Towards Affect-sensitive Assistive Intervention Technologies for Children with Autism

摘要

There is increasing consensus in the autism community that development of assistive tools that exploit advanced technology will likely make application of intensive intervention for children with ASD more readily accessible. In recent years, various applications of advanced interactive technologies have been investigated in order to facilitate and/or partially automate the existing behavioral intervention that addresses specific deficits associated with autism. However, the current technology-assisted intervention tools for children with ASD do not possess the ability of deciphering affective cues from the children, which could be critical given that the affective factors of children with ASD have significant impacts on the intervention practice. In this work, we have proposed a novel framework for affect-sensitive human-machine interaction where the intelligent system can detect the affective states of the children with ASD implicitly and respond to it accordingly. The presented affective modeling methodology could allow the recognition of affective states of children with ASD from physiological signals in real time and provide the basis for future technology-assisted affect-sensitive interactive autism intervention. In Phase I, two cognitive tasks ? solving anagrams and playing Pong ? have been designed to elicit the affective states of liking, anxiety, and engagement for children with ASD that are considered important in autism intervention. To have reliable reference points to link the physiological data to the affective states, the reports from the child, the therapist, and the parent were collected and analyzed. A large set of physiological indices have been investigated to determine their correlation with the affective states of the children with ASD. We have experimentally demonstrated that it is viable to detect the affective states of children with ASD via a physiology-based affect recognition mechanism. A SVM-based affective model yielded reliable prediction with a success rate of 82.9% when using the therapist's reports. In order to investigate the affect-sensitive closed-loop interaction between the children with ASD and an intelligent system, we designed a proof-of-concept task, robot-based basketball, and developed an experimental system for its real-time implementation and verification. The real-time prediction of liking level of the children with ASD was accomplished with an average accuracy of 81.1%. The robot learned individual preferences of the children with ASD over time based on the interaction experience and the predicted liking level and hence automatically selected the most-preferred behavior, on average, 72.5% of the time. We have observed that such affect-sensitive robot behavior adaptation has led to an increase in reported liking level of the children with ASD. 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-