Dependence independence measure for posterior and anterior EMG sensors used in simple and complex finger flexion movements: Evaluation using SDICA

Naik, GR; Baker, KG; Nguyen, HT

Dependence independence measure for posterior and anterior EMG sensors used in simple and complex finger flexion movements: Evaluation using SDICA

Naik, GR

Baker, KG Nguyen, HT

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Publication Type:: Journal Article
Citation:: IEEE Journal of Biomedical and Health Informatics, 2015, 19 (5), pp. 1689 - 1696
Issue Date:: 2015-09-01

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Field	Value	Language
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Baker, KG	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2015-09-01	en_US
dc.identifier.citation	IEEE Journal of Biomedical and Health Informatics, 2015, 19 (5), pp. 1689 - 1696	en_US
dc.identifier.issn	2168-2194	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37103
dc.description.abstract	© 2015 IEEE. Identification of simple and complex finger flexion movements using surface electromyography (sEMG) and a muscle activation strategy is necessary to control human-computer interfaces such as prosthesis and orthoses. In order to identify these movements, sEMG sensors are placed on both anterior and posterior muscle compartments of the forearm. In general, the accuracy of myoelectric classification depends on several factors, which include number of sensors, features extraction methods, and classification algorithms. Myoelectric classification using a minimum number of sensors and optimal electrode configuration is always a challenging task. Sometimes, using several sensors including high density electrodes will not guarantee high classification accuracy. In this research, we investigated the dependence and independence nature of anterior and posterior muscles during simple and complex finger flexion movements. The outcome of this research shows that posterior parts of the hand muscles are dependent and hence responsible for most of simple finger flexion. On the other hand, this study shows that anterior muscles are responsible for most complex finger flexion. This also indicates that simple finger flexion can be identified using sEMG sensors connected only on anterior muscles (making posterior placement either independent or redundant), and vice versa is true for complex actions which can be easily identified using sEMG sensors on posterior muscles. The result of this study is beneficial for optimal electrode configuration and design of prosthetics and other related devices using a minimum number of sensors.	en_US
dc.relation.ispartof	IEEE Journal of Biomedical and Health Informatics	en_US
dc.relation.isbasedon	10.1109/JBHI.2014.2340397	en_US
dc.subject.mesh	Fingers	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Movement	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.title	Dependence independence measure for posterior and anterior EMG sensors used in simple and complex finger flexion movements: Evaluation using SDICA	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	19	en_US
utslib.for	090303 Biomedical Instrumentation	en_US
utslib.for	090602 Control Systems, Robotics and Automation	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 Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© 2015 IEEE. Identification of simple and complex finger flexion movements using surface electromyography (sEMG) and a muscle activation strategy is necessary to control human-computer interfaces such as prosthesis and orthoses. In order to identify these movements, sEMG sensors are placed on both anterior and posterior muscle compartments of the forearm. In general, the accuracy of myoelectric classification depends on several factors, which include number of sensors, features extraction methods, and classification algorithms. Myoelectric classification using a minimum number of sensors and optimal electrode configuration is always a challenging task. Sometimes, using several sensors including high density electrodes will not guarantee high classification accuracy. In this research, we investigated the dependence and independence nature of anterior and posterior muscles during simple and complex finger flexion movements. The outcome of this research shows that posterior parts of the hand muscles are dependent and hence responsible for most of simple finger flexion. On the other hand, this study shows that anterior muscles are responsible for most complex finger flexion. This also indicates that simple finger flexion can be identified using sEMG sensors connected only on anterior muscles (making posterior placement either independent or redundant), and vice versa is true for complex actions which can be easily identified using sEMG sensors on posterior muscles. The result of this study is beneficial for optimal electrode configuration and design of prosthetics and other related devices using a minimum number of sensors.

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http://hdl.handle.net/10453/37103