Biomedical Signals and Systems Research Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, 7522NB, The Netherlands.
Research Center for Information and Communication Technologies, University of Granada, Granada, E-18071, Spain.
Sci Data. 2022 Dec 6;9(1):754. doi: 10.1038/s41597-022-01856-8.
Aiming to illuminate the effects of enforced confinements on people's lives, this paper presents a novel dataset that measures human behaviour holistically and longitudinally during the COVID-19 outbreak. In particular, we conducted a study during the first wave of the lockdown, where 21 healthy subjects from the Netherlands and Greece participated, collecting multimodal raw and processed data from smartphone sensors, activity trackers, and users' responses to digital questionnaires. The study lasted more than two months, although the duration of the data collection varies per participant. The data are publicly available and can be used to model human behaviour in a broad sense as the dataset explores physical, social, emotional, and cognitive domains. The dataset offers an exemplary perspective on a given group of people that could be considered to build new models for investigating behaviour changes as a consequence of the lockdown. Importantly, to our knowledge, this is the first dataset combining passive sensing, experience sampling, and virtual assistants to study human behaviour dynamics in a prolonged lockdown situation.
本文旨在探讨强制隔离对人们生活的影响，为此提出了一个新颖的数据集，全面而系统地记录了 COVID-19 疫情期间人们的行为。具体而言，我们在封锁的第一波疫情期间进行了一项研究，来自荷兰和希腊的 21 名健康受试者参与了该研究，从智能手机传感器、活动追踪器以及用户对数字问卷的反馈中收集了多模态原始和处理后的数据。研究持续了两个多月，但每个参与者的数据采集时长不同。这些数据是公开的，可用于对人类行为进行广义建模，因为该数据集探索了身体、社交、情感和认知领域。该数据集提供了一个关于特定人群的范例视角，可以考虑构建新模型来研究封锁导致的行为变化。重要的是，据我们所知，这是第一个结合被动感知、体验采样和虚拟助手来研究长时间封锁下人类行为动态的数据集。
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