Towards Recognition of Driver Drowsiness States by Using ECG Signals

Abstract

Drowsy driving is one of the leading causes of car accidents that can result in great loss and tragedy, which could be prevented with early warning. Recent work has used behavioral, physiological, and driving skill traits that are present during drowsiness, such as yawning, closed eyes, decreased heart rate, and sudden steering wheel movements. From these traits, features can be extracted to be used in machine learning (ML) models for the automatic detection of the state of drowsiness. On the other hand, the study of fatigue or sleepiness in real settings leads to risks by exposing test subjects to states of non-alertness. In the present work, it is proposed to use a combination of features extracted from physiological signals, captured with a wearable ECG sensor (Polar H10) during a simulated driving environment, for building and evaluating ML-based models in order to classify different levels of drowsiness. These levels were recorded by self-report using the Karolinska Sleepiness Scale. An accuracy of 76.5% was archived with kNN when classifying drowsiness in 2 levels and 70.5% using Random Forest when classifying drowsiness in 3 levels. The results obtained are promising despite the fact that only physiological type traits were processed.