Robot-Assisted Autism Spectrum Disorder Diagnostic Based on Artificial Reasoning

Autism spectrum disorder (ASD) is a neurodevelopmental 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. Additionally, using computer vision to analyze child's behaviors and automated video coding to summarize the responses would help clinicians to reduce the delay of ASD diagnosis. In this article, a CRI to enhance the traditional tools for ASD diagnosis is proposed. The system relies on computer vision and an unstructured and scalable network of RGBD sensors built upon Robot Operating System (ROS) and machine learning algorithms for automated face analysis. Also, a proof of concept is presented, with participation of three typically developing (TD) children and three children in risk of suffering from ASD.