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

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 beha...

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Main Authors:	Jaiswal, Shashank, Valstar, Michel F., Gillott, Alinda, Daley, David
Format:	Conference or Workshop Item
Language:	English
Published:	2017
Online Access:	http://eprints.nottingham.ac.uk/40827/ http://eprints.nottingham.ac.uk/40827/1/paper.pdf

id	nottingham-40827
recordtype	eprints
spelling	nottingham-408272018-07-02T09:11:08Z http://eprints.nottingham.ac.uk/40827/ Automatic detection of ADHD and ASD from expressive behaviour in RGBD data Jaiswal, Shashank Valstar, Michel F. Gillott, Alinda Daley, David 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. 2017-05-30 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.nottingham.ac.uk/40827/1/paper.pdf 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)
repository_type	Digital Repository
institution_category	Local University
institution	University of Nottingham Malaysia Campus
building	Nottingham Research Data Repository
collection	Online Access
language	English
description	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.
format	Conference or Workshop Item
author	Jaiswal, Shashank Valstar, Michel F. Gillott, Alinda Daley, David
spellingShingle	Jaiswal, Shashank Valstar, Michel F. Gillott, Alinda Daley, David Automatic detection of ADHD and ASD from expressive behaviour in RGBD data
author_facet	Jaiswal, Shashank Valstar, Michel F. Gillott, Alinda Daley, David
author_sort	Jaiswal, Shashank
title	Automatic detection of ADHD and ASD from expressive behaviour in RGBD data
title_short	Automatic detection of ADHD and ASD from expressive behaviour in RGBD data
title_full	Automatic detection of ADHD and ASD from expressive behaviour in RGBD data
title_fullStr	Automatic detection of ADHD and ASD from expressive behaviour in RGBD data
title_full_unstemmed	Automatic detection of ADHD and ASD from expressive behaviour in RGBD data
title_sort	automatic detection of adhd and asd from expressive behaviour in rgbd data
publishDate	2017
url	http://eprints.nottingham.ac.uk/40827/ http://eprints.nottingham.ac.uk/40827/1/paper.pdf
first_indexed	2018-09-06T13:08:59Z
last_indexed	2018-09-06T13:08:59Z
_version_	1610863702061350912

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

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