Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm

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.

I. Introduction

Drowsiness is an intermediate state between wakefulness and sleep that has been defined as a state of progressive impaired awareness associated with a desire or inclination to sleep [1]. In certain tasks, such as driving, drowsiness is considered as a significant risk factor that substantially contributes to the increasing number of motor vehicle accidents each year [2]. Critical aspects of driving impairments associated with drowsiness are slow reaction times, reduced vigilance, and deficits in information processing that all lead to an abnormal driving behavior [3], [4]. Driver drowsiness is usually used interchangeably with the term driver fatigue; however, each of these terms has its own meaning. Fatigue is considered as one of the factors that can lead to drowsiness and is a consequence of physical labor or a prolonged experience, and is defined as a disinclination to continue the task at hand [5]. Driver fatigue is believed to account for 35%-45% of all vehicle accidents [6]. Some authors distinguish fatigue from drowsiness as the former does not fluctuate rapidly, over periods of a few seconds, as drowsiness. Usually, rest and inactivity relieves fatigue, however, it makes drowsiness worse [7].