Intelligent driver drowsiness detection system using uncorrelated fuzzy locality preserving analysis

Khushaba, RN; Kodagoda, S; Lal, S; Dissanayake, G

Intelligent driver drowsiness detection system using uncorrelated fuzzy locality preserving analysis

Khushaba, RN

Kodagoda, S

Lal, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Intelligent Robots and Systems, 2011, pp. 4608 - 4614
Issue Date:: 2011-12-29

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Field	Value	Language
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Lal, S https://orcid.org/0000-0002-0911-0850	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2011-12-29	en_US
dc.identifier.citation	IEEE International Conference on Intelligent Robots and Systems, 2011, pp. 4608 - 4614	en_US
dc.identifier.isbn	9781612844541	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19225
dc.description.abstract	One of the leading causes of automobile accidents is related to driving impairment due to drowsiness. A large percentage of these accidents occur due to drivers' unawareness of the degree of impairment. An automatic detection of drowsiness levels could lead to lower accidents and hence lower fatalities. However, the significant fluctuations of the drowsiness state within a short time poses a major challenge in this problem. In response to such a challenge, we present the Uncorrelated Fuzzy Locality Preserving Analysis (UFLPA) feature projection method. The proposed UFLPA utilizes the changes in driver behavior, by means of the corresponding Electroencephalogram (EEG), Electrooculogram (EOG), and Electrocardiogram (ECG) signals to extract a set of features that can highly discriminate between the different drowsiness levels. Unlike existing methods, the proposed UFLPA takes into consideration the fuzzy nature of the input measurements while preserving the local discriminant and manifold structures of the data. Additionally, UFLPA also utilizes Singular Value Decomposition (SVD) to avoid the singularity problem and produce a set of uncorrelated features. Experiments were performed on datasets collected from thirty-one subjects participating in a simulation driving test with practical results indicating the significance of the results achieved by UFLPA of 94%-95% accuracy on average across all subjects. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE International Conference on Intelligent Robots and Systems	en_US
dc.relation.isbasedon	10.1109/IROS.2011.6048051	en_US
dc.title	Intelligent driver drowsiness detection system using uncorrelated fuzzy locality preserving analysis	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	San Francisco, USA	en_US
dc.location.activity	San Francisco, CA
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 Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
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/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

One of the leading causes of automobile accidents is related to driving impairment due to drowsiness. A large percentage of these accidents occur due to drivers' unawareness of the degree of impairment. An automatic detection of drowsiness levels could lead to lower accidents and hence lower fatalities. However, the significant fluctuations of the drowsiness state within a short time poses a major challenge in this problem. In response to such a challenge, we present the Uncorrelated Fuzzy Locality Preserving Analysis (UFLPA) feature projection method. The proposed UFLPA utilizes the changes in driver behavior, by means of the corresponding Electroencephalogram (EEG), Electrooculogram (EOG), and Electrocardiogram (ECG) signals to extract a set of features that can highly discriminate between the different drowsiness levels. Unlike existing methods, the proposed UFLPA takes into consideration the fuzzy nature of the input measurements while preserving the local discriminant and manifold structures of the data. Additionally, UFLPA also utilizes Singular Value Decomposition (SVD) to avoid the singularity problem and produce a set of uncorrelated features. Experiments were performed on datasets collected from thirty-one subjects participating in a simulation driving test with practical results indicating the significance of the results achieved by UFLPA of 94%-95% accuracy on average across all subjects. © 2011 IEEE.

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http://hdl.handle.net/10453/19225