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Information 2016, 7(2), 21; doi:10.3390/info7020021

On-Body Smartphone Localization with an Accelerometer

Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho Koganei, Tokyo 184-8588, Japan
Academic Editors: James Park, Marek R. Ogiela and Yang Xiao
Received: 17 November 2015 / Revised: 7 March 2016 / Accepted: 10 March 2016 / Published: 29 March 2016
(This article belongs to the Special Issue Future Information Technology and Intelligent Systems)
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Abstract

A user of a smartphone may feel convenient, happy, safe, etc., if his/her smartphone works smartly based on his/her context or the context of the device. In this article, we deal with the position of a smartphone on the body and carrying items like bags as the context of a device. The storing position of a smartphone impacts the performance of the notification to a user, as well as the measurement of embedded sensors, which plays an important role in a device’s functionality control, accurate activity recognition and reliable environmental sensing. In this article, nine storing positions, including four types of bags, are subject to recognition using an accelerometer on a smartphone. In total, 63 features are selected as a set of features among 182 systematically-defined features, which can characterize and discriminate the motion of a smartphone terminal during walking. As a result of leave-one-subject-out cross-validation, an accuracy of 0.801 for the nine-class classification is shown, while an accuracy of 0.859 is obtained against five classes, which merges the subclasses of trouser pockets and bags. We also show the basic performance evaluation to select the proper window size and classifier. Furthermore, the analysis of the contributive features is presented. View Full-Text
Keywords: smartphone; on-body position; device localization; accelerometer; machine learning; feature selection; activity recognition; opportunistic sensing; intelligent systems; wearable computing smartphone; on-body position; device localization; accelerometer; machine learning; feature selection; activity recognition; opportunistic sensing; intelligent systems; wearable computing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Fujinami, K. On-Body Smartphone Localization with an Accelerometer. Information 2016, 7, 21.

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