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 Leave-One-Subject-Out (LOSO) test, where a support vector machine (SVM) classifier was trained with dataset from other subjects.
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Fujinami, K., Kouchi, S. (2013). Recognizing a Mobile Phone’s Storing Position as a Context of a Device and a User. In: Zheng, K., Li, M., Jiang, H. (eds) Mobile and Ubiquitous Systems: Computing, Networking, and Services. MobiQuitous 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40238-8_7
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DOI: https://doi.org/10.1007/978-3-642-40238-8_7
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