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Smartphone-based Real-time Indoor Location Tracking with One-meter Precision

Krause, PJ and Liang, PC (2016) Smartphone-based Real-time Indoor Location Tracking with One-meter Precision IEEE Journal of Biomedical and Health Informatics, 20 (3). pp. 756-762.

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Monitoring the activities of daily living of the elderly at home is widely recognized as useful for the detection of new or deteriorating health conditions. However, the accuracy of existing indoor location tracking systems remains unsatisfactory. The aim of this study was, therefore, to develop a localization system that can identify a patient's real-time location in a home environment with maximum estimation error of 2 m at a 95% confidence level. A proof-of-concept system based on a sensor fusion approach was built with considerations for lower cost, reduced intrusiveness, and higher mobility, deployability, and portability. This involved the development of both a step detector using the accelerometer and compass of an iPhone 5, and a radio-based localization subsystem using a Kalman filter and received signal strength indication to tackle issues that had been identified as limiting accuracy. The results of our experiments were promising with an average estimation error of 0.47 m. We are confident that with the proposed future work, our design can be adapted to a home-like environment with a more robust localization solution.

Item Type: Article
Subjects : Computing
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Krause, PJ
Liang, PC
Date : 9 May 2016
DOI : 10.1109/JBHI.2015.2500439
Copyright Disclaimer : © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Uncontrolled Keywords : Kalman filter, Localization, Received signal strength, Sensor fusion, Step detection, Telemonitoring
Depositing User : Symplectic Elements
Date Deposited : 18 Jul 2016 15:45
Last Modified : 31 Oct 2017 18:27

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