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Conventionally, evaluation for the diagnosis of Autism spectrum disorder isdone by a trained specialist through questionnaire-based formal assessments andby observation of behavioral cues under various settings to capture the earlywarning signs of autism. These evaluation techniques are highly subjective andtheir accuracy relies on the experience of the specialist. In this regard,machine learning-based methods for automated capturing of early signs of autismfrom the recorded videos of the children is a promising alternative. In thispaper, the authors propose a novel pipelined deep learning architecture todetect certain self-stimulatory behaviors that help in the diagnosis of autismspectrum disorder (ASD). The authors also supplement their tool with anaugmented version of the Self Stimulatory Behavior Dataset (SSBD) and alsopropose a new label in SSBD Action detection: no-class. The deep learning modelwith the new dataset is made freely available for easy adoption to theresearchers and developers community. An overall accuracy of around 81% wasachieved from the proposed pipeline model that is targeted for real-time andhands-free automated diagnosis. All of the source code, data, licenses of use,and other relevant material is made freely available inhttps://github.com/sarl-iiitb/

Introducing SSBD+ Dataset with a Convolutional Pipeline for detecting Self-Stimulatory Behaviours in Children using raw videos