Design, Development, and Evaluation of a Noninvasive Autonomous Robot-Mediated Joint Attention Intervention System for Young Children With ASD

Research indicates that human–robot interaction can help children with autism spectrum disorder (ASD). While most early robot-mediated interaction studies were based on free interactions, recent studies have shown that robot-mediated interventions that focus on the core impairments of ASD such as joint attention deficit tend to produce better outcomes. Joint attention impairment is one of the core deficits in ASD that has an important impact in the neuropsychological development of these children. In this paper, we propose a novel joint attention intervention system for children with ASD that overcomes several existing limitations in this domain such as the need to use body-worn sensors, nonautonomous robot operation requiring human involvement and lack of a formal model for robot-mediated joint attention interaction. We present a fully autonomous robotic system, called noncontact-responsive robot-mediated intervention system, that can infer attention through a distributed noncontact gaze inference mechanism with an embedded least-to-most (LTM) robot-mediated interaction model to address the current limitations. The system was tested in a multisession user study with 14 young children with ASD. The results showed that participants’ joint attention skills improved significantly, their interest in the robot remained consistent throughout the sessions, and the LTM interaction model was effective in promoting the children's performance.