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The DREAM Dataset: Supporting a data-driven study of autism spectrum disorder and robot enhanced therapy

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journal contribution
posted on 18.03.2021, 09:59 by Erik Billing, Tony Belpaeme, Haibin CaiHaibin Cai, Hoang-Long Cao, Anamaria Ciocan, Cristina Costescu, Daniel David, Robert Homewood, Daniel Hernandez Garcia, Pablo Gómez Esteban, Honghai Liu, Vipul Nair, Silviu Matu, Alexandre Mazel, Mihaela Selescu, Emmanuel Senft, Serge Thill, Bram Vanderborght, David Vernon, Tom Ziemke
We present a dataset of behavioral data recorded from 61 children diagnosed with Autism Spectrum Disorder (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and more than 300 hours of therapy. Half of the children interacted with the social robot NAO supervised by a therapist. The other half, constituting a control group, interacted directly with a therapist. Both groups followed the Applied Behavior Analysis (ABA) protocol. Each session was recorded with three RGB cameras and two RGBD (Kinect) cameras, providing detailed information of children’s behavior during therapy. This public release of the dataset comprises body motion, head position and orientation, and eye gaze variables, all specified as 3D data in a joint frame of reference. In addition, metadata including participant age, gender, and autism diagnosis (ADOS) variables are included. We release this data with the hope of supporting further data-driven studies towards improved therapy methods as well as a better understanding of ASD in general.

Funding

Seventh Frame Programme grant #611391: Development of Robot-Enhanced therapy for children with AutisM spectrum disorders (DREAM)

History

School

  • Science

Department

  • Computer Science

Published in

PLOS ONE

Volume

15

Issue

8

Publisher

Public Library of Science (PLoS)

Version

VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by PloS under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

14/07/2020

Publication date

2020-08-21

Copyright date

2020

eISSN

1932-6203

Language

en

Depositor

Dr Haibin Cai. Deposit date: 17 March 2021

Article number

e0236939

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