Personalized Health Navigation for Drivers

To decrease the number of traffic accidents caused by changes in drivers' physical and mental health conditions and accomplish automatic monitoring and real-time optimization of drivers' health states, a closed-loop feedback system framework for drivers' health states was proposed based on the basic theory of cybernetics. First, a personalized health model was established based on a driver's log data. Then by combining this model with the real-time multimodal data of the driver, vehicle and road environment from various sensors, the driver's current health state was estimated. Finally given the health goal of the driver, executable behavior suggestions were provided to navigate the driver to an optimized health state. For the most critical phase of real-time monitoring, a multimodal fusion model based on attentional convolutional neural networks and long short-term memory network (CNN-LSTM) was proposed to estimate the three aspects of driver health, namely, stress, emotion, and fatigue. Experiments on both private and public datasets have achieved a detection accuracy of more than 90%, which demonstrates that the proposed model and methods can accurately monitor drivers' stress, emotion, and fatigue states in real time, thus provide a solid basis for implementing the personalized health navigation system for drivers (PHN-D).