In this paper, we describe the design of a preliminary prototype and pilot results of the SMART-Games suite, a video game intervention for children with Autism Spectrum Disorders (ASDs). The prototype, consisting of a stuffed animal game controller with an on-screen video game, emphasizes empathy and related social skills. The proposed design aims to accommodate users across the autism spectrum through adaptive modules focused on core deficits: sensory and motor skills, imitation and turn-taking, joint attention and theory of mind. Response from the preliminary pilot suggests that the application appeals to the target audience and further development is underway.

This paper presents an overview of our work on the investigation of an affect-sensitive system to be applied in future autism intervention. A physiology-based affect-inference and adaptation framework was proposed, which could endow the assistive intervention technology with the capability of detecting the affective states of a child with autism spectrum disorder (ASD) and responding to them accordingly. Given the importance of affective cues in human-machine interaction and its significant role in autism intervention practice, this work marks an important step towards intelligent intervention systems that embody human-like functionality - affect recognition and adaptation. To account for the spectrum nature of autism and the differences of emotional expression, an individual-specific approach was employed for affective modeling. Two computer-based cognitive tasks were designed for eliciting target affective states considered important in autism intervention. Experimental results on real-time affect recognition and adaptation are presented based on a study of 6 children with ASD in a proof-of-concept experiment (i.e., robot-based basketball game). The preliminary results demonstrated that such an affect-sensitive adaptive system could hold promise for computer/robot-assisted autism intervention.

Investigation into robot-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, a robot must also be able to understand the affective needs of these children-an ability that the current robot-assisted ASD intervention systems lack-to achieve effective interaction that addresses the role of affective states in human-robot interaction and intervention practice. In this paper, we present a physiology-based affect-inference mechanism for robot-assisted intervention where the robot can detect the affective states of a child with ASD as discerned by a therapist and adapt its behaviors accordingly. This paper is the first step toward developing ldquounderstandingrdquo robots for use in future ASD intervention. Experimental results with six children with ASD from a proof-of-concept experiment (i.e., a robot-based basketball game) are presented. 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 that 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.

This paper presents a novel affect-sensitive human-robot interaction framework for rehabilitation of children with autism spectrum disorder (ASD). The overall aim is to enable the robot to detect and respond to the affective cues of the children in order to help them explore social interaction dynamics in a gradual and adaptive manner. The first part of the proposed framework, namely the 'affect recognition' module is developed in detail in this paper. Two tasks are designed to elicit the affective states of liking, anxiety, and engagement that are considered important in autism rehabilitation. Affective cues are inferred from psychophysiological analysis that uses subjective reports of the affective states from a therapist, a parent, and the child himself/herself. Comprehensive physiological indices are investigated that may correlate with the affective states of children with ASD. A support vector machines based affect recognizer is designed that yielded reliable prediction with approximately 83% success when using the therapist's reports. This is the first time, to our knowledge, such a human-robot interaction framework for autism rehabilitation is proposed. This is also the first time that the affective states of children with ASD have been experimentally detected via physiology-based affect recognition technique.

This paper presents a novel affect-sensitive human-robot interaction framework for rehabilitation of children with autism spectrum disorder (ASD) where the robot can detect the affective cues of the children implicitly and response to them appropriately. Psychophysiological analysis is performed that uses subjective reports of the affective states from a clinical observer. Comprehensive physiological indices are investigated that may correlate with the affective states of children with ASD. A robot uses a support vector machines based affect model to detect the affective cues. A reinforcement learning based adaptation mechanism is employed to allow the robot to adjust its behaviors autonomously as a function of the predicted children's affective state. Four adolescents diagnosed with high-functioning autism participated in the experiments. This is the first time, to our knowledge, that the affective states of children with ASD have been detected via physiology-based affective modeling technique in real-time. This is also the first time that impact of affect-sensitive interaction between a robot and children with ASD in closed loop has been demonstrated experimentally.