Selecting the optimal movement subset with different pattern recognition based EMG control algorithms.

Al-Timemy, AH; Khushaba, RN; Escudero, J

Selecting the optimal movement subset with different pattern recognition based EMG control algorithms.

Al-Timemy, AH Khushaba, RN Escudero, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Conf Proc IEEE Eng Med Biol Soc, 2016, 2016 pp. 315 - 318
Issue Date:: 2016-08

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Field	Value	Language
dc.contributor.author	Al-Timemy, AH	en_US
dc.contributor.author	Khushaba, RN	en_US
dc.contributor.author	Escudero, J	en_US
dc.date	2016-08-16	en_US
dc.date.accessioned	2017-04-06T05:02:16Z
dc.date.available	2017-04-06T05:02:16Z
dc.date.issued	2016-08	en_US
dc.identifier.citation	Conf Proc IEEE Eng Med Biol Soc, 2016, 2016 pp. 315 - 318	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/90329
dc.description.abstract	Pattern Recognition (PR)-based EMG controllers of multi-functional upper-limb prostheses have been recently deployed on commercial state-of-the-art prostheses, offering intuitive control with the ability to control large number of movements with fast reaction time. Current challenges with such PR systems include the lack of training and deployment protocols that can help optimize the system's performance based on amputees' needs. Selecting the best subset of movements that each individual amputee can perform will help to exclude movements that have poor performance so that a subject-specific training can be achieved. In this paper, we propose to select the best set of movements that each amputee can perform as well as identifying the movements for which the PR system would have the worst performance and, therefore, would require further training. Unlike previous studies in this direction, different feature extraction and classification methods were utilized to examine if the choice of features/classifiers could affect the best movements subset selection. We performed our experiments on EMG signals collected from four transradial amputees with an accuracy > 97.5% on average across all subjects for the selection of best subset of movements.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Conf Proc IEEE Eng Med Biol Soc	en_US
dc.relation.ispartof	IEEE Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)	en_US
dc.relation.isbasedon	10.1109/EMBC.2016.7590703	en_US
dc.rights	© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Selecting the optimal movement subset with different pattern recognition based EMG control algorithms.	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016	en_US
utslib.location	Piscataway, USA	en_US
utslib.location.activity	United States	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 Elec, Mech and Mechatronic Systems
utslib.copyright.status	open_access
pubs.declined	2017-04-06T15:02:16.67+1000
pubs.consider-herdc	true	en_US
pubs.deleted	2017-04-06T15:02:16.67+1000
pubs.finish-date	2016-08-20	en_US
pubs.place-of-publication	Piscataway, USA	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2016-08-16	en_US
pubs.volume	2016	en_US

Abstract:

Pattern Recognition (PR)-based EMG controllers of multi-functional upper-limb prostheses have been recently deployed on commercial state-of-the-art prostheses, offering intuitive control with the ability to control large number of movements with fast reaction time. Current challenges with such PR systems include the lack of training and deployment protocols that can help optimize the system's performance based on amputees' needs. Selecting the best subset of movements that each individual amputee can perform will help to exclude movements that have poor performance so that a subject-specific training can be achieved. In this paper, we propose to select the best set of movements that each amputee can perform as well as identifying the movements for which the PR system would have the worst performance and, therefore, would require further training. Unlike previous studies in this direction, different feature extraction and classification methods were utilized to examine if the choice of features/classifiers could affect the best movements subset selection. We performed our experiments on EMG signals collected from four transradial amputees with an accuracy > 97.5% on average across all subjects for the selection of best subset of movements.

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http://hdl.handle.net/10453/90329