Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data

TL;DR

This paper introduces an automatic RGBD-based system that analyzes expressive behaviour to diagnose ADHD and ASD with high accuracy, offering a faster, objective alternative to traditional manual assessments.

Contribution

It presents a fully automatic end-to-end system combining facial expression analysis and 3D behaviour analysis for diagnosing ADHD and ASD, improving objectivity and efficiency.

Findings

Achieved 96% accuracy in controls vs ADHD/ASD group

Achieved 94% accuracy in ASD vs comorbid group

Demonstrated potential for time-saving diagnostic aid

Abstract

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.