  Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder
(ASD) are neurodevelopmental conditions which impact on a significant number of
children and adults. Currently, the diagnosis of such disorders is done by
experts who employ standard questionnaires and look for certain behavioural
markers through manual observation. Such methods for their diagnosis are not
only subjective, difficult to repeat, and costly but also extremely time
consuming. In this work, we present a novel methodology to aid diagnostic
predictions about the presence/absence of ADHD and ASD by automatic visual
analysis of a person's behaviour. To do so, we conduct the questionnaires in a
computer-mediated way while recording participants with modern RGBD
(Colour+Depth) sensors. In contrast to previous automatic approaches which have
focussed only detecting certain behavioural markers, our approach provides a
fully automatic end-to-end system for directly predicting ADHD and ASD in
adults. Using state of the art facial expression analysis based on Dynamic Deep
Learning and 3D analysis of behaviour, we attain classification rates of 96%
for Controls vs Condition (ADHD/ASD) group and 94% for Comorbid (ADHD+ASD) vs
ASD only group. We show that our system is a potentially useful time saving
contribution to the diagnostic field of ADHD and ASD.
