Mobile and Ubiquitous Systems: Computing, Networking, and Services. 9th International Conference, MobiQuitous 2012, Beijing, China, December 12-14, 2012. Revised Selected Papers

Research Article

Recognizing a Mobile Phone’s Storing Position as a Context of a Device and a User

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  • @INPROCEEDINGS{10.1007/978-3-642-40238-8_7,
        author={Kaori Fujinami and Satoshi Kouchi},
        title={Recognizing a Mobile Phone’s Storing Position as a Context of a Device and a User},
        proceedings={Mobile and Ubiquitous Systems: Computing, Networking, and Services. 9th International Conference, MobiQuitous 2012, Beijing, China, December 12-14, 2012. Revised Selected Papers},
        proceedings_a={MOBIQUITOUS},
        year={2013},
        month={9},
        keywords={},
        doi={10.1007/978-3-642-40238-8_7}
    }
    
  • Kaori Fujinami
    Satoshi Kouchi
    Year: 2013
    Recognizing a Mobile Phone’s Storing Position as a Context of a Device and a User
    MOBIQUITOUS
    Springer
    DOI: 10.1007/978-3-642-40238-8_7
Kaori Fujinami1,*, Satoshi Kouchi1
  • 1: Tokyo University of Agriculture and Technology
*Contact email: fujinami@cc.tuat.ac.jp

Abstract

A mobile phone is getting smarter by employing a sensor and awareness of various contexts about a user and the terminal itself. In this paper, we deal with 9 storing positions of a smartphone on the body as a context of a device itself and a user: 1) around the neck (hanging), 2) chest pocket, 3) jacket pocket (side), 4) front pocket of trousers, 5) back pocket of trousers, 6) backpack, 7) handbag, 8) messenger bag, and 9) shoulder bag. We propose a method of recognizing the 9 positions by machine learning algorithms with 60 features that characterize specific movements of a terminal at the position during walking. The result of offline experiment showed that an overall accuracy was 74.6% in a strict condition of (LOSO) test, where a support vector machine (SVM) classifier was trained with dataset from other subjects.