RFID RSS fingerprinting system for wearable human activity recognition

Shuaieb, Wafa, Oguntala, George, AlAbdullah, Ali, Obeidat, Huthaifa, Asif, Rameez, Abd-Alhameed, Raed A., Bin-Melha, Mohammed S. and Kara-Zaïtri, Chakib (2020) RFID RSS fingerprinting system for wearable human activity recognition. Future Internet, 12 (2). ISSN 1999-5903

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Abstract

Alternative healthcare solutions have been identified as a viable approach to ameliorate the increasing demand for telehealth and prompt healthcare delivery. Moreover, indoor ocalization using different technologies and approaches have greatly contributed to alternative healthcare solutions. In this paper, a cost-effective, radio frequency identification (RFID)-based indoor location system that employs received signal strength (RSS) information of passive RFID tags is presented. The proposed system uses RFID tags placed at different positions on the target body. The mapping of the analysed data against a set of reference position datasets is used to accurately track the vertical and horizontal positioning of a patient within a confined space in real-time. The Euclidean distance model achieves an accuracy of 98% for all sampled activities. However, the accuracy of the activity recognition algorithm performs below the threshold performance for walking and standing, which is due to similarities in the target height, weight and body density for both activities. The obtained results from the proposed system indicate significant potentials to provide reliable health measurement tool for patients at risk.

Item Type: Article
Additional Information: Funding Information: H2020 Marie Skłodowska-Curie Actions: 722424. This work is partially supported by innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424 and the financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/E022936/1. Funding Information: Acknowledgments: This work is partially supported by innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424 and the financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/E022936/1. Publisher Copyright: © 2020 by the authors.
Uncontrolled Keywords: fingerprinting,human activity recognition,indoor ocalization,patient tracking,rfid,computer networks and communications ,/dk/atira/pure/subjectarea/asjc/1700/1705
Faculty \ School: Faculty of Science > School of Computing Sciences
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Depositing User: LivePure Connector
Date Deposited: 25 Aug 2022 00:07
Last Modified: 24 Sep 2022 07:06
URI: https://ueaeprints.uea.ac.uk/id/eprint/87615
DOI: 10.3390/fi12020033

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