Method of detection of early falling asleep while driving using EOG analysis

Deeb, Jinan; Zakaria, Firas; Kamali, Walid

doi:10.1109/icabme.2017.8167559

Cited by 4 publications

(1 citation statement)

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“…It is thus critical to tell the difference between Sleepiness and dangerous driving because Sleepiness progresses over time and can turn into a real danger at any moment. The rapid recognition of sleep-deficient driver status would probably reduce the total number of accidents in traffic [3]. The current driver drowsiness evaluation approaches were primarily divided into three groups.…”

Section: Introductionmentioning

confidence: 99%

Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer

Jasim

Hassan

2022

IJECE

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<span lang="EN-US">In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) classifiers have been used. The results show that the proposed hybrid method provides 99.6% accuracy, while the SVM classifier provides 93.0% accuracy when the kernel is (RBF) and outlier (0.1). Furthermore, the k-NN classifier provides 96.7% accuracy, whereas the standalone ANN algorithm provides 97.7% accuracy.</span>

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Section: Introductionmentioning

confidence: 99%