Drowsiness Estimation Model Based on Hemodynamics

Abstract

A large number of traffic fatalities are caused by falling asleep at the wheel. Several drowsiness detection technologies have been developed in recent years. A previous study describes how hemodynamics can vary significantly due to drowsiness. However, it was difficult to estimate drowsiness from the time series of hemodynamics. In this study, general models for estimating three drowsiness levels (i.e., high, medium, and low) based on hemodynamics were constructed using a convolutional neural network for detecting the condition before a state of complete sleepiness is reached, the goal being traffic accident prevention. The results showed that the accuracy of the model was 68.9%.