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This paper presents a functional imitation task aimed at facilitating Autism Spectrum Disorder (ASD) diagnostics in children. Imitation plays a key role in the development of social skills at a young age, and studies have shown that the ability to imitate is impaired in children with ASD. Therefore, we expect imitation-based tasks to have diagnostic value. In this paper, we introduce two novel elements of human-robot interaction in the context of autism diagnostics. Instead of pure motoric imitation, we propose imitation tasks involving real objects in the environment. The introduction of physical objects strongly emphasizes joint attention skills, another area that is typically impaired in children with ASD. Furthermore, we present simple object detection, manipulation, tracking and gesture recognition algorithms, suitable for real-time, onboard execution on the small-scale humanoid robot NAO. The proposed system paves the way for fully autonomous execution of diagnostic tasks, which would simplify the deployment of robotic assistants in clinical settings. The source code for all described functionalities has been made publicly available as open-source software. We present a preliminary evaluation of the proposed system with a control group of typically developing preschool children and a group of seven children diagnosed with ASD. |
