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Sensing-enhanced therapy system for assessing children with autism spectrum disorders: A feasibility study

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posted on 09.10.2019 by Haibin Cai, Yinfeng Fang, Zhaojie Ju, Cristina Costescu, Daniel David, Erik Billing, Tom Ziemke, Serge Thill, Tony Belpaeme, Bram Vanderborght, David Vernon, Kathleen Richardson, Honghai Liu
It is evident that recently reported robot-assisted therapy systems for assessment of children with autism spectrum disorder (ASD) lack autonomous interaction abilities and require significant human resources. This paper proposes a sensing system that automatically extracts and fuses sensory features, such as body motion features, facial expressions, and gaze features, further assessing the children behaviors by mapping them to therapist-specified behavioral classes. Experimental results show that the developed system has a capability of interpreting characteristic data of children with ASD, thus has the potential to increase the autonomy of robots under the supervision of a therapist and enhance the quality of the digital description of children with ASD. The research outcomes pave the way to a feasible machine-assisted system for their behavior assessment.

Funding

EU Seventh Framework Program DREAM under Grant 611391

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Sensors Journal

Volume

19

Issue

4

Pages

1508 - 1518

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2018 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.

Acceptance date

11/10/2018

Publication date

2018-10-23

Copyright date

2018

ISSN

1530-437X

eISSN

1558-1748

Language

en

Depositor

Haibin Cai

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