Mental Health Resources: Reflection on AffecTech Platform

This positon paper presents an overview of the mental health resources developed within the AffecTech, an interdisciplinary European Commission-funded project supporting the wok of 15 PhD students in the space of digital interventions for affective health. It describes different types of targeted resources highlighting both traditional and innovative ones, our approach to developing them, and the challenges and opportunities that our work has uncovered

Keynote: HCI research and wellbeing

This talk provides outlines the disciplinary field of Human-Computer Interaction and provides an overview of my work on technologies for emotional wellbeing and mental health. The presentation places emphasis on the value the human body experiencing the world and describe research outcomes regarding digital disposal following life transitions marked by social loss, and design principles for affective health technologies. The talk concludes with an outline of the AffecTech: Personal technologies for affective health, an EC-funded Innovative Training Network and its interdisciplinary focus

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

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

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

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