构建日常情绪数据采集框架，提升情感AI训练质量
AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI
- 从119名利益相关者中提炼真实场景情绪标注难点
- 开发AnnoSense框架并经25位专家验证可用性
- 适合情感AI研究者与可穿戴设备开发者参考
情绪与心理健康是生活质量的重要组成部分。随着智能手机、可穿戴设备和人工智能（AI）的普及，人们在日常环境中监测情绪成为可能。然而，要让AI算法有效，必须依赖高质量数据和准确标注。随着研究重点转向真实世界环境以捕捉更真实的体验，情绪标注过程日益复杂。本研究从关键利益相关者角度探讨了日常情绪数据收集的挑战。我们收集了75份问卷、对公众进行了32次访谈，并组织了3场焦点小组讨论（共12名心理健康专业人士参与）。来自119名受访者的洞见推动了AnnoSense框架的开发，该框架旨在支持面向AI的情绪数据日常采集。该框架随后由25名情绪AI专家评估其清晰性、实用性和可扩展性。最后，我们讨论了AnnoSense对未来情绪AI研究的潜在影响，强调其在真实场景下提升情绪数据采集与分析能力的潜力。
原文摘要 · Abstract (English)
Emotional and mental well-being are vital components of quality of life, and with the rise of smart devices like smartphones, wearables, and artificial intelligence (AI), new opportunities for monitoring emotions in everyday settings have emerged. However, for AI algorithms to be effective, they require high-quality data and accurate annotations. As the focus shifts towards collecting emotion data in real-world environments to capture more authentic emotional experiences, the process of gathering emotion annotations has become increasingly complex. This work explores the challenges of everyday emotion data collection from the perspectives of key stakeholders. We collected 75 survey responses, performed 32 interviews with the public, and 3 focus group discussions (FGDs) with 12 mental health professionals. The insights gained from a total of 119 stakeholders informed the development of our framework, AnnoSense, designed to support everyday emotion data collection for AI. This framework was then evaluated by 25 emotion AI experts for its clarity, usefulness, and adaptability. Lastly, we discuss the potential next steps and implications of AnnoSense for future research in emotion AI, highlighting its potential to enhance the collection and analysis of emotion data in real-world contexts.
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