Estimation of angle based on EMG using ANFIS

Anwar, T; Al Jumaily, A

Estimation of angle based on EMG using ANFIS

Anwar, T Al Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, 2017
Issue Date:: 2017-02-09

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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	2017-02-09	en_US
dc.identifier.citation	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, 2017	en_US
dc.identifier.isbn	9781509042401	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125918
dc.description.abstract	© 2016 IEEE. There are wide verities of human movement possible that involves a range from the gait of the physically handicapped, the lifting of a load by a factory worker to the performance of a superior athlete. Output of the movement can be described by a large number of kinematic variables. Modeling each case with a muscle model is difficult. Intended action data can also be extracted from surface Electromyography (EMG) signal which may include intended torque, angle and impedance parameters of the knee joint dynamics. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used in trying to estimate angle. As EMG signal is a function of angle, velocity and muscle activation level (load lifted), an adaptive machine learning technique is most desirable. Many different EMG signal intensity is possible at the same extension angle for different velocity of lower limb movement about knee joint. The EMG signal has been extracted from two different muscles and their patterns are very unique from velocity to velocity for entire range of extension angle. So a learning method of a Neural structure whose connections are based on rules is required to be able to estimate the angle at various speed about the knee joint as the slope of EMG signal intensity for each case of velocity varies significantly. The EMG signal has been collected from volunteer who has completed the knee joint extension in 15 Sec, 10 Sec, 8 Sec, 5 Sec, 3 Sec, 1 Sec, 0.5 Sec and 0.35 Sec respectively. RMS feature has been used to smooth the raw EMG signal. ANFIS is able to estimate angle adaptively although EMG pattern is changing with respect to speed. The simulation has shown experiment of comparative performance of angle estimation by different membership function and features.	en_US
dc.relation.ispartof	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016	en_US
dc.relation.isbasedon	10.1109/SSCI.2016.7850065	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Estimation of angle based on EMG using ANFIS	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. There are wide verities of human movement possible that involves a range from the gait of the physically handicapped, the lifting of a load by a factory worker to the performance of a superior athlete. Output of the movement can be described by a large number of kinematic variables. Modeling each case with a muscle model is difficult. Intended action data can also be extracted from surface Electromyography (EMG) signal which may include intended torque, angle and impedance parameters of the knee joint dynamics. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used in trying to estimate angle. As EMG signal is a function of angle, velocity and muscle activation level (load lifted), an adaptive machine learning technique is most desirable. Many different EMG signal intensity is possible at the same extension angle for different velocity of lower limb movement about knee joint. The EMG signal has been extracted from two different muscles and their patterns are very unique from velocity to velocity for entire range of extension angle. So a learning method of a Neural structure whose connections are based on rules is required to be able to estimate the angle at various speed about the knee joint as the slope of EMG signal intensity for each case of velocity varies significantly. The EMG signal has been collected from volunteer who has completed the knee joint extension in 15 Sec, 10 Sec, 8 Sec, 5 Sec, 3 Sec, 1 Sec, 0.5 Sec and 0.35 Sec respectively. RMS feature has been used to smooth the raw EMG signal. ANFIS is able to estimate angle adaptively although EMG pattern is changing with respect to speed. The simulation has shown experiment of comparative performance of angle estimation by different membership function and features.

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http://hdl.handle.net/10453/125918