A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

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.

Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness

“…A few studies on a small number of drivers have evaluated ECG-based indicators like heart rate and HRV with promising results, especially for HRV (Patel et al 2011;Sato et al 2001;Vicente et al 2016). Using indicators based on the heart rate signal can open up possibilities to use nonintrusive measurement devices that can be suitable for real-life sleepy driver alert systems, in contrast to EEG/electromyogram/ECG measurements that are suitable only in experimental settings, although progress is being made for improving wearability by, for example, combining EEG and ECG using only 2 electrodes (Awais et al 2017). Heart rate could be measured by sensors in the driving wheel, wristbands, and sensors mounted in the seat using techniques such as bioimpedance, contactless recordings of ECG, and microwave technology (Macias et al 2013;Wartzek et al 2011).…”

Using EEG for Mental Fatigue Assessment: A Comprehensive Look Into the Current State of the Art

“…Adaptive filters are also viable digital options for noise and artifact rejection [80]. Experts also use visual inspection to remove artifacts [81], [82]. This approach is especially effective when relying on data from other sensors, such as EMG and EOG [83].…”

“…As in the frequency domain, SVM is extensively applied as a clustering method, to group the input signals in different MF state classes based on non-linear features. Only non-linear kernels were used for SVM in the surveyed works [81], [82], [91]. SVR was also used together with a continuous conditional neural field (CCNF) and a continuous conditional random field (CCRF) to produce a dynamic estimator of the MF state [89].…”

Gaze and Eye Tracking: Techniques and Applications in ADAS

“…Researchers have exploited the features of eye movement data for driver's distraction and drowsiness detection [169,170]. The following features related to eyeball and eyelid movements are frequently used in this field [171][172][173][174].…”