Development of a model for sEMG based joint-torque estimation using Swarm techniques

Nurhanim, K; Elamvazuthi, I; Izhar, LI; Ganesan, T; Su, SW

Development of a model for sEMG based joint-torque estimation using Swarm techniques

Nurhanim, K Elamvazuthi, I Izhar, LI Ganesan, T Su, SW

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Publication Type:: Conference Proceeding
Citation:: 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation, ROMA 2016, 2017
Issue Date:: 2017-02-08

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Field	Value	Language
dc.contributor.author	Nurhanim, K	en_US
dc.contributor.author	Elamvazuthi, I	en_US
dc.contributor.author	Izhar, LI	en_US
dc.contributor.author	Ganesan, T	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.date.issued	2017-02-08	en_US
dc.identifier.citation	2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation, ROMA 2016, 2017	en_US
dc.identifier.isbn	9781509009282	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120319
dc.description.abstract	© 2016 IEEE. Over the years, numerous researchers have explored the relationship between surface electromyography (sEMG) signal with joint torque that would be useful to develop a suitable controller for rehabilitation robot. This research focuses on the transformation of sEMG signal by adopting a mathematical model to find the estimated joint torque of knee extension. Swarm techniques such as Particle Swarm Optimization (PSO) and Improved Particle Swarm Optimization (IPSO) were adapted to optimize the mathematical model for estimated joint torque. The correlation between the estimated joint torque and actual joint torque were determined by Coefficient of Determination (R2) and fitness value of Sum Squared Error (SSE). The outcome of the research shows that both the PSO and IPSO have yielded promising results.	en_US
dc.relation.ispartof	2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation, ROMA 2016	en_US
dc.relation.isbasedon	10.1109/ROMA.2016.7847833	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Development of a model for sEMG based joint-torque estimation using Swarm techniques	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	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Over the years, numerous researchers have explored the relationship between surface electromyography (sEMG) signal with joint torque that would be useful to develop a suitable controller for rehabilitation robot. This research focuses on the transformation of sEMG signal by adopting a mathematical model to find the estimated joint torque of knee extension. Swarm techniques such as Particle Swarm Optimization (PSO) and Improved Particle Swarm Optimization (IPSO) were adapted to optimize the mathematical model for estimated joint torque. The correlation between the estimated joint torque and actual joint torque were determined by Coefficient of Determination (R2) and fitness value of Sum Squared Error (SSE). The outcome of the research shows that both the PSO and IPSO have yielded promising results.

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http://hdl.handle.net/10453/120319