A new hybrid and optimized algorithm for drivers’ drowsiness detection

Mouad ELMOUZOUN ELIDRISSI, Elmaati Essoukaki, Azeddine Mouhsen, Lhoucine Ben Taleb, Mohammed Harmouchi


The human brain generates millions of signals as they translate all our movements and thoughts, our physical and psychological state. While driving, all these signals are generated simultaneously. Vigilance at the wheel is necessary. However, when the roads are monotonous, especially on the highways, this state of vigilance decreases, and the state of drowsiness appears. In Morocco, 1/3 of fatal accidents on the highway are caused by drowsiness and sleepiness at the wheel. Therefore, we proposed the idea of developing an automatic system based on electroencephalogram (EEG) signals that can predict the state of drowsiness in real-time using several features extracted from EEG recordings when this state occurs in drivers while driving. The proposed work is based on time-frequency analysis of EEG signals from a single channel (FP1-Ref), and drowsiness is predicted using a modified and optimized machine learning model (optimized decision tree classification method) under Python. The results are much significant and optimized, improving the accuracy from 95.7\% to 96.4\% and a time consuming from 0.065 to 0.053 seconds.


Drivers’ drowsiness automatic detection; EEG signals analysis; Road safety; Machine Learning; Hybrid algorithm; Real-time analysis

DOI: http://doi.org/10.11591/ijai.v11.i3.pp%25p


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