Real-time classification of finger movements using two-channel surface electromyography

Anam, K; Al-Jumaily, A

Real-time classification of finger movements using two-channel surface electromyography

Anam, K

Al-Jumaily, A

Permalink

Publication Type:: Conference Proceeding
Citation:: NEUROTECHNIX 2013 - Proceedings of the International Congress on Neurotechnology, Electronics and Informatics, 2013, pp. 218 - 223
Issue Date:: 2013-12-03

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (3.36 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Anam, K https://orcid.org/0000-0003-4169-2237	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2013-12-03	en_US
dc.identifier.citation	NEUROTECHNIX 2013 - Proceedings of the International Congress on Neurotechnology, Electronics and Informatics, 2013, pp. 218 - 223	en_US
dc.identifier.isbn	9789898565808	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26725
dc.description.abstract	The use of a small number of Electromyography (EMG) channels for classifying the finger movement is a challenging task. This paper proposes the recognition system for decoding the individual and combined finger movements using two channels surface EMG. The proposed system utilizes Spectral Regression Discriminant Analysis (SRDA) for dimensionality reduction, Extreme Learning Machine (ELM) for classification and the majority vote for the classification smoothness. The experimental results show that the proposed system was able to classify ten classes of individual and combined finger movements, offline and online with accuracy 97.96 % and 97.07% respectively.	en_US
dc.relation.ispartof	NEUROTECHNIX 2013 - Proceedings of the International Congress on Neurotechnology, Electronics and Informatics	en_US
dc.title	Real-time classification of finger movements using two-channel surface electromyography	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Vilamoura, Algarve, Portugal	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

The use of a small number of Electromyography (EMG) channels for classifying the finger movement is a challenging task. This paper proposes the recognition system for decoding the individual and combined finger movements using two channels surface EMG. The proposed system utilizes Spectral Regression Discriminant Analysis (SRDA) for dimensionality reduction, Extreme Learning Machine (ELM) for classification and the majority vote for the classification smoothness. The experimental results show that the proposed system was able to classify ten classes of individual and combined finger movements, offline and online with accuracy 97.96 % and 97.07% respectively.

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

http://hdl.handle.net/10453/26725