In this paper, we address the problem of possible stress conditions arising in car drivers, thus affecting their driving performance. We apply various Machine Learning (ML) algorithms to analyse the stress of subjects while driving in an urban area in two different situations: one with cars, pedestrians and traffic along the course, and the other characterized by the complete absence of any of these possible stress-inducing factors. To evaluate the presence of a stress condition we use two Skin Potential Response (SPR) signals, recorded from each hand of the test subjects, and process them through a Motion Artifact (MA) removal algorithm which reduces the artifacts that might be introduced by the hand movements. We then compute some statistical features starting from the cleaned SPR signal. A binary classification ML algorithm is then fed with these features, giving as an output a label that indicates if a time interval belongs to a stress condition or not. Tests are carried out in a laboratory at the University of Udine, where a car driving simulator with a motorized motion platform has been prearranged. We show that the use of one single SPR signal, along with the application of ML algorithms, enables the detection of possible stress conditions while the subjects are driving, in the traffic and no traffic situations. As expected, we observe that the test individuals are less stressed in the situation without traffic, confirming the effectiveness of the proposed slightly invasive system for detection of stress in drivers.
