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Empowering the Blind: Contactless Activity Recognition with Commodity Software-Defined Radio and Ultra-High-Frequency Radio Frequency Identification.
Khan, Muhammad Zakir; Althobaiti, Turke; Almutiry, Muhannad; Ramzan, Naeem.
Afiliação
  • Khan MZ; James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
  • Althobaiti T; Department of Computer Science, Faculty of Science, Northern Border University, Arar 73222, Saudi Arabia.
  • Almutiry M; Electrical Engineering Department, Northern Border University, Arar 73222, Saudi Arabia.
  • Ramzan N; School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK.
Sensors (Basel) ; 24(11)2024 Jun 04.
Article em En | MEDLINE | ID: mdl-38894436
ABSTRACT
This study presents a novel computational radio frequency identification (RFID) system designed specifically for assisting blind individuals, utilising software-defined radio (SDR) with coherent detection. The system employs battery-less ultra-high-frequency (UHF) tag arrays in Gen2 RFID systems, enhancing the transmission of sensed information beyond standard identification bits. Our method uses an SDR reader to efficiently manage multiple tags with Gen2 preambles implemented on a single transceiver card. The results highlight the system's real-time capability to detect movements and direction of walking within a four-meter range, indicating significant advances in contactless activity monitoring. This system not only handles the complexities of multiple tag scenarios but also delineates the influence of system parameters on RFID operational efficiency. This study contributes to assistive technology, provides a platform for future advancements aimed at addressing contemporary limitations in pseudo-localisation, and offers a practical, affordable assistance system for blind individuals.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article