Multi run ICA and surface EMG based signal processing system for recognising hand gestures

Naik, GR; Kumar, DK; Palaniswami, M

Multi run ICA and surface EMG based signal processing system for recognising hand gestures

Naik, GR

Kumar, DK Palaniswami, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008, 2008, pp. 700 - 705
Issue Date:: 2008-09-24

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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	Palaniswami, M	en_US
dc.date.issued	2008-09-24	en_US
dc.identifier.citation	Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008, 2008, pp. 700 - 705	en_US
dc.identifier.isbn	9781424423583	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32005
dc.description.abstract	Hand gesture identification is a complex problem, where more number of muscles will be involved even for a simple hand movement. Surface electromyography (sEMG) is an indicator of muscle activity and related to body movement and posture. In the recent past sEMG had been used with various statistical signal processing technique to identify different hand gestures, but since the hand actions require simultaneous muscle contractions reliability issues exist. Recently Blind source separation (BSS) techniques like Independent Component Analysis (ICA) had been used to tackle this problem. In this paper, a novel method is proposed to enhance the performance of ICA of sEMG by decomposing the signal into components originating from different muscles. First, we use FastICA algorithm to generate random mixing matrix, and the best mixing matrix is chosen based on the highest Signal to interference ratio(SIR) of mixing matrix. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The proposed model-based approach is able to overcome the ambiguity problems (order and magnitude problem) of BSS methods by selecting an apriori mixing matrix based on known hand muscle anatomy. Testing was conducted using several single shot experiments conducted with seven subjects. The results indicate that the system is able to classify six different hand gestures with 99% accuracy. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008	en_US
dc.relation.isbasedon	10.1109/CIT.2008.4594760	en_US
dc.title	Multi run ICA and surface EMG based signal processing system for recognising hand gestures	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	Sydney, NSW
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:

Hand gesture identification is a complex problem, where more number of muscles will be involved even for a simple hand movement. Surface electromyography (sEMG) is an indicator of muscle activity and related to body movement and posture. In the recent past sEMG had been used with various statistical signal processing technique to identify different hand gestures, but since the hand actions require simultaneous muscle contractions reliability issues exist. Recently Blind source separation (BSS) techniques like Independent Component Analysis (ICA) had been used to tackle this problem. In this paper, a novel method is proposed to enhance the performance of ICA of sEMG by decomposing the signal into components originating from different muscles. First, we use FastICA algorithm to generate random mixing matrix, and the best mixing matrix is chosen based on the highest Signal to interference ratio(SIR) of mixing matrix. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The proposed model-based approach is able to overcome the ambiguity problems (order and magnitude problem) of BSS methods by selecting an apriori mixing matrix based on known hand muscle anatomy. Testing was conducted using several single shot experiments conducted with seven subjects. The results indicate that the system is able to classify six different hand gestures with 99% accuracy. © 2008 IEEE.

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