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

Hristo Valev; Tim Leufkens; Corina Sas; Joyce Westerink; Ron Dotsch

doi:10.1007/978-3-030-25872-6_19

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

Hristo Valev
, Tim Leufkens
, Corina Sas
, Joyce Westerink
, Ron Dotsch

Human Technology Interaction

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

1 Downloads (Pure)

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.

Original language	English
Title of host publication	Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings
Editors	Pietro Cipresso, Silvia Serino, Daniela Villani
Place of Publication	Cham
Publisher	Springer
Pages	231-241
Number of pages	11
ISBN (Electronic)	978-3-030-25872-6
ISBN (Print)	978-3-030-25871-9
DOIs	https://doi.org/10.1007/978-3-030-25872-6_19
Publication status	Published - 11 Jul 2019
Event	9th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2019 - Buenos Aires, Argentina Duration: 23 Apr 2019 → 24 Apr 2019

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	288
ISSN (Print)	1867-8211

Conference

Conference	9th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2019
Country/Territory	Argentina
City	Buenos Aires
Period	23/04/19 → 24/04/19

Funding

Acknowledgment. This work has been supported by AffecTech: Personal Technologies for Affective Health, Innovative Training Network funded by the H2020 People Programme under Marie Skłodowska-Curie grant agreement No. 722022.

Keywords

Mood assessment
Self-report system
User interface

Access to Document

10.1007/978-3-030-25872-6_19

Cite this

Valev, H., Leufkens, T., Sas, C., Westerink, J., & Dotsch, R. (2019). Evaluation of a self-report system for assessing mood using facial expressions. In P. Cipresso, S. Serino, & D. Villani (Eds.), Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings (pp. 231-241). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 288). Springer. https://doi.org/10.1007/978-3-030-25872-6_19

Valev, Hristo ; Leufkens, Tim ; Sas, Corina et al. / Evaluation of a self-report system for assessing mood using facial expressions. Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings. editor / Pietro Cipresso ; Silvia Serino ; Daniela Villani. Cham : Springer, 2019. pp. 231-241 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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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{\textquoteright}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.",

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Valev, H, Leufkens, T, Sas, C, Westerink, J & Dotsch, R 2019, Evaluation of a self-report system for assessing mood using facial expressions. in P Cipresso, S Serino & D Villani (eds), Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 288, Springer, Cham, pp. 231-241, 9th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2019, Buenos Aires, Argentina, 23/04/19. https://doi.org/10.1007/978-3-030-25872-6_19

Evaluation of a self-report system for assessing mood using facial expressions. / Valev, Hristo; Leufkens, Tim; Sas, Corina et al.
Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings. ed. / Pietro Cipresso; Silvia Serino; Daniela Villani. Cham: Springer, 2019. p. 231-241 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 288).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Dotsch, Ron

PY - 2019/7/11

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N2 - 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.

AB - 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.

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Valev H, Leufkens T, Sas C, Westerink J, Dotsch R. Evaluation of a self-report system for assessing mood using facial expressions. In Cipresso P, Serino S, Villani D, editors, Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings. Cham: Springer. 2019. p. 231-241. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-25872-6_19

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

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