Subtle hand gesture identification for HCI using temporal Decorrelation source separation BSS of surface EMG

Naik, GR; Kumar, DK; Weghorn, H; Palaniswami, M

Subtle hand gesture identification for HCI using temporal Decorrelation source separation BSS of surface EMG

Naik, GR

Kumar, DK Weghorn, H Palaniswami, M

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - Digital Image Computing Techniques and Applications: 9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007, 2007, pp. 30 - 37
Issue Date:: 2007-12-01

Closed Access

	Filename	Description	Size
	2012007322OK.pdf		489.1 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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.contributor.author	Palaniswami, M	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	Proceedings - Digital Image Computing Techniques and Applications: 9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007, 2007, pp. 30 - 37	en_US
dc.identifier.isbn	0769530672	en_US
dc.identifier.isbn	9780769530673	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26723
dc.description.abstract	Hand gesture identification has various human computer interaction (HCI) applications. This paper presents a method for subtle hand gesture identification from sEMG of the forearm by decomposing the signal into components originating from different muscles. The processing requires the decomposition of the surface EMG by temporal decorrelation source separation (TDSEP) based blind source separation technique. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other HCI based devices. The proposed model based approach is able to overcome the ambiguity problems (order and magnitude problem) of BSS methods by selecting an a priori mixing matrix based on known hand muscle anatomy. The paper reports experimental results, where the system was able to reliably recognize different subtle hand gesture with an overall accuracy of 97%. The advantage of such a system is that it is easy to train by a lay user, and can easily be implemented in real time after the initial training. The paper also highlights the importance of mixing matrix analysis in BSS technique. © 2007 IEEE.	en_US
dc.relation.ispartof	Proceedings - Digital Image Computing Techniques and Applications: 9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007	en_US
dc.relation.isbasedon	10.1109/DICTA.2007.4426772	en_US
dc.title	Subtle hand gesture identification for HCI using temporal Decorrelation source separation BSS of 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	Glenelg, SA	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:

Hand gesture identification has various human computer interaction (HCI) applications. This paper presents a method for subtle hand gesture identification from sEMG of the forearm by decomposing the signal into components originating from different muscles. The processing requires the decomposition of the surface EMG by temporal decorrelation source separation (TDSEP) based blind source separation technique. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other HCI based devices. The proposed model based approach is able to overcome the ambiguity problems (order and magnitude problem) of BSS methods by selecting an a priori mixing matrix based on known hand muscle anatomy. The paper reports experimental results, where the system was able to reliably recognize different subtle hand gesture with an overall accuracy of 97%. The advantage of such a system is that it is easy to train by a lay user, and can easily be implemented in real time after the initial training. The paper also highlights the importance of mixing matrix analysis in BSS technique. © 2007 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26723