Skip to main content

Research Repository

See what's under the surface

Advanced Search

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

Jaiswal, Shashank; Valstar, Michel F.; Gillott, Alinda; Daley, David

Authors

Shashank Jaiswal

Michel F. Valstar

Alinda Gillott

David Daley



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.

Start Date May 30, 2017
Publication Date May 30, 2017
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Pages 762-769
APA6 Citation Jaiswal, S., Valstar, M. F., Gillott, A., & Daley, D. (2017). Automatic detection of ADHD and ASD from expressive behaviour in RGBD data. doi:10.1109/FG.2017.95
DOI https://doi.org/10.1109/FG.2017.95
Publisher URL https://ieeexplore.ieee.org/document/7961818/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Files

paper.pdf (685 Kb)
PDF

Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





You might also like



Downloadable Citations

;