The Use of Social Robots in the Diagnosis of Autism in Preschool Children

Abstract

The present study contributes to the research problem of applying social robots in autism diagnosis. There is a common belief that existing diagnostic methods for autistic spectrum disorder are not effective. Advances in Human–Robot Interactions (HRI) provide potential new diagnostic methods based on interactive robots. We investigated deficits in turn-taking in preschool children by observing their interactions with the NAO robot during two games: (Dance with me vs. Touch me). We compared children’s interaction profiles with the robot (five autistic vs. five typically developing young children). Then, to investigate turn-taking deficits, we adopted a rating procedure to indicate differences between both groups of children based on an observational scale. A statistical analysis based on ratings of the children’s interactions with the NAO robot indicated that autistic children presented a deficient level of turn-taking behaviors. Our study provides evidence for the potential of designing and implementing an interactive dyadic game between a child and a social robot that can be used to detect turn-taking deficits based on objective measures. We also discuss our results in the context of existing studies and propose guidelines for a robotic-enabled autism diagnosis system.