  This paper has been withdrawn by the authors due to insufficient or
definition error(s) in the ethics approval protocol.
  Autism spectrum disorders (ASD) impact the cognitive, social, communicative
and behavioral abilities of an individual. The development of new clinical
decision support systems is of importance in reducing the delay between
presentation of symptoms and an accurate diagnosis. In this work, we contribute
a new database consisting of video clips of typical (normal) and atypical (such
as hand flapping, spinning or rocking) behaviors, displayed in natural
settings, which have been collected from the YouTube video website. We propose
a preliminary non-intrusive approach based on skeleton keypoint identification
using pretrained deep neural networks on human body video clips to extract
features and perform body movement analysis that differentiates typical and
atypical behaviors of children. Experimental results on the newly contributed
database show that our platform performs best with decision tree as the
classifier when compared to other popular methodologies and offers a baseline
against which alternate approaches may developed and tested.
