A new hybrid and optimized algorithm for drivers’ drowsiness detection

Abstract

When the roads are monotonous, especially on the highways, the state of vigilance decreases and the state of drowsiness appears. Drowsiness is defined as the transitional phase from the awake to the sleepy state. However, In Morocco, the majority of fatal accidents on the highway are caused by drowsiness at the wheel, reaching 33.33% rate. Therefore, we proposed the conception and realization of an automatic method based on electroencephalogram (EEG) signals that can predict drowsiness in real time. The proposed work is based on time-frequency analysis of EEG signals from a single channel (FP1-Ref), and drowsiness is predicted using a personalized 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.

Machine learning-based stress classification system using wearable sensor devices

“…We rely on relative band power instead of absolute band power since a stress response corresponds to the change in actual band power relative to the total power. The relative band is defined as (9).…”

“…In this study, our primary focus is on four physiological signals -electroencephalogram (EEG), electrodermal activity (EDA), skin temperature (SKT), and heart rate (HR). Numerous studies use EEG for applications such as assistive smart homes [7], seizure detection [8], driver drowsiness [9], classification of autism [10] and mental stress detection. EEG can capture localized signals of the brain regions that generate the stress response and can give many robust features for assessing stress [11].…”