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Robotics 2018, 7(2), 25; https://doi.org/10.3390/robotics7020025

Deep Learning Systems for Estimating Visual Attention in Robot-Assisted Therapy of Children with Autism and Intellectual Disability

1
Sheffield Robotics, Sheffield Hallam University, Sheffield S1 1WB, UK
2
Psychology Operative Unit, IRCSS Oasi Maria SS, 94018 Troina, Italy
3
Department of Educational Sciences, University of Catania, 95124 Catania, Italy
*
Author to whom correspondence should be addressed.
Received: 1 May 2018 / Revised: 18 May 2018 / Accepted: 30 May 2018 / Published: 4 June 2018
(This article belongs to the Special Issue Intelligent Systems in Robotics)
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Abstract

Recent studies suggest that some children with autism prefer robots as tutors for improving their social interaction and communication abilities which are impaired due to their disorder. Indeed, research has focused on developing a very promising form of intervention named Robot-Assisted Therapy. This area of intervention poses many challenges, including the necessary flexibility and adaptability to real unconstrained therapeutic settings, which are different from the constrained lab settings where most of the technology is typically tested. Among the most common impairments of children with autism and intellectual disability is social attention, which includes difficulties in establishing the correct visual focus of attention. This article presents an investigation on the use of novel deep learning neural network architectures for automatically estimating if the child is focusing their visual attention on the robot during a therapy session, which is an indicator of their engagement. To study the application, the authors gathered data from a clinical experiment in an unconstrained setting, which provided low-resolution videos recorded by the robot camera during the child–robot interaction. Two deep learning approaches are implemented in several variants and compared with a standard algorithm for face detection to verify the feasibility of estimating the status of the child directly from the robot sensors without relying on bulky external settings, which can distress the child with autism. One of the proposed approaches demonstrated a very high accuracy and it can be used for off-line continuous assessment during the therapy or for autonomously adapting the intervention in future robots with better computational capabilities. View Full-Text
Keywords: deep learning neural networks; face detection and alignment; robot-assisted therapy; intellectual disability; autism spectrum disorder; human–robot interaction deep learning neural networks; face detection and alignment; robot-assisted therapy; intellectual disability; autism spectrum disorder; human–robot interaction
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Di Nuovo, A.; Conti, D.; Trubia, G.; Buono, S.; Di Nuovo, S. Deep Learning Systems for Estimating Visual Attention in Robot-Assisted Therapy of Children with Autism and Intellectual Disability. Robotics 2018, 7, 25.

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