Citation

Khushaba RN, Kodagoda S, Lal S, Dissanayake G. IEEE Trans. Biomed. Eng. 2011; 58(1): 121-131.

Copyright

(Copyright © 2011, Institute of Electrical and Electronic Engineers)

DOI

10.1109/TBME.2010.2077291

PMID

20858575

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 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 mutual information 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 thirty-one 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 average across all subjects.

Language: en