EmoPhone：用于野外移动和可穿戴情感感知的多波数据集
EmoPhone: A Multi-Wave Dataset for In-the-Wild Mobile and Wearable Affect Sensing
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中文总结 AI 辅助
本研究提出三波次野外多模态情感数据集EmoPhone，整合手机与可穿戴感知及ESM标签，并通过三设置基准评估，发现不同设置下最优方法各异，揭示移动情感计算受标签和参与者变异性制约。
中文摘要 AI 辅助
我们引入了一个三波次的野外多模态情感感知数据集，该数据集整合了智能手机感知、可穿戴感知以及从2020年到2022年每年收集的密集经验采样法（ESM）标签。该数据集通过所有波次共享的维度标签核心支持时刻级情感建模，并在第三波次（D-3）中提供了额外的情感描述符。我们从研究设计、原位标签的时间密度以及各波次的感知和标签覆盖范围方面描述了该资源。为了支持在此资源内的评估，我们定义了一个初始的三设置基准，涵盖来自用户历史的时间预测、波内跨用户泛化以及跨波泛化，其中每个波次被视为一个独立数据集。我们的基准结果表明，最强的方法系列取决于评估设置：监督基线在时间设置中表现最佳，无监督域适应在波内跨用户设置中整体最强，而域泛化在跨波性能上整体最强，尽管其相对于强基线的优势不大。这些发现表明，稳健的移动情感计算不仅受到标签可用性的限制，还受到纵向原位部署中固有的显著参与者级变异性和现实跨波差异的制约。
英文摘要
We introduce a three-wave, in-the-wild multimodal dataset for affect sensing that integrates smartphone sensing, wearable sensing, and dense experience-sampling-method (ESM) labels collected annually from 2020 to 2022. The dataset supports moment-level affect modeling through a shared dimensional label core across all waves, with additional affective descriptors available in the third wave (D-3). We describe the resource in terms of study design, temporal density of in-situ labels, and sensing and label coverage across waves. To support evaluation within this resource, we define an initial three-setting benchmark spanning temporal prediction from within-user history, within-wave cross-user generalization, and cross-wave generalization in which each wave is treated as a separate dataset. Our benchmark results show that the strongest method family depends on the evaluation setting: supervised baselines perform best in the temporal setting, unsupervised domain adaptation is strongest overall in the within-wave cross-user setting, and domain generalization shows the strongest overall cross-wave performance, although its margin over strong baselines is modest. These findings indicate that robust mobile affective computing is constrained not only by label availability but also by substantial participant-level variability and realistic cross-wave differences inherent in longitudinal in-situ deployments.
发表机构
- Graduate School of Data Science, KAIST（KAIST数据科学研究生院）
- Samsung Electronics Co., Ltd.（三星电子株式会社）
- School of Computing, KAIST（KAIST计算机学院）
- R&D Department, HumbleBeeAI（HumbleBeeAI研发部）
- Department of Data Science and Department of Computer Engineering, Kangwon National University（江原国立大学数据科学与计算机工程系）
- Department of Computer Engineering, Gachon University（嘉泉大学计算机工程系）
- Department of Computer Science and Information Systems, BITS Pilani, K K Birla Goa Campus（比拉理工学院皮拉尼校区计算机科学信息系统系，K K Birla果阿校区）
- Department of AI Computing, KAIST（KAIST人工智能计算系）
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