A reasoning-based framework for car driver’s stress prediction

In this work, we present a novel methodology based on a dynamic Bayesian network for the estimation of car drivers stress produced due to specific driving events. the proposed methodology monitors driverpsilas stress using selected biosignals and provides a probabilistic framework in order to infer the driving events resulting in stress level increase. We conducted a series of experiments under real driving conditions. The extracted results indicate a strong correlation between the level of the stress as reported by the driver and the outcome of our model.