Towards Reliable Driver Drowsiness Detection Leveraging Wearables

Driver drowsiness is a significant factor in road crashes. However, existing solutions for driver drowsiness detection have major drawbacks of requiring special hardware, constrained recording conditions, and cannot handle the asynchronous and contradictory nature of multiple indicators. In view of this, we propose FDWatch, a novel drowsiness detection system that exploits the low-cost Photoplethysmogram (PPG) sensor and motion sensor integrated into wrist-worn devices. We design a set of novel algorithms to extract multiple drowsiness-related indicators covering major categories of human factors. In particular, we demonstrate that commodity PPG sensors can be utilized to detect yawning behavior; it contributes as an important indicator for drowsiness detection. The core of FDWatch is based on the Dempster-Shafer evidence theory. It considers different indicators as evidence describing the state of the driver from different angles. To make the extracted indicators applicable to Dempster-Shafer evidence theory, we employ backpropagation neural networks to obtain the basic probability assignment. Moreover, we propose a similarity-distance-based method to handle evidence conflicts. Extensive experiments with real-road driving data show that FDWatch can accurately detect driver drowsiness with a missing alarm rate of 3.57% and a false alarm rate of 3.68%.