Performance comparison of ICA algorithms for isometric hand gesture identification using surface EMG

Naik, GR; Kumar, DK; Weghorn, H

Performance comparison of ICA algorithms for isometric hand gesture identification using surface EMG

Naik, GR

Kumar, DK Weghorn, H

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP, 2007, pp. 613 - 618
Issue Date:: 2007-12-01

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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.contributor.author	Weghorn, H	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP, 2007, pp. 613 - 618	en_US
dc.identifier.isbn	1424415020	en_US
dc.identifier.isbn	9781424415021	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26719
dc.description.abstract	There is an urgent need for developing a robust technique that can identify small and subtle hand and other body movements with applications in health, rehabilitation and defence. Surface electromyogram (sEMG) is a measure of the electrical activity of the muscles and a measure of the strength of muscle contraction. While this may be a good measure of the actions and gestures, this is unable to identify small variations in the muscle activity, especially when there are number of simultaneously active muscles. Independent component analysis (ICA) is a statistical based source separation technique that has been shown to be suitable for the decomposition of signals such as sEMG and been shown to improve the ability of sEMG to identify small variations in muscle activity. ICA algorithms using multivariate statistical data analysis technique have been successfully used for signal extraction and source separation in the field of biomedical and statistical signal processing. Recent research has resulted in the development of number of different ICA technique. While there are some researchers who have compared their techniques with the existing methods for audio examples, there is no comparison of performance between ICA algorithms for biosignal applications such as surface electromyography (sEMG) applications. With ICA being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper has studied the performance of four ICA algorithms (FastICA, JADE, Infomax and TDSEP) for decomposition of sEMG to identify subtle hand gestures. Comparing several ICA algorithms, it is observed that an algorithm based on temporal decorrelation method (TDSEP) which is based on the second order statistics gives the best performance. © 2007 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP	en_US
dc.relation.isbasedon	10.1109/ISSNIP.2007.4496913	en_US
dc.title	Performance comparison of ICA algorithms for isometric hand gesture identification using surface EMG	en_US
dc.type	Conference Proceeding
utslib.for	090609 Signal Processing	en_US
utslib.for	090302 Biomechanical Engineering	en_US
utslib.for	090399 Biomedical Engineering not elsewhere classified	en_US
dc.location.activity	Melbourne, VIC	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.publication-status	Published	en_US

Abstract:

There is an urgent need for developing a robust technique that can identify small and subtle hand and other body movements with applications in health, rehabilitation and defence. Surface electromyogram (sEMG) is a measure of the electrical activity of the muscles and a measure of the strength of muscle contraction. While this may be a good measure of the actions and gestures, this is unable to identify small variations in the muscle activity, especially when there are number of simultaneously active muscles. Independent component analysis (ICA) is a statistical based source separation technique that has been shown to be suitable for the decomposition of signals such as sEMG and been shown to improve the ability of sEMG to identify small variations in muscle activity. ICA algorithms using multivariate statistical data analysis technique have been successfully used for signal extraction and source separation in the field of biomedical and statistical signal processing. Recent research has resulted in the development of number of different ICA technique. While there are some researchers who have compared their techniques with the existing methods for audio examples, there is no comparison of performance between ICA algorithms for biosignal applications such as surface electromyography (sEMG) applications. With ICA being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper has studied the performance of four ICA algorithms (FastICA, JADE, Infomax and TDSEP) for decomposition of sEMG to identify subtle hand gestures. Comparing several ICA algorithms, it is observed that an algorithm based on temporal decorrelation method (TDSEP) which is based on the second order statistics gives the best performance. © 2007 IEEE.

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