Fatigue driving detection based on electrooculography: a review

Abstract

To accurately identify fatigued driving, establishing a monitoring system is one of the important guarantees of improving traffic safety and reducing traffic accidents. Among many research methods, electrooculogram signal (EOG) has unique advantages. This paper presents a systematic literature review of these technologies and summarizes a basic framework of fatigue driving monitoring system based on EOGs. Then we summarize the advantages and disadvantages of existing technologies. In addition, 80 primary references published during the last decade were identified. The multi-feature fusion technique based on EOGs performs better than other traditional methods due to its low cost, low power consumption and low intrusion, while its application is still limited which needs more efforts to obtain good and generalizable results. And then, an overview of the literature on technology is given, revealing a premier and unbiased survey of the existing empirical research of classification techniques that have been applied to fatigue driving analysis. Finally, this paper adds value to the current literature by investigating the application of EOG signals in fatigued driving and the design of related systems, future guidelines have been provided to practitioners and researchers to grasp the major contributions and challenges in the state-of-the-art research.

Exploration of Eye Fatigue Detection Features and Algorithm Based on Eye-Tracking Signal

“…In studies using eye-tracking signals for fatigue detection, the main detection tools available are video signals, electrooculography signals, and multimodal fusion methods. The study of electrooculography (EOG) has been an important aspect of eye fatigue research [9][10][11][12]. The EOG signal is a measurement that uses electrodes placed in four directions: above, below, to the left, and to the right of the eye.…”

Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state

“…Further, adoption of reinforcement learning is reported in work of Langroodi and Nahvi [93] where fuzzy logic has been used together to prove its effectiveness over conventional artificial neural network (ANN) based approaches. Finally, the study carried out by Tian and Cao [94] have discussed about usage of electrooculography which is quite different from other approaches in order to determine fatigueness in driver. According to findings of this study, the aggregated approaches on electrooculography is found to be effective in contrast to other methods.…”

A CNN-Based Wearable System for Driver Drowsiness Detection

“…Before video-based eye-tracking was widely available, electrooculography (EOG) was a technology psychologists used to track human eye movements (see [ 17 ], for a review). In addition to gaze information, the EOG signal also contains abundant information about the eyelid movements which can be used to derive measures sensitive to driver drowsiness, e.g., blinks or PERCLOS (for a recent review, see [ 18 ]). For instance, in a recent study by Xue et al [ 19 ], EOG electrodes were used to extract an eye closure measure, which was then used as a key parameter to assess driver fatigue.…”