ContextLabeler Dataset: physical and virtual sensors data collected from smartphone usage in-the-wild

TL;DR

This paper presents a comprehensive dataset collected from smartphone sensors in real-world conditions, capturing diverse user activities and contexts to facilitate development of context-aware mobile solutions.

Contribution

The paper introduces a large, labeled dataset from in-the-wild smartphone sensor data, enabling research on context-aware applications without user behavior constraints.

Findings

Dataset contains over 45,000 samples with 1332 features each.

Includes diverse physical and virtual sensor data with activity labels.

Supports development and evaluation of context-aware algorithms.

Abstract

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.