用可穿戴设备连续采集日常行为数据，助力健康研究
Understanding Human Daily Experience Through Continuous Sensing: ETRI Lifelog Dataset 2024
- 通过手机、手表等设备全天候无感采集生理与行为数据
- 收集多日客观数据及睡眠前后主观疲劳/压力评分
- 公开部分匿名数据，适合睡眠、心理状态研究者使用
提升人类健康与福祉需要准确理解个体在日常生活中的身心状态。为此，我们利用智能手机、智能手表和睡眠传感器，以最小干扰方式实现全天候被动连续数据采集，获得多日的日常行为与睡眠活动量化数据。同时，在睡眠前后即时开展问卷调查，获取参与者对疲劳、压力和睡眠质量的主观报告。该综合性生活日志数据集有望为探索人类日常生活与生活方式模式提供基础资源，部分数据已匿名化并公开，供后续研究使用。本文介绍ETRI Lifelog Dataset 2024的数据结构，并展示其应用潜力，如利用机器学习模型预测睡眠质量与压力水平。
原文摘要 · Abstract (English)
Improving human health and well-being requires an accurate and effective understanding of an individual's physical and mental state throughout daily life. To support this goal, we utilized smartphones, smartwatches, and sleep sensors to collect data passively and continuously for 24 hours a day, with minimal interference to participants' usual behavior, enabling us to gather quantitative data on daily behaviors and sleep activities across multiple days. Additionally, we gathered subjective self-reports of participants' fatigue, stress, and sleep quality through surveys conducted immediately before and after sleep. This comprehensive lifelog dataset is expected to provide a foundational resource for exploring meaningful insights into human daily life and lifestyle patterns, and a portion of the data has been anonymized and made publicly available for further research. In this paper, we introduce the ETRI Lifelog Dataset 2024, detailing its structure and presenting potential applications, such as using machine learning models to predict sleep quality and stress.
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