MobileWireless Middleware, Operating Systems, and Applications. Second International Conference, Mobilware 2009, Berlin, Germany, April 28-29, 2009 Proceedings

Research Article

Context Inference for Mobile Applications in the UPCASE Project

Download216 downloads
  • @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
André Santos1, Luís Tarrataca1, João Cardoso2,*, Diogo Ferreira1, Pedro Diniz1, Paulo Chainho3
  • 1: Technical University of Lisbon
  • 2: University of Porto
  • 3: PT Inovação S.A.
*Contact email: jmpc@acm.org

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.