EmoPhone: A Multi-Wave Dataset for In-the-Wild Mobile and Wearable Affect Sensing

2026-09-14

AI Summary

研究通过构建一个包含智能手机、可穿戴设备和密集体验采样标签的多波数据集，采用不同方法解决情感感知中的用户间和跨时间泛化问题。

Abstract

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.

Problem

• affect sensing
• in-the-wild
• longitudinal deployment
• participant variability
• cross-wave differences

Innovation

• multi-wave dataset
• affect sensing
• experience-sampling-method
• domain adaptation
• cross-wave generalization