Upper limb recovery prediction after stroke rehabilitation based on regression method

Bani Musa, GM; Alnajjar, F; Al-Jumaily, A; Shimoda, S

Upper limb recovery prediction after stroke rehabilitation based on regression method

Bani Musa, GM

Alnajjar, F Al-Jumaily, A

Shimoda, S

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Publication Type:: Conference Proceeding
Citation:: 2019, 21 pp. 380 - 384
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Bani Musa, GM https://orcid.org/0000-0002-1471-3226	en_US
dc.contributor.author	Alnajjar, F	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Shimoda, S	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	2019, 21 pp. 380 - 384	en_US
dc.identifier.issn	2195-3562	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128635
dc.description.abstract	© Springer Nature Switzerland AG 2019. In this paper, we investigate the possibility of a machine-learning algorithm using the Support Victor Machine Regression (SVMR) to predict the motor functional recovery of moderate post stroke patients during their rehabilitation program. To train the model, we used the recorded electromyography (EMG) signals from the upper limb muscles of the patients during their initial rehabilitation sessions. Then we tested the trained model to predict the later muscles performance of the patient during the same sessions. The results of this pilot study were promising; data were, to some extent, predictable. We believe such research direction could be essential to motivate the patient to complete the designed rehabilitation program and can assist the therapist to innovate proper rehabilitation menu for individual patients.	en_US
dc.relation.isbasedon	10.1007/978-3-030-01845-0_76	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Upper limb recovery prediction after stroke rehabilitation based on regression method	en_US
dc.type	Conference Proceeding
utslib.citation.volume	21	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0802 Computation Theory and Mathematics	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
pubs.volume	21	en_US

Abstract:

© Springer Nature Switzerland AG 2019. In this paper, we investigate the possibility of a machine-learning algorithm using the Support Victor Machine Regression (SVMR) to predict the motor functional recovery of moderate post stroke patients during their rehabilitation program. To train the model, we used the recorded electromyography (EMG) signals from the upper limb muscles of the patients during their initial rehabilitation sessions. Then we tested the trained model to predict the later muscles performance of the patient during the same sessions. The results of this pilot study were promising; data were, to some extent, predictable. We believe such research direction could be essential to motivate the patient to complete the designed rehabilitation program and can assist the therapist to innovate proper rehabilitation menu for individual patients.

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http://hdl.handle.net/10453/128635