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Evaluation of a self-report system for assessing mood using facial expressions

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Publication date23/04/2019
Host publicationPervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings
EditorsSilvia Serino, Daniela Villani, Pietro Cipresso
PublisherSpringer
Pages231-241
Number of pages11
ISBN (Print)9783030258719
Original languageEnglish

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume288
ISSN (Print)1867-8211

Abstract

Effective and frequent sampling of mood through self-reports
could enable a better understanding of the interplay between mood and
events influencing it. To accomplish this, we built a mobile application featuring a sadness-happiness visual analogue scale and a facial
expression-based scale. The goal is to evaluate, whether a facial expression based scale could adequately capture mood. The method and mobile application were evaluated with 11 participants. They rated the mood of characters presented in a series of vignettes, using both scales. Participants also completed a user experience survey rating the two assessment methods and the mobile interface. Findings reveal a Pearson’s correlation coefficient of 0.97 between the two assessment scales and a stronger preference for the face scale. We conclude with a discussion of the implications of our findings for mood self-assessment and an outline future research.

Bibliographic note

The final publication is available at Springer via https://link.springer.com/chapter/10.1007%2F978-3-030-25872-6_19