Fall Detection and 3-D Indoor Localization by a Custom RFID Reader Embedded in a Smart e-Health Platform

In this article, we describe a customized 2.45-GHz radio frequency identification (RFID) reader designed to simultaneously perform 3-D tracking of multiple tagged entities (objects or people), static or dynamic, in harsh electromagnetic indoor environments. This is obtained by a bi-dimensional elect...

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Veröffentlicht in:IEEE transactions on microwave theory and techniques 2019-12, Vol.67 (12), p.5329-5339
Hauptverfasser: Paolini, Giacomo, Masotti, Diego, Antoniazzi, Francesco, Salmon Cinotti, Tullio, Costanzo, Alessandra
Format: Artikel
Sprache:eng
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Zusammenfassung:In this article, we describe a customized 2.45-GHz radio frequency identification (RFID) reader designed to simultaneously perform 3-D tracking of multiple tagged entities (objects or people), static or dynamic, in harsh electromagnetic indoor environments. This is obtained by a bi-dimensional electronic beam-steering, implementing the monopulse radar concept simultaneously in the elevation and azimuth directions, with tags-reader distance estimation based on received signal strength indicator (RSSI) measurements. Experimental results show that the system is able to perform a tri-dimensional scanning of a monitored room with decimeter-accuracy over the three reference axes. The RF front end is designed to be lightweight, thin, and compact in such a way that it is portable and embeddable in domestic objects. For this purpose, a multi-layer solution is adopted with a 2-D patch antenna array aperture coupled with the RF front end. 3-D localization data are computed onboard by means of a seamless connection of the RF front end with a low-power microcontroller, which is able to store tags 3-D localization data over a multi-hour time frame. A useful method to remotely control the whole system is presented, using a Raspberry Pi 3B, directly connecting the reader with a flexible and extensive digital platform for Smart Homes. The presented architecture is experimentally demonstrated to perform a reliable fall detection of tagged people in indoor environments.
ISSN:0018-9480
1557-9670
DOI:10.1109/TMTT.2019.2939807