Driver stress level detection based on multimodal measurements

Successful driver performance is fundamental in preventing vehicle crashes. Stress can negatively affect driver performance and significantly increase the risk of a crash. Therefore, an in-vehicle warning system for driver stress levels is needed to continuously predict dangerous driving situations and proactively alert drivers to ensure safe and comfortable driving. As a result of the recent developments in sensing technologies and context recognition, driver stress can be detected using multimodal measurements. This thesis proposes a general framework for building a driver stress level detection system based on multimodal measurements and adopts different approaches to maximise the performance of the system