Towards classification of low-level finger movements using forearm muscle activation: A comparative study based on ICA and Fractal theory

Naik, GR; Kumar, DK; Arjunan, SP

Towards classification of low-level finger movements using forearm muscle activation: A comparative study based on ICA and Fractal theory

Naik, GR

Kumar, DK Arjunan, SP

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Publication Type:: Journal Article
Citation:: International Journal of Biomedical Engineering and Technology, 2011, 6 (2), pp. 150 - 162
Issue Date:: 2011-07-01

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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	2011-07-01	en_US
dc.identifier.citation	International Journal of Biomedical Engineering and Technology, 2011, 6 (2), pp. 150 - 162	en_US
dc.identifier.issn	1752-6418	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31732
dc.description.abstract	There are number of possible rehabilitation applications of surface Electromyogram (sEMG) that are currently unreliable, when the level of muscle contraction is low. This paper has experimentally analysed the features of forearm sEMG based on Independent Component Analysis (ICA) and Fractal Dimension (FD) for identification of low-level finger movements. To reduce inter-experimental variations, the normalised feature values were used as the training and testing vectors to artificial neural network. The identification accuracy using raw sEMG and FD of sEMG was 51% and 58%, respectively. The accuracy increased to 96% when the signals are separated to their independent components using ICA. © 2011 Inderscience Enterprises Ltd.	en_US
dc.relation.ispartof	International Journal of Biomedical Engineering and Technology	en_US
dc.relation.isbasedon	10.1504/IJBET.2011.041121	en_US
dc.title	Towards classification of low-level finger movements using forearm muscle activation: A comparative study based on ICA and Fractal theory	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	6	en_US
utslib.for	090305 Rehabilitation Engineering	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0903 Biomedical 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	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	6	en_US

Abstract:

There are number of possible rehabilitation applications of surface Electromyogram (sEMG) that are currently unreliable, when the level of muscle contraction is low. This paper has experimentally analysed the features of forearm sEMG based on Independent Component Analysis (ICA) and Fractal Dimension (FD) for identification of low-level finger movements. To reduce inter-experimental variations, the normalised feature values were used as the training and testing vectors to artificial neural network. The identification accuracy using raw sEMG and FD of sEMG was 51% and 58%, respectively. The accuracy increased to 96% when the signals are separated to their independent components using ICA. © 2011 Inderscience Enterprises Ltd.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/31732