This paper describes a data collection campaign and the resulting dataset derived from smartphone sensors characterizing the daily life activities of 3 volunteers in a period of two weeks. The dataset is released as a collection of CSV files containing more than 45K data samples, where each sample is composed by 1332 features related to a heterogeneous set of physical and virtual sensors, including motion sensors, running applications, devices in proximity, and weather conditions. Moreover, each data sample is associated with a ground truth label that describes the user activity and the situation in which she was involved during the sensing experiment (e.g., working, at restaurant, and doing sport activity). To avoid introducing any bias during the data collection, we performed the sensing experiment in-the-wild, that is, by using the volunteers' devices, and without defining any constraint related to the user's behavior. For this reason, the collected dataset represents a useful source of real data to both define and evaluate a broad set of novel context-aware solutions (both algorithms and protocols) that aim to adapt their behavior according to the changes in the user's situation in a mobile environment.

翻译：本文描述了一项数据采集活动及其所得数据集，该数据集源自智能手机传感器，记录了3名志愿者在两周内的日常活动。数据集以CSV文件形式发布，包含超过4.5万个数据样本，每个样本由1332个特征组成，涵盖异构的物理与虚拟传感器集合，包括运动传感器、运行中的应用、邻近设备及天气状况。此外，每个数据样本均附有地面真实标签，描述用户在传感实验期间所从事的活动及所处情境（例如，工作中、餐厅用餐、体育锻炼）。为避免数据采集过程中引入任何偏差，我们采用自然情境实验方式，即使用志愿者自有设备，且不定义任何与用户行为相关的约束。因此，该数据集为定义与评估一系列新颖的上下文感知解决方案（包括算法与协议）提供了真实的现实数据资源，这些方案旨在移动环境中根据用户情境变化自适应调整其行为。