Indicator of the quality of the sensor set up: A study using surface EMG on sub-band ICA

Naik, GR; Kumar, DK

Indicator of the quality of the sensor set up: A study using surface EMG on sub-band ICA

Naik, GR

Kumar, DK

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Publication Type:: Journal Article
Citation:: International Journal of Innovative Computing, Information and Control, 2011, 7 (3), pp. 1285 - 1298
Issue Date:: 2011-03-01

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Field	Value	Language
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Kumar, DK	en_US
dc.date.issued	2011-03-01	en_US
dc.identifier.citation	International Journal of Innovative Computing, Information and Control, 2011, 7 (3), pp. 1285 - 1298	en_US
dc.identifier.issn	1349-4198	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27572
dc.description.abstract	Surface electromyogram (sEMG) is a non-invasive recording and it has numerous applications. Multiple electrical sensors are essential for extracting intrinsic physiological and contextual information from the corresponding sEMG signals. The reason, why more than just one sEMG signal capture has to be used, is as follows: Due to signal propagation inside the human body in terms of an electrical conductor, there cannot be a one-to-one mapping of activities between muscle fibre groups and corresponding sEMG sensing electrodes. Each of such electrodes rather records a composition of many, and widely activity-independent signals, and such kind of raw signal capture cannot be efficiently used for pattern matching due to its linear dependency. On the other hand, Independent Component Analysis (ICA) provides the perfect answer of un-mixing a set of skin surface recordings into a vector (set) of independent muscle actions. Hence, there is a need for a method that indicates the quality of the sensor set in sEMG recording. The purpose of this paper is to describe the use of source separation for sEMG based on ICA. We demonstrate how this can be used in practical sEMG experiments, when the number of recording channels for electrical muscle activities is varied. Keywords: Hand gesture sensing, Bio-signal analysis, Independent component analysis (ICA), Surface electromyography (sEMG), Blind source separation (BSS). © 2011 ICIC International.	en_US
dc.relation.ispartof	International Journal of Innovative Computing, Information and Control	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Indicator of the quality of the sensor set up: A study using surface EMG on sub-band ICA	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	7	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 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
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

Surface electromyogram (sEMG) is a non-invasive recording and it has numerous applications. Multiple electrical sensors are essential for extracting intrinsic physiological and contextual information from the corresponding sEMG signals. The reason, why more than just one sEMG signal capture has to be used, is as follows: Due to signal propagation inside the human body in terms of an electrical conductor, there cannot be a one-to-one mapping of activities between muscle fibre groups and corresponding sEMG sensing electrodes. Each of such electrodes rather records a composition of many, and widely activity-independent signals, and such kind of raw signal capture cannot be efficiently used for pattern matching due to its linear dependency. On the other hand, Independent Component Analysis (ICA) provides the perfect answer of un-mixing a set of skin surface recordings into a vector (set) of independent muscle actions. Hence, there is a need for a method that indicates the quality of the sensor set in sEMG recording. The purpose of this paper is to describe the use of source separation for sEMG based on ICA. We demonstrate how this can be used in practical sEMG experiments, when the number of recording channels for electrical muscle activities is varied. Keywords: Hand gesture sensing, Bio-signal analysis, Independent component analysis (ICA), Surface electromyography (sEMG), Blind source separation (BSS). © 2011 ICIC International.

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http://hdl.handle.net/10453/27572