Study of Mechanisms of Social Interaction Stimulation in Autism Spectrum Disorder by Assisted Humanoid Robot

Information and Communication Technologies (ICT) have been proved to have a great impact in enhancing social, communicative, and language development in children with Autism Spectrum Disorders (ASD) as demonstrated by plenty of effective technological tools reported in the literature for diagnosis, assessment and treatment of such neurological diseases. On the contrary, there are very few works exploiting ICT to study the mechanisms that trigger the behavioral patterns during the specialized sessions of treatment focused on social interaction stimulation. From the study of the literature it emerges that the behavioral outcomes are qualitatively evaluated by the therapists making this way impossible to assess, in a consistent manner, the worth of the supplied ASD treatments that should be based on quantitative metric not available for this purpose yet. Moreover, the rare attempts to use a methodological approach are limited to the study of one (of at least a couple) of the several behavioral cues involved. In order to fill this gap, in this paper a technological framework able to analyze and integrate multiple visual cues in order to capture the behavioral trend along a ASD treatment is introduced. It is based on an algorithmic pipeline involving face detection, landmark extraction, gaze estimation, head pose estimation and facial expression recognition and it has been used to detect behavioral features during the interaction among different children, affected by ASD, and a humanoid robot. Experimental results demonstrated the superiority of the proposed framework in the specific application context with respect to leading approaches in the literature, providing a reliable pathway to automatically build a quantitative report that could help therapists to better achieve either ASD diagnosis or assessment tasks.