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  • 1.
    Cai, Haibin
    et al.
    School of Computing, University of Portsmouth, U.K..
    Fang, Yinfeng
    School of Computing, University of Portsmouth, U.K..
    Ju, Zhaojie
    School of Computing, University of Portsmouth, U.K..
    Costescu, Cristina
    Department of Clinical Psychology and Psychotherapy, Babe-Bolyai University, Cluj-Napoca, Romania.
    David, Daniel
    Department of Clinical Psychology and Psychotherapy, Babe-Bolyai University, Cluj-Napoca, Romania.
    Billing, Erik
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Ziemke, Tom
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Department of Computer and Information Science, Linkoping University, Sweden.
    Thill, Serge
    University of Plymouth, U.K..
    Belpaeme, Tony
    University of Plymouth, U.K..
    Vanderborght, Bram
    Vrije Universiteit Brussel and Flanders Make, Belgium.
    Vernon, David
    Carnegie Mellon University Africa, Rwanda.
    Richardson, Kathleen
    De Montfort University, U.K..
    Liu, Honghai
    School of Computing, University of Portsmouth, U.K..
    Sensing-enhanced Therapy System for Assessing Children with Autism Spectrum Disorders: A Feasibility Study2019In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 19, no 4, p. 1508-1518Article in journal (Refereed)
    Abstract [en]

    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. IEEE

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