  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.
