Exploring Emotions: Study of Five Design Workshops for Generating Ideas for Emotional Self-report Interfaces

Accurately reporting our emotions is essential for various purposes, such as men-tal health monitoring and annotating artificial intelligence datasets. However, emotions are complex and challenging to convey, and commonly used concepts such as valence and arousal can be difficult for users to understand correctly. Our main goal was to explore new ways to inform the design of affective self-report instruments that can bridge the gap between people's understanding of emotions and machine-interpretable data. In this paper, we present the findings of five de-sign workshops to generate ideas and solutions to represent emotion-related con-cepts and improve the design of affective self-report interfaces. The workshops originated seven themes that informed the derivation of design implications. These implications include representing arousal using concepts such as shape, movement, and body-related elements, representing valence using facial emojis and color properties, prioritizing arousal in questioning, and facilitating user con-firmation while preserving introspection.