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Robotic social therapy on children with autism: preliminary evaluation through multi-parametric analysis

Mazzei, Daniele, Greco, Alberto, Lazzeri, Nicole, Zaraki, Abolfazl, Lanata, Antonio, Igliozzi, Roberta, Mancini, Alice, Stoppa, Francesca, Scilingo, Enzo Pasquale, Muratori, Filippo and De Rossi, Danilo 2013. Robotic social therapy on children with autism: preliminary evaluation through multi-parametric analysis. Presented at: 2012 International Conference on Social Computing (SocialCom), Amsterdam, The Netherlands, 3-5 September 2012. 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing. IEEE, pp. 766-771. 10.1109/SocialCom-PASSAT.2012.101

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Abstract

Autism Spectrum Disorder (ASD) is a neural development disorder characterized by specific patterns of behavioral and social difficulties. Beyond these core symptoms, additional problems such as absence of gender differences identification, interactional distortions of environmental and family responses are often present. Taking into account these emotional and behavioral problems researchers and clinicians are focusing on the design of innovative therapeutic approaches aimed to improve social capabilities of subjects with ASD. Thanks to the technological and scientific progresses of the last years, nowadays it is possible to create human-like robots with social and emotional capabilities. Furthermore it is also possible to analyze physiological signals inferring subjects' psycho-physiological state which can be compared with a behavioral analysis in order to obtain a deeper understanding of subjects reactions to treatments. In this work a preliminary evaluation of an innovative social robot-based treatment for subjects with ASD is described. The treatment consists in a complex stimulation and acquisition platform composed of a social robot, a multi-parametric acquisition system and a therapeutic protocol. During the preliminary tests of the treatment the subject's physiological signals and behavioral parameters have been recorded and used together with the therapists' annotations to infer the subjects' induced reactions. Physiological signals were analyzed and statistically evaluated demonstrating the possibility to correctly discern the two groups (ASD and normally developing subjects) with a classification percentage higher than 92%. Statistical analysis also highlighted the treatment capability to induce different affective states in subjects with ASDs more than in control subjects, demonstrating that the treatment is well designed and tuned on ASDs deficits and behavioral lacks.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Engineering
Publisher: IEEE
ISBN: 9781467356381
Last Modified: 18 Mar 2020 12:00
URI: http://orca.cf.ac.uk/id/eprint/129003

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