhere classified	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
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	4	en_US

Abstract:

There is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer-assisted devices. Surface electromyogram (SEMG) is a non-invasive measure of the muscle activities but is not reliable because there are a multiple simultaneously active muscles. This study proposes the use of independent component analysis (ICA) for SEMG to separate activity from different muscles. A mitigation strategy to overcome shortcomings related to order and magnitude ambiguity related to ICA has been developed. This is achieved by using a combination of unmixing matrix obtained from FastICA analysis and weight matrix derived from training of the supervised neural network corresponding to the specific user. This is referred to as ICANN (independent component analysis neural network combination). Experiments were conducted and the results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user. © 2010 The Institution of Engineering and Technology.

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http://hdl.handle.net/10453/26644