Scales, Reflections, and Conversations: A Multi-Modal Approach to Emotion Annotation

2026-09-04

AI Summary

研究通过多模态情绪标注方法，解决传统数据收集方式忽视参与者可用性和情感复杂性的问题，以支持更丰富细腻的情绪数据收集。

Abstract

Mental health concerns are increasing worldwide, highlighting the need for interventions that support everyday emotional well being. Prior work has demonstrated the potential of wearable and mobile technologies to deliver data driven interventions. However, developing effective data-driven systems requires access to emotion data that captures individuals' emotional variability and change in everyday contexts. Existing approaches to data collection largely rely on frequent, prescheduled prompts and predefined scales or questionnaires. These methods often fail to account for participants' availability, agency, or the complexity of their emotional experiences, resulting in shallow, context poor data. In this paper, we present a feasibility study of a participant centric, multimodal emotion-annotation application designed around users' emotional intensity and availability. Our findings show how multimodal emotion logging can shape participants' experiences and data logging behaviors, and demonstrate its potential to support the collection of richer, more nuanced emotion data.

Problem

• emotional variability
• data collection
• context poor data
• multimodal emotion logging

Innovation

• multimodal emotion logging
• participant-centric
• emotional intensity