Modelling driving performance using implicit interaction

The current project has been realized in collaboration with EIT Digital and Philips Research as part of the high impact initiative in the health & well-being action line. The challenge we are facing in the present work is to design a system for professional truck drivers that monitors driving behavior and predicts vigilance degradation. The research ended with defining parameters that can model drowsiness, fatigue, stress, aggressiveness and driver inattentiveness. The final proposal includes an in-vehicle system that does not impede the drivers' primary or secondary tasks, requires no explicit user input and provides feedback that promotes driving awareness and safer on-road behavior. The system is being designed to support user identification, personal profiles, driving performance monitoring and context-aware interaction for providing personalized and relevant to the circumstances feedback. In order to reach the desired conclusion, we initially conducted a literature review on advanced human-computer interaction and intelligent systems models and we present a model-based interface that supports the desired functionalities. The work also included comparison of cutting edge technologies for affective computing and driver modelling. Due to the nature of the agreement with Philips, we are not authorized to disclose any information that relate to user studies, thus the reader is presented with hypothetical scenarios for system output and user feedback that remain to be verified. These scenarios have been shaped with the help of technology acceptance and data privacy academic papers as well as deep understanding of the driving related context