title: Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.
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.
authors.affiliation: ARC Centre of Excellence for Autonomous Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, Broadway NSW 2007, Australia. rkhushab@eng.uts.edu.au
authors.forename: Rami N
authors.initials: RN
authors.lastname: Khushaba
authors.forename: Sarath
authors.initials: S
authors.lastname: Kodagoda
authors.forename: Sara
authors.initials: S
authors.lastname: Lal
authors.forename: Gamini
authors.initials: G
authors.lastname: Dissanayake
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D004568:Electrodiagnosis / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology*