Strengthening association between driver drowsiness and its physiological predictors by combining EEG with measures of body movement

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

Strengthening association between driver drowsiness and its physiological predictors by combining EEG with measures of body movement

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

Burton, D

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Publication Type:: Conference Proceeding
Citation:: IB2COM 2011 - 6th International Conference on Broadband Communications and Biomedical Applications, Program, 2011, pp. 103 - 107
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	Lal, S https://orcid.org/0000-0002-0911-0850	en_US
dc.contributor.author	Burton, D	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. 103 - 107	en_US
dc.identifier.isbn	9781467307680	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19275
dc.description.abstract	Driver fatigue is acknowledged as a major contributing factor in motor vehicle accidents that result in serious injuries or death. As a result it is valuable to develop driver drowsiness monitoring and warning systems. Many systems that are currently being developed utilise a single source of data to evaluate drowsiness level, however it is anticipated that using hybrid data sources would increase the accuracy of such devices. The objective of this analysis was to determine if using a combination of EEG and body movement parameters would increase the ability to accurately predict the graduated driver drowsiness levels compared to EEG signals alone. Addition of the body movement data has increased the goodness of fit in modelling the average driver drowsiness using a linear regression (R 2 = 0.272 for EEG alone and R 2 = 0.308 for EEG and body movement combined). © 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.2011.6217901	en_US
dc.title	Strengthening association between driver drowsiness and its physiological predictors by combining EEG with measures of 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 as a major contributing factor in motor vehicle accidents that result in serious injuries or death. As a result it is valuable to develop driver drowsiness monitoring and warning systems. Many systems that are currently being developed utilise a single source of data to evaluate drowsiness level, however it is anticipated that using hybrid data sources would increase the accuracy of such devices. The objective of this analysis was to determine if using a combination of EEG and body movement parameters would increase the ability to accurately predict the graduated driver drowsiness levels compared to EEG signals alone. Addition of the body movement data has increased the goodness of fit in modelling the average driver drowsiness using a linear regression (R 2 = 0.272 for EEG alone and R 2 = 0.308 for EEG and body movement combined). © 2011 IEEE.

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http://hdl.handle.net/10453/19275