Electromyogram (EMG) feature reduction using Mutual Components Analysis for multifunction prosthetic fingers control

Khushaba, RN; Kodagoda, S

Electromyogram (EMG) feature reduction using Mutual Components Analysis for multifunction prosthetic fingers control

Khushaba, RN

Kodagoda, S

Permalink

Publication Type:: Conference Proceeding
Citation:: 2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012, 2012, pp. 1534 - 1539
Issue Date:: 2012-12-01

Closed Access

	Filename	Description	Size
	2012003034OK.pdf		299.64 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012, 2012, pp. 1534 - 1539	en_US
dc.identifier.isbn	9781467318716	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29459
dc.description.abstract	Surface Electromyogram (EMG) signals are usually utilized as a control source for multifunction powered prostheses. A challenge that arises with the current demands of such prostheses is the ability to accurately control a large number of individual and combined fingers movements and to do so in a computationally efficient manner. As a response to such a challenge, we present a combined feature selection and projection algorithm, denoted as Mutual Components Analysis (MCA). The proposed MCA algorithm extends the well-known Principal Components Analysis (PCA) by pruning the noisy and redundant features before projecting the data. To implement the feature selection step, the mutual information concept is utilized to implement a new information gain evaluation function. The performance and significance of the proposed MCA is demonstrated on EMG datasets collected for the purpose of this research from eight subjects with eight electrodes attached on their forearm. Fifteen classes of fingers movements where considered in this paper with MCA achieving >95% accuracy on average across all subjects. © 2012 IEEE.	en_US
dc.relation.ispartof	2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012	en_US
dc.relation.isbasedon	10.1109/ICARCV.2012.6485374	en_US
dc.title	Electromyogram (EMG) feature reduction using Mutual Components Analysis for multifunction prosthetic fingers control	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Guangzhou, China	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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Surface Electromyogram (EMG) signals are usually utilized as a control source for multifunction powered prostheses. A challenge that arises with the current demands of such prostheses is the ability to accurately control a large number of individual and combined fingers movements and to do so in a computationally efficient manner. As a response to such a challenge, we present a combined feature selection and projection algorithm, denoted as Mutual Components Analysis (MCA). The proposed MCA algorithm extends the well-known Principal Components Analysis (PCA) by pruning the noisy and redundant features before projecting the data. To implement the feature selection step, the mutual information concept is utilized to implement a new information gain evaluation function. The performance and significance of the proposed MCA is demonstrated on EMG datasets collected for the purpose of this research from eight subjects with eight electrodes attached on their forearm. Fifteen classes of fingers movements where considered in this paper with MCA achieving >95% accuracy on average across all subjects. © 2012 IEEE.

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

http://hdl.handle.net/10453/29459