The Potential of Wrist-Worn Wearables for Driver Drowsiness Detection: A Feasibility Analysis

Abstract

Drowsiness is a major cause of fatal traffic accidents. Automated driving is intended to counteract this problem, but in the lower levels of automation, the driver is still responsible as a fallback. Current drowsiness detection methods are often based on driving behavior parameters. Since the automation of the driving task reduces the scope of use of these parameters, alternatives are necessary. Particularly methods that include physiological signals seem to be auspicious. However, inside a vehicle, only non- or minimally intrusive measurement techniques are allowed. In this work, a machine learning-based driver drowsiness detection method is presented applied solely to physiological data from non-intrusive wrist-worn smart wearable devices. A user study (N=30) on a test track with SAE level-2 automated driving was conducted where heart rate data with three commercially available fitness trackers were recorded. Different machine learning models were tested in a 2- and 3-level classification of drowsiness. For both cases and with all tested devices, high accuracies (>90%) could be achieved. The proposed methodology provides new options for the development of intelligent driver-vehicle interaction concepts and interfaces, especially for driver drowsiness detection on the way to fully automating the driving task.