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

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

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Abstract

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 behaviours by mapping them to therapist specified behavioural 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 behaviour assessment.
Original languageEnglish
Article number18394336
Pages (from-to)1508-1518
Number of pages11
JournalIEEE Sensors Journal
Volume19
Issue number4
Early online date23 Oct 2018
DOIs
Publication statusPublished - 15 Feb 2019

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