EMG signal based knee joint torque estimation

Anwar, T; Al Jumaily, A

EMG signal based knee joint torque estimation

Anwar, T Al Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: 2016 International Conference on Systems in Medicine and Biology, ICSMB 2016, 2017, pp. 182 - 185
Issue Date:: 2017-04-28

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Field	Value	Language
dc.contributor.author	Anwar, T	en_US
dc.contributor.author	Al Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2017-04-28	en_US
dc.identifier.citation	2016 International Conference on Systems in Medicine and Biology, ICSMB 2016, 2017, pp. 182 - 185	en_US
dc.identifier.isbn	9781467376662	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127301
dc.description.abstract	© 2016 IEEE. Although Lower Limb Robotic Rehabilitation device exhibit a great prospect in the rehabilitation of impaired limb, yet it has not been widely applied to clinical rehabilitation of the patient with impairment. This is mostly due to insufficient bidirectional information interaction between exoskeleton and patient. The intended action data that can be extracted from surface electromyography (sEMG) signal may include the intended posture, intended torque, intended knee joint angle and intended desired impedance of the patient. Capturing intended knee joint torque from sEMG signal is one of the necessary parameter to achieve a smooth Human Machine Interaction force in a multilayer control mechanism. In this paper, a new technique to estimate Knee joint torque using SVM has been proposed that has used wavelet feature. The estimator is able to estimate required knee joint torque to lift 5kg, 12kg and 19kg weight using extensor and flexor muscles. Based on weight-Torque relationship, greater the weight, greater the torque is required to lift the weight. The estimator has classified required torque of 5kg, 12kg and 19kg with accuracy of 98.7296%, 86.0254% and 95.6443% respectively. The estimator can also be used to estimate torque about knee joint at different joint angle.	en_US
dc.relation.ispartof	2016 International Conference on Systems in Medicine and Biology, ICSMB 2016	en_US
dc.relation.isbasedon	10.1109/ICSMB.2016.7915117	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	EMG signal based knee joint torque estimation	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical 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. Although Lower Limb Robotic Rehabilitation device exhibit a great prospect in the rehabilitation of impaired limb, yet it has not been widely applied to clinical rehabilitation of the patient with impairment. This is mostly due to insufficient bidirectional information interaction between exoskeleton and patient. The intended action data that can be extracted from surface electromyography (sEMG) signal may include the intended posture, intended torque, intended knee joint angle and intended desired impedance of the patient. Capturing intended knee joint torque from sEMG signal is one of the necessary parameter to achieve a smooth Human Machine Interaction force in a multilayer control mechanism. In this paper, a new technique to estimate Knee joint torque using SVM has been proposed that has used wavelet feature. The estimator is able to estimate required knee joint torque to lift 5kg, 12kg and 19kg weight using extensor and flexor muscles. Based on weight-Torque relationship, greater the weight, greater the torque is required to lift the weight. The estimator has classified required torque of 5kg, 12kg and 19kg with accuracy of 98.7296%, 86.0254% and 95.6443% respectively. The estimator can also be used to estimate torque about knee joint at different joint angle.

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http://hdl.handle.net/10453/127301