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

Rami N Khushaba¹, Sarath Kodagoda, Sara Lal

¹ARC 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

IEEE Transactions on Bio-Medical Engineering

|September 23, 2010

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This study introduces an efficient fuzzy mutual-information based wavelet packet transform (FMIWPT) to detect driver drowsiness from physiological signals. The method achieved high accuracy in classifying drowsiness levels, enhancing road safety.

Area of Science:

Neuroscience and Biomedical Engineering
Road Safety and Human Factors

Background:

Driver drowsiness and vigilance loss are significant contributors to road accidents.
Monitoring physiological signals offers a potential method for detecting and warning drivers of fatigue.

Purpose of the Study:

To maximize drowsiness-related information from electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals.
To develop an efficient feature-extraction method for classifying driver drowsiness states.

Main Methods:

Development of a fuzzy mutual-information (MI)-based wavelet packet transform (FMIWPT) for feature extraction.
Estimation of MI using a novel fuzzy membership approach for accurate information content measurement.
Classification of driver drowsiness states using extracted features from simulation driving test data.

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Main Results:

The FMIWPT method demonstrated significance in extracting features highly correlated with different drowsiness levels.
Classification accuracy ranged from 95% to 97% on average across all 31 subjects.
The novel fuzzy MI estimation provided an accurate measure of information content.

Conclusions:

The FMIWPT method is effective for extracting drowsiness-related information from physiological signals.
Accurate classification of driver drowsiness states can be achieved, potentially improving road safety.
The fuzzy MI approach offers a robust way to assess information content in physiological signals for fatigue detection.