To overcome the limitations of contact sensors, computational psychophysiology based on thermal infrared (IR) imaging has been assessed as a solution for the quantitative evaluation of several parameters associated with ANS activity.
The possibility to assess human factors through non-contact technology makes thermal IR imaging perfectly suitable for in cabin monitoring. Recently, we developed machine learning-based models to classify driver’s stress, drowsiness, thermal comfort, and cognitive workload.
In this talk, we will show the latest advancements in the field.