Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: A review

Naik, GR; Arjunan, S; Kumar, D

Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: A review

Naik, GR

Arjunan, S Kumar, D

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Publication Type:: Journal Article
Citation:: Australasian Physical and Engineering Sciences in Medicine, 2011, 34 (2), pp. 179 - 193
Issue Date:: 2011-06-01

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Field	Value	Language
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Arjunan, S	en_US
dc.contributor.author	Kumar, D	en_US
dc.date.available	2011-03-09	en_US
dc.date.issued	2011-06-01	en_US
dc.identifier.citation	Australasian Physical and Engineering Sciences in Medicine, 2011, 34 (2), pp. 179 - 193	en_US
dc.identifier.issn	0158-9938	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27579
dc.description.abstract	The surface electromyography (sEMG) signal separation and decphompositions has always been an interesting research topic in the field of rehabilitation and medical research. Subtle myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in independent component analysis and Fractal dimensional analysis for sEMG pattern recognition, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined. © 2011 Australasian College of Physical Scientists and Engineers in Medicine.	en_US
dc.relation.ispartof	Australasian Physical and Engineering Sciences in Medicine	en_US
dc.relation.isbasedon	10.1007/s13246-011-0066-4	en_US
dc.subject.classification	Nuclear Medicine & Medical Imaging	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Movement	en_US
dc.subject.mesh	Fractals	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Skin Physiological Phenomena	en_US
dc.title	Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: A review	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	34	en_US
utslib.for	029903 Medical Physics	en_US
utslib.for	09 Engineering	en_US
utslib.for	02 Physical Sciences	en_US
utslib.for	11 Medical and Health Sciences	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	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	34	en_US

Abstract:

The surface electromyography (sEMG) signal separation and decphompositions has always been an interesting research topic in the field of rehabilitation and medical research. Subtle myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in independent component analysis and Fractal dimensional analysis for sEMG pattern recognition, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined. © 2011 Australasian College of Physical Scientists and Engineers in Medicine.

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http://hdl.handle.net/10453/27579