Engagement Estimation During Child Robot Interaction Using Deep Convolutional Networks Focusing on ASD Children
Authors: Papaeliou, Christina 
Maragos, P. 
Efthymiou, Niki 
Anagnostopoulou, D. 
Issue Date: 1-Jan-2021
Conference: 2021 IEEE International Conference on Robotics and Automation, 30 May-05 June 20201, Xi’an, China 
Book: Proceedings of the 2021 IEEE International Conference on Robotics and Automation 
Volume: 2021-May
Abstract: 
Estimating the engagement of children is an essential prerequisite for constructing natural Child-Robot Interaction. Especially in the case of children with Autism Spectrum Disorder, monitoring the engagement of the other party allows robots to adjust their actions according to the educational and therapeutic goals in hand. In this work we delve into engagement estimation with a focus on children with autism spectrum disorder. We propose deep convolutional architectures for engagement estimation that outperform previous methods, and explore their performance under variable conditions, in four databases depicting ASD and TD children interacting with robots or humans.
ISBN: 9781728190778
ISSN: 10504729
DOI: 10.1109/ICRA48506.2021.9561687
URI: https://uniwacris.uniwa.gr/handle/3000/1592
Type: Conference Paper
Department: Department of Early Childhood Education and Care 
School: School of Administrative, Economics and Social Sciences 
Affiliation: University of West Attica (UNIWA) 
Appears in Collections:Book Chapter / Κεφάλαιο Βιβλίου

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