SafeWatch

Abstract

Driving while distracted or losing alertness significantly increases the risk of traffic accident. The emerging Internet of Things (IoT) systems for smart driving hold the promise of significantly reducing road accidents. In particular, detecting unsafe hand motions and warning the driver using smart sensors can improve the driver’s alertness and skill. However, due to the impact of the vehicle’s movement and the significant variation across different driving environments, detecting the position of the driver’s hand is challenging. This article presents SafeWatch—a system based on smartwatches and smartphones that detects the driver’s unsafe behaviors in a real-time manner. SafeWatch infers driver’s hand position based on several important features, such as the posture of the driver’s forearm and the vibration on the smartwatch. SafeWatch employs a novel adaptive training algorithm that keeps updating the training data set at run-time based on inferred hand positions in certain driving conditions. The evaluation with 75 real driving trips from six subjects shows that SafeWatch has a high accuracy over 97.0% for both recall and precision in detection of the unsafe hand positions when the condition lasts for more than 6.0 s , as well as over 97.1% recall and over 91.0% precision in detection of the unsafe hand movements when it lasts for more than 2.5 s . The relative position of the hand to the steering wheel also reveals some detailed driving habits, like the type of steering method.