Automatic detection of ADHD and ASD from expressive behaviour in RGBD data

Jaiswal, Shashank and Valstar, Michel F. and Gillott, Alinda and Daley, David (2017) Automatic detection of ADHD and ASD from expressive behaviour in RGBD data. In: 12th IEEE International Conference on Face and Gesture Recognition (FG 2017), 30 May - 3 June 2017, Washington, DC, USA. (In Press)

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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 on detecting certain behavioural markers, our approach provides a fully automatic end-to-end system to directly predict 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) groups and 94% for Comorbid (ADHD+ASD) vs ASD only group. We show that our system is a potentially useful time saving contribution to the clinical diagnosis of ADHD and ASD.

Item Type: Conference or Workshop Item (Paper)
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Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Medicine > Division of Psychiatry and Applied Psychology
University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Valstar, Michel
Date Deposited: 27 Feb 2017 14:43
Last Modified: 31 May 2017 07:22
URI: http://eprints.nottingham.ac.uk/id/eprint/40827

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