Automating the Administration and Analysis of Psychiatric Tests: the Case of Attachment in School Age Children

Roffo, G. , Vo, D.-B. , Tayarani, M., Rooksby, M. , Sorrentino, A., Di Folco, S., Minnis, H. , Brewster, S. and Vinciarelli, A. (2019) Automating the Administration and Analysis of Psychiatric Tests: the Case of Attachment in School Age Children. In: 2019 CHI Conference on Human Factors in Computing Systems (CHI '19), Glasgow, UK, 04-09 May 2019, p. 595. ISBN 9781450359702 (doi:10.1145/3290605.3300825)

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Abstract

This article presents the School Attachment Monitor, a novel interactive system that can reliably administer the Manch- ester Child Attachment Story Task (a standard psychiatric test for the assessment of attachment in children) without the supervision of trained professionals. Attachment prob- lems in children cause significant mental health issues and costs to society which technology has the potential to re- duce. SAM collects, through instrumented doll-play games, enough information to allow a human assessor to manually identify the attachment status of children. Experiments show that the system successfully does this in 87.5% of cases. In addition, the experiments show that an automatic approach based on deep neural networks can map the information collected into the attachment condition of the children. The outcome SAM matches the judgment of expert human asses- sors in 82.8% of cases. This is the first time an automated tool has been successful in measuring attachment. This work has significant implications for psychiatry as it allows profes- sionals to assess many more children cost effectively and to direct healthcare resources more accurately and efficiently to improve mental health.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Minnis, Professor Helen and sorrentino, alessandra and Rooksby, Dr Maki and Tayarani, Dr Mohammad and Vo, Dr Dong-Bach and Roffo, Dr Giorgio and Brewster, Professor Stephen and Vinciarelli, Professor Alessandro
Authors: Roffo, G., Vo, D.-B., Tayarani, M., Rooksby, M., Sorrentino, A., Di Folco, S., Minnis, H., Brewster, S., and Vinciarelli, A.
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > Mental Health and Wellbeing
College of Science and Engineering > School of Computing Science
Research Group:GIST
Publisher:Association for Computing Machinery.
ISBN:9781450359702
Copyright Holders:Copyright © 2019 Association for Computing Machinery
First Published:First published in 2019 CHI Conference on Human Factors in Computing Systems (CHI '19): 595
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
683651"SAM: Automated Attachment Analysis Using the School Attachment Monitor "Stephen BrewsterEngineering and Physical Sciences Research Council (EPSRC)EP/M025055/1COM - COMPUTING SCIENCE
714531A Robot Training Buddy for adults with ASDAlessandro VinciarelliEngineering and Physical Sciences Research Council (EPSRC)EP/N035305/1COM - COMPUTING SCIENCE