Physiology-based Affect Recognition During Driving in Virtual Environment for Autism Intervention

Independent driving is believed to be an important factor of quality of life for individual with autism spectrum disorder (ASD). In recent years, several computer technologies, particularly Virtual Reality (VR), have been explored to improve driving skills in this population. In this work a VR-based driving environment was developed for skill training for teenagers with ASD. Eight channels of physiological signals were recorded in real time for affect recognition during driving. A large set of physiological features were investigated to determine their correlation with four categories of affective states: engagement, enjoyment, frustration and boredom, of teenagers with ASD. In order to have reliable reference points to link the physiological data with the affective states, the subjective reports from a therapist were recorded and analyzed. Six well-known classifiers were used to develop physiology-based affect recognition models, which yielded reliable predictions. These models could potentially be used in future physiology-based adaptive driving skill training system such that the system could adapt based on individual affective states.