Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer

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.