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Robot Assisted Diagnosis for Autism in Children

The complexity of the diagnostic procedure coupled with the absence of medical markers poses a variety of challenges in the early detection of autism in children. The diagnosis relies on the observation of behavioural symptoms and parent reporting of the developmental history of the child. It requires years of experience and training for identifying these symptoms which could be often misjudged as other mental conditions with similar symptomology. In this work, we propose a robot-assisted diagnostic protocol for the early detection of autism leveraging the onboard sensors of Socially Assistive Robots (SAR) and the vast potential of machine learning techniques. The robot assistant's role will be two-fold: (a) supporting the expert in administering the diagnostic procedure and (b) evaluating the behavioural symptoms and reaching a reliable diagnosis. Even though similar studies have been done in the Global North, the challenges posed by the social and cultural diversity of the Indian population is varied and needs to be explored further.