Differences in EMG Feature Space between Able-Bodied and Amputee Subjects for Myoelectric Control

Campbell, E; Phinyomark, A; Al-Timemy, AH; Khushaba, RN; Petri, G; Scheme, E

Differences in EMG Feature Space between Able-Bodied and Amputee Subjects for Myoelectric Control

Campbell, E Phinyomark, A Al-Timemy, AH Khushaba, RN

Petri, G Scheme, E

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Publication Type:: Conference Proceeding
Citation:: International IEEE/EMBS Conference on Neural Engineering, NER, 2019, 2019-March pp. 33 - 36
Issue Date:: 2019-05-16

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Field	Value	Language
dc.contributor.author	Campbell, E	en_US
dc.contributor.author	Phinyomark, A	en_US
dc.contributor.author	Al-Timemy, AH	en_US
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Petri, G	en_US
dc.contributor.author	Scheme, E	en_US
dc.date.accessioned	2020-03-23T02:50:09Z
dc.date.available	2020-03-23T02:50:09Z
dc.date.issued	2019-05-16	en_US
dc.identifier.citation	International IEEE/EMBS Conference on Neural Engineering, NER, 2019, 2019-March pp. 33 - 36	en_US
dc.identifier.isbn	9781538679210	en_US
dc.identifier.issn	1948-3546	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139472
dc.description.abstract	© 2019 IEEE. Difficulties accessing amputee populations has resulted in the widespread adoption of able-bodied subjects in virtual environments for the development of myoelectric prostheses. Factors such as scar tissue, different physiologies or surgical outcomes, and reduced visual and proprioceptive feedback, however, may contribute to differences in electromyogram (EMG) patterns between these groups. As such, studies have consistently found worse results when comparing the performance of amputee subjects to that of their able-bodied counterparts under the same conditions. To identify the source of this performance degradation, a topology-based data analysis method, called Mapper, was employed to visualize the «shape» of EMG feature spaces derived from amputee and able-bodied subjects. The information content of amputee EMG features was found to differ from those of non-amputee subject in three ways: 1) the loss of nonlinear complexity and frequency information, 2) the loss of time-series modeling information, and 3) the segmentation of unique information. The empirical effects of these differences were visualized by classifying motion classes using consistent and migratory features from functional feature groups. In summary, this work characterized inconsistencies in EMG features between amputee and able-bodied populations by theoretical means, highlighted the empirical effects when these are ignored, and proposed a solution for future studies with able-bodied subjects.	en_US
dc.relation.ispartof	International IEEE/EMBS Conference on Neural Engineering, NER	en_US
dc.relation.isbasedon	10.1109/NER.2019.8717161	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Differences in EMG Feature Space between Able-Bodied and Amputee Subjects for Myoelectric Control	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-March	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
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	2019-March	en_US

Abstract:

© 2019 IEEE. Difficulties accessing amputee populations has resulted in the widespread adoption of able-bodied subjects in virtual environments for the development of myoelectric prostheses. Factors such as scar tissue, different physiologies or surgical outcomes, and reduced visual and proprioceptive feedback, however, may contribute to differences in electromyogram (EMG) patterns between these groups. As such, studies have consistently found worse results when comparing the performance of amputee subjects to that of their able-bodied counterparts under the same conditions. To identify the source of this performance degradation, a topology-based data analysis method, called Mapper, was employed to visualize the «shape» of EMG feature spaces derived from amputee and able-bodied subjects. The information content of amputee EMG features was found to differ from those of non-amputee subject in three ways: 1) the loss of nonlinear complexity and frequency information, 2) the loss of time-series modeling information, and 3) the segmentation of unique information. The empirical effects of these differences were visualized by classifying motion classes using consistent and migratory features from functional feature groups. In summary, this work characterized inconsistencies in EMG features between amputee and able-bodied populations by theoretical means, highlighted the empirical effects when these are ignored, and proposed a solution for future studies with able-bodied subjects.

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http://hdl.handle.net/10453/139472