发表机构
IIIT-Delhi; RIT（印度德里信息技术研究所; 罗切斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有情绪数据收集方法存在的缺陷，本文开展以参与者为中心的多模态情绪标注应用可行性研究，证明该方法可收集更丰富细致的情绪数据。
AI 中文摘要
全球范围内心理健康问题日益增多，凸显出对支持日常情绪健康的干预措施的需求。已有研究表明，可穿戴设备和移动技术具备提供数据驱动干预措施的潜力。然而，开发有效的数据驱动系统需要能捕捉个体在日常情境下情绪变异性与变化的情绪数据。现有的数据收集方法主要依赖频繁的预设提示以及预定义量表或问卷，这些方法往往未能考虑参与者的可用性、自主性或情绪体验的复杂性，导致数据浅显且缺乏情境性。本文呈现一项以参与者为中心的多模态情绪标注应用的可行性研究，该应用围绕用户的情绪强度和可用性设计。研究结果显示，多模态情绪记录可影响参与者的体验与数据记录行为，并证明其具备支持收集更丰富、更细致情绪数据的潜力。
英文摘要
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.
CommentsAccepted at MobileHCI 2026
DOI：10.1145/3821661
