Introducing SSBD+ Dataset with a Convolutional Pipeline for detecting
  Self-Stimulatory Behaviours in Children using raw videos
Vaibhavi Lokegaonkar Vijay Jaisankar Pon Deepika Madhav Rao T K Srikanth Sarbani Mallick Manjit Sodhi
Conventionally, evaluation for the diagnosis of Autism spectrum disorder is
done by a trained specialist through questionnaire-based formal assessments and
by observation of behavioral cues under various settings to capture the early
warning signs of autism. These evaluation techniques are highly subjective and
their accuracy relies on the experience of the specialist. In this regard,
machine learning-based methods for automated capturing of early signs of autism
from the recorded videos of the children is a promising alternative. In this
paper, the authors propose a novel pipelined deep learning architecture to
detect certain self-stimulatory behaviors that help in the diagnosis of autism
spectrum disorder (ASD). The authors also supplement their tool with an
augmented version of the Self Stimulatory Behavior Dataset (SSBD) and also
propose a new label in SSBD Action detection: no-class. The deep learning model
with the new dataset is made freely available for easy adoption to the
researchers and developers community. An overall accuracy of around 81% was
achieved from the proposed pipeline model that is targeted for real-time and
hands-free automated diagnosis. All of the source code, data, licenses of use,
and other relevant material is made freely available in
https://github.com/sarl-iiitb/