RFID RSS Fingerprinting System for Wearable Human Activity Recognition

Wafa Shuaieb, George Oguntala, Ali AlAbdullah, Huthaifa Obeidat, Rameez Asif, Raed A. Abd-Alhameed, Mohammed S. Bin-Melha and Chakib Kara-Zaïtri
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Wafa Shuaieb: Department of Biomedical and Electronics Engineering, Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK
George Oguntala: Department of Biomedical and Electronics Engineering, Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK
Ali AlAbdullah: Department of Biomedical and Electronics Engineering, Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK
Huthaifa Obeidat: Faculty of Engineering, Jerash University, Jerash 26150, Jordan
Rameez Asif: Department of Biomedical and Electronics Engineering, Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK
Raed A. Abd-Alhameed: Department of Biomedical and Electronics Engineering, Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK
Mohammed S. Bin-Melha: Department of Biomedical and Electronics Engineering, Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK
Chakib Kara-Zaïtri: Department of Mechanical and Energy Systems Engineering, Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK

Future Internet, 2020, vol. 12, issue 2, 1-12

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.

Keywords: fingerprinting; human activity recognition; patient tracking; indoor ocalization; RFID (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
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