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

arXiv: https://arxiv.org/abs/1612.02374 · HTML: https://mlsanity.com/paper/1612.02374/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2016.

## Built on by (most cited first, 6 of 6)

- [AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit](https://mlsanity.com/paper/2202.12059/) (2022)
- [Facial Expression Analysis Using Decomposed Multiscale Spatiotemporal Networks](https://mlsanity.com/paper/2203.11111/) (2022)
- [Cross Your Body: A Cognitive Assessment System for Children](https://mlsanity.com/paper/2111.12824/) (2021)
- [Automatic Detection of Sentimentality from Facial Expressions](https://mlsanity.com/paper/2209.04908/) (2022)
- [Path Signature Representation of Patient-Clinician Interactions as a Predictor for Neuropsychological Tests Outcomes in Children: A Proof of Concept](https://mlsanity.com/paper/2312.11512/) (2023)
- [PainNet: Statistical Relation Network with Episode-Based Training for Pain Estimation](https://mlsanity.com/paper/2504.06257/) (2025)

## Abstract (arXiv)

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.