Developing a System for High-Resolution Detection of Driver Drowsiness Using Physiological Signals

Background: This research aims to develop a high-resolution, reliable, and efficient drowsiness detection system. Existing systems for detecting drowsiness are of low-resolution, expensive, dependent on external parameters, or are inconvenient for the driver. Method: Two studies were conducted: First, we analyzed electroencephalogram (EEG) data collected during a sleep study to develop a high-resolution drowsiness detection algorithm. This algorithm was then tested in a second study that actively engaged participants in a reaction time task. Results: In the sleep study, a sigmoid wake probability model yielded high drowsiness detection rates. In the reaction time study, however, the same method showed low sensitivity. Instead, a time-domain feature based algorithm performed best with high accuracy, high sensitivity, and high specificity. Significance: Upon successful validation of the developed algorithm in a driving study, this research will help to develop a reliable, wearable, and convenient device to detect drowsy driving that could increase road safety.