Abstract
Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.
Highlights
Safety has received an unprecedented amount of research attention in recent years, in relation to transportation
Karolinska Sleepiness Scale (KSS) rating levels at the start and the end of the Monotonous Driving (MD) session were analyzed to study the effects of drowsiness on subjective ratings
Data was divided into two groups: the alert group; and the drowsy group
Summary
Safety has received an unprecedented amount of research attention in recent years, in relation to transportation. Transportation safety research has typically focused on increasing the level of safety for drivers and their surroundings, aiming to reduce the high number of road accidents. Recent studies [1,2] have shown that drowsiness at the wheel is a major contributing factor in road. A study conducted by the National Highway Traffic Safety Administration (NHTSA) in the United States reported 416,000 drowsiness-related accidents from 2005 to 2009 [1]. Another study conducted by the Centre for Accident Research and Road Safety in Queensland, Australia [2], reported that drowsiness was one of the leading factors contributing to road accidents, among other factors including speeding and drinking. Due to the subjective nature of accident reporting, many crashes are not verified by the police, suggesting that accidents due to drowsiness may be substantially more common than has been estimated in the past [2]
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