Comparing accuracy of two algorithms for detecting driver drowsiness Single source (EEG) and hybrid (EEG and body movement)

Pritchett, S; Zilberg, E; Xu, ZM; Karrar, M; Burton, D; Lal, S

Comparing accuracy of two algorithms for detecting driver drowsiness Single source (EEG) and hybrid (EEG and body movement)

Pritchett, S Zilberg, E Xu, ZM Karrar, M Burton, D Lal, S

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Publication Type:: Conference Proceeding
Citation:: IB2COM 2011 - 6th International Conference on Broadband Communications and Biomedical Applications, Program, 2011, pp. 179 - 184
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Pritchett, S	en_US
dc.contributor.author	Zilberg, E	en_US
dc.contributor.author	Xu, ZM	en_US
dc.contributor.author	Karrar, M	en_US
dc.contributor.author	Burton, D	en_US
dc.contributor.author	Lal, S https://orcid.org/0000-0002-0911-0850	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	IB2COM 2011 - 6th International Conference on Broadband Communications and Biomedical Applications, Program, 2011, pp. 179 - 184	en_US
dc.identifier.isbn	9781467307680	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19273
dc.description.abstract	Driver fatigue is acknowledged to have similar effects on driving performance as driving under the influence of alcohol. As such, drowsiness detection systems should prove to be valuable in-vehicle safety measures. There are many algorithms that are currently being developed for this purpose, however, they often utilise a single source of data to detect drowsiness onset. It is anticipated that using hybrid data sources would increase the accuracy of such devices. The objective of this analysis was to compare the performance of a hybrid drowsiness detection algorithm with its single source counterpart. Addition of the body movement data to form a hybrid algorithm improved drowsiness detection performance over its EEG only (single source) counterpart, such that area under the ROC curve values increased from 0.764 (single source) to 0.783 (hybrid). © 2011 IEEE.	en_US
dc.relation.ispartof	IB2COM 2011 - 6th International Conference on Broadband Communications and Biomedical Applications, Program	en_US
dc.relation.isbasedon	10.1109/IB2Com.2011.6217916	en_US
dc.title	Comparing accuracy of two algorithms for detecting driver drowsiness Single source (EEG) and hybrid (EEG and body movement)	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	Melbourne, Australia	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/Faculty of Science/School of Medical and Molecular Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Driver fatigue is acknowledged to have similar effects on driving performance as driving under the influence of alcohol. As such, drowsiness detection systems should prove to be valuable in-vehicle safety measures. There are many algorithms that are currently being developed for this purpose, however, they often utilise a single source of data to detect drowsiness onset. It is anticipated that using hybrid data sources would increase the accuracy of such devices. The objective of this analysis was to compare the performance of a hybrid drowsiness detection algorithm with its single source counterpart. Addition of the body movement data to form a hybrid algorithm improved drowsiness detection performance over its EEG only (single source) counterpart, such that area under the ROC curve values increased from 0.764 (single source) to 0.783 (hybrid). © 2011 IEEE.

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http://hdl.handle.net/10453/19273