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

Giorgio Roffo, Dong-Bach Vo, Mohammad Tayarani, Maki Rooksbi, Alessandro Vinciarelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This article presents the School Attachment Monitor, a novel interactive system that can reliably administer the Manchester Child Attachment Story Task (a standard psychiatric test for the assessment of attachment in children) without the supervision of trained professionals. Attachment problems in children cause significant mental health issues and costs to society which technology has the potential to reduce. 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 assessors 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 professionals to assess many more children cost effectively and to direct healthcare resources more accurately and efficiently to improve mental health.

Original languageEnglish
Title of host publicationCHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Publication statusPublished - 3 May 2019


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