Towards Affect-sensitive Assistive Intervention Technologies for Children with Autism

Abstract

Investigation into technology-assisted intervention for children with autism spectrum disorder (ASD) has gained momentum in recent years. Therapists involved in interventions must overcome the communication impairments generally exhibited by children with ASD by adeptly inferring the affective cues of the children to adjust the intervention accordingly. Similarly, an intelligent system, such as a computer or robot, must also be able to understand the affective needs of these children an ability that the current technologyassisted ASD intervention systems lack to achieve effective interaction that addresses the role of affective states in human-computer interaction (HCI), human-robot interaction (HRI), and intervention practice. In this chapter we present a physiology-based affect-inference mechanism for emotion modeling, emotion recognition, and emotion-sensitive adaptive response in technology-assisted intervention. This work is the first step towards developing “understanding” interactive technologies for use in future ASD intervention. We address the problem of how to make computer-based ASD intervention tools affect-sensitive by designing therapist-like affective models of the children with ASD based on their physiological responses. By employing these models, we explain how a robot can detect the affective states of a child with ASD and adapt its behaviors accordingly. Experimental results with 6 children with ASD from computer-based cognitive tasks and a proof-ofconcept experiment (i.e., a robot-based basketball game) are presented. A Support Vector Machines (SVM) based affective model yielded approximately 82.9% success for predicting affect inferred from a therapist. The robot learned the individual liking level of each child with regard to the game configuration and selected appropriate behaviors to present the task at his/her preferred liking level. Results show the robot automatically predicted individual liking level in real time with 81.1% accuracy. This is the first time, to our knowledge, that the affective states of children with ASD have been detected via a physiology-based affect recognition technique in real time. This is also the first time that the impact of affect-sensitive closed-loop interaction between a robot and a child with ASD has been demonstrated experimentally. While there is at present no single accepted intervention, treatment, or known cure for ASD, there is growing consensus that intensive behavioral and educational intervention programs can significantly improve long term outcomes for individuals and their families (Rogers,