Detecting Early Signs of Drowsy Driving using an Artificial Neural Network on Electrocardiography Signals

Abstract

Objective: This study developed an artificial neural network (ANN) model that can detect an early sign of drowsy driving (fighting-off drowsiness) based on electrocardiography (ECG) signals.<BR>Background: Detecting an early state of drowsy driving is very important to prevent vehicle accidents on the road by providing appropriate interventions to the driver.<BR>Method: The ECG signals for forty-three participants (mean age: 23.1, SD: 1.6) were recorded while performing a simulator-based monotonous driving for 20 minutes, and the ECG for twenty participants (mean age: 23.2, SD: 1.3) who suffered drowsiness were used in further analysis. The three driver states (normal, fighting-off drowsiness, and drowsy) were determined through participant