Research Article
Context Inference for Mobile Applications in the UPCASE Project
@INPROCEEDINGS{10.1007/978-3-642-01802-2_26, author={Andr\^{e} Santos and Lu\^{\i}s Tarrataca and Jo\"{a}o Cardoso and Diogo Ferreira and Pedro Diniz and Paulo Chainho}, title={Context Inference for Mobile Applications in the UPCASE Project}, proceedings={MobileWireless Middleware, Operating Systems, and Applications. Second International Conference, Mobilware 2009, Berlin, Germany, April 28-29, 2009 Proceedings}, proceedings_a={MOBILWARE}, year={2012}, month={5}, keywords={Context-aware services context inference smartphones wearable sensors decision trees}, doi={10.1007/978-3-642-01802-2_26} }- André Santos
Luís Tarrataca
João Cardoso
Diogo Ferreira
Pedro Diniz
Paulo Chainho
Year: 2012
Context Inference for Mobile Applications in the UPCASE Project
MOBILWARE
Springer
DOI: 10.1007/978-3-642-01802-2_26
Abstract
The growing processing capabilities of mobile devices coupled with portable and wearable sensors have enabled the development of context-aware services tailored to the user environment and its daily activities. The problem of determining the user context at each particular point in time is one of the main challenges in this area. In this paper, we describe the approach pursued in the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via . We describe the system architecture from raw data acquisition to feature extraction and context inference. As a proof of concept, the inference of contexts is based on a decision tree to learn and identify contexts automatically and dynamically at runtime. Preliminary results suggest that this is a promising approach for context inference in several application scenarios.