A fusion of time-domain descriptors for improved myoelectric hand control

Khushaba, RN; Al-Ani, A; Al-Timemy, A; Al-Jumaily, A

A fusion of time-domain descriptors for improved myoelectric hand control

Khushaba, RN

Al-Ani, A

Al-Timemy, A Al-Jumaily, A

Permalink

Publication Type:: Conference Proceeding
Citation:: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, 2017
Issue Date:: 2017-02-09

Closed Access

	Filename	Description	Size
	07850064 (1).pdf	Published version	568.85 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	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Al-Timemy, A	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2017-02-09	en_US
dc.identifier.citation	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, 2017	en_US
dc.identifier.isbn	9781509042401	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125916
dc.description.abstract	© 2016 IEEE. This paper presents a new feature extraction algorithm for the challenging problem of the classification of myoelectric signals for prostheses control. The algorithm employs the orientation between a set of descriptors of muscular activities and a nonlinearly mapped version of them. It incorporates information about the Electromyogram (EMG) signal power spectrum characteristics derived from each analysis window while correlating that with the descriptors of previous windows for robust activity recognition. The proposed idea can be summarized in the following three steps: 1) extract power spectrum moments from the current analysis window and its nonlinearly scaled version in time-domain through Fourier transform relations, 2) compute the orientation between the two sets of moments, and 3) apply data fusion on the resulting orientation features for the current and previous time windows and use the result as the final feature set. EMG data collected from nine transradial amputees performing six classes of movements with different force levels is used to validate the proposed features. When compared to other well-known EMG feature extraction methods, the proposed features produced an improvement of at least 4%.	en_US
dc.relation.ispartof	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016	en_US
dc.relation.isbasedon	10.1109/SSCI.2016.7850064	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A fusion of time-domain descriptors for improved myoelectric hand control	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic 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
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	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. This paper presents a new feature extraction algorithm for the challenging problem of the classification of myoelectric signals for prostheses control. The algorithm employs the orientation between a set of descriptors of muscular activities and a nonlinearly mapped version of them. It incorporates information about the Electromyogram (EMG) signal power spectrum characteristics derived from each analysis window while correlating that with the descriptors of previous windows for robust activity recognition. The proposed idea can be summarized in the following three steps: 1) extract power spectrum moments from the current analysis window and its nonlinearly scaled version in time-domain through Fourier transform relations, 2) compute the orientation between the two sets of moments, and 3) apply data fusion on the resulting orientation features for the current and previous time windows and use the result as the final feature set. EMG data collected from nine transradial amputees performing six classes of movements with different force levels is used to validate the proposed features. When compared to other well-known EMG feature extraction methods, the proposed features produced an improvement of at least 4%.

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

http://hdl.handle.net/10453/125916