Robot-Assisted Diagnosis for Children with Autism Spectrum Disorder Based on Automated Analysis of Nonverbal Cues

Autism spectrum disorder (ASD) is a neurode-velopmental disorder that affects people from birth, whose symptoms are found in the early developmental period. The ASD diagnosis is usually performed through several sessions of behavioral observation, exhaustive screening, and manual coding behavior. The early detection of ASD signs in naturalistic behavioral observation may be improved through Child-Robot Interaction (CRI) and technological-based tools for automated behavior assessment. Robot-Assisted Tools using CRI theories have been of interest in intervention for children with Autism Spectrum Disorder (CwASD), elucidating faster and more significant gains from the diagnosis and therapeutic intervention when compared to classical methods. Besides that, using computer vision to analyze child's behaviors and automated video coding to summarize the responses would help the clinicians to reduce the delay of ASD diagnosis. In this article, a CRI to enhance the traditional tools for ASD diagnosis is proposed, which is inspired on a well-established protocol for Joint Attention assessment, using a computer vision system composed of an unstructured and scalable network of RGBD sensors to allow the child's movement. Also, a proof of concept is presented, with the participation of three typically developing (TD) children.