K-EmoPhone, A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

ABSTRACT: With the popularization of low-cost mobile and wearable sensors, many prior studies used such sensors to track and analyze people's mental well-being, productivity, and behavioral patterns. However, there is a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention. This limits the advances in affective computing and human-computer interaction research. In this work, we present K-EmoPhone, an in-the-wild naturalistic dataset (n=80, 1-week) of smartphone use, wearable sensing, and self-reported affect states from college students. The dataset contains continuous probing of peripheral physiological signals and mobility data measured by off-the-shelf commercial devices in addition to context and interaction data by users' smartphones. Moreover, the dataset includes self-reports of in-situ affect states (n=5,753) such as emotion, stress level, attention level, and disturbance level, acquired by the experience sampling method. The resulting K-EmoPhone dataset helps to advance the research and development of affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data. Last update: Aug. 3, 2022 ----------------------------- * Version 1.0.0 (Aug. 3, 2022) Added P##.zip files, where each P## means the separate participant. Added SubjData.zip file, which includes individual characteristics information and labels.

摘要：随着低成本移动与可穿戴传感器的普及，既往诸多研究依托此类传感器追踪并分析人类的心理健康状态、生产力水平与行为模式。然而，当前缺乏在真实场景中采集的、带有情感与认知状态标签（如情绪、压力与注意力）的开放数据集，这制约了情感计算（Affective Computing）与人机交互（Human-Computer Interaction）领域研究的进展。本研究推出K-EmoPhone数据集：这是一项采集自大学生群体的智能手机使用、可穿戴传感数据与自我报告情感状态的真实世界自然场景数据集（样本量n=80，采集周期为1周）。该数据集除包含用户智能手机采集的上下文与交互数据外，还涵盖了由商用现成设备采集的外周生理信号与移动性连续监测数据。此外，数据集通过经验抽样法（Experience Sampling Method）获取了共计5753份原位情感状态自我报告，内容涉及情绪、压力水平、注意力水平与干扰程度。本K-EmoPhone数据集有助于推动基于移动与可穿戴传感数据的情感计算、情感智能技术以及注意力管理相关研究与开发。最后更新时间：2022年8月3日
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* 版本1.0.0（2022年8月3日）：新增P##.zip压缩包，其中P##代表独立参与者的编号；新增SubjData.zip压缩包，内含个体特征信息与标签数据。