Use of sEMG in identification of low level muscle activities: Features based on ICA and Fractal dimension

Naik, GR; Kumar, DK; Arjunan, S

Use of sEMG in identification of low level muscle activities: Features based on ICA and Fractal dimension

Naik, GR

Kumar, DK Arjunan, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, 2009, pp. 364 - 367
Issue Date:: 2009-01-01

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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, S	en_US
dc.date.issued	2009-01-01	en_US
dc.identifier.citation	Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, 2009, pp. 364 - 367	en_US
dc.identifier.isbn	9781424432967	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26724
dc.description.abstract	This paper has experimentally verified and compared features of sEMG (Surface Electromyogram) such as ICA (Independent Component Analysis) and Fractal Dimension (FD) for identification of low level forearm muscle activities. The fractal dimension was used as a feature as reported in the literature. The normalized feature values were used as training and testing vectors for an Artificial neural network (ANN), in order to reduce inter-experimental variations. The identification accuracy using FD of four channels sEMG was 58%, and increased to 96% when the signals are separated to their independent components using ICA. ©2009 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009	en_US
dc.relation.isbasedon	10.1109/IEMBS.2009.5332489	en_US
dc.subject.mesh	Fingers	en_US
dc.subject.mesh	Muscle, Skeletal	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Isometric Contraction	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Fractals	en_US
dc.subject.mesh	Principal Component Analysis	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Physical Exertion	en_US
dc.subject.mesh	Young Adult	en_US
dc.title	Use of sEMG in identification of low level muscle activities: Features based on ICA and Fractal dimension	en_US
dc.type	Conference Proceeding
utslib.for	090609 Signal Processing	en_US
utslib.for	090302 Biomechanical Engineering	en_US
utslib.for	090399 Biomedical Engineering not elsewhere classified	en_US
dc.location.activity	Minneapolis, MN	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.publication-status	Published	en_US

Abstract:

This paper has experimentally verified and compared features of sEMG (Surface Electromyogram) such as ICA (Independent Component Analysis) and Fractal Dimension (FD) for identification of low level forearm muscle activities. The fractal dimension was used as a feature as reported in the literature. The normalized feature values were used as training and testing vectors for an Artificial neural network (ANN), in order to reduce inter-experimental variations. The identification accuracy using FD of four channels sEMG was 58%, and increased to 96% when the signals are separated to their independent components using ICA. ©2009 IEEE.

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http://hdl.handle.net/10453/26724