Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm

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

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm

Khushaba, RN

Kodagoda, S

Lal, S

Dissanayake, G

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Biomedical Engineering, 2011, 58 (1), pp. 121 - 131
Issue Date:: 2011-01-01

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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	2011-01-01	en_US
dc.identifier.citation	IEEE Transactions on Biomedical Engineering, 2011, 58 (1), pp. 121 - 131	en_US
dc.identifier.issn	0018-9294	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15352
dc.description.abstract	Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-97% on an average across all subjects. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Biomedical Engineering	en_US
dc.relation.isbasedon	10.1109/TBME.2010.2077291	en_US
dc.subject.classification	Biomedical Engineering	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electrodiagnosis	en_US
dc.subject.mesh	Sleep Stages	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Fuzzy Logic	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Aged	en_US
dc.subject.mesh	Middle Aged	en_US
dc.subject.mesh	Male	en_US
dc.title	Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	58	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	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 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.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	58	en_US

Abstract:

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-97% on an average across all subjects. © 2011 IEEE.

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http://hdl.handle.net/10453/15352