Proceedings of the 3rd International Multi-Disciplinary Conference: “Integrated Sciences and Technologies”, IMDC-IST 2023, 25-27 October 2023, Yola, Nigeria

Research Article

Patient Localization using RFID in Wireless Insite Propagation & Neural Network

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  • @INPROCEEDINGS{10.4108/eai.25-10-2023.2348738,
        author={Prince Okoto Siaw and Ebenezer Adjei and Ahmad Aldelemy and Claudia Barbosa},
        title={Patient Localization using RFID in Wireless Insite Propagation \& Neural Network},
        proceedings={Proceedings of the 3rd International Multi-Disciplinary Conference: “Integrated Sciences and Technologies”, IMDC-IST 2023, 25-27 October 2023, Yola, Nigeria},
        publisher={EAI},
        proceedings_a={IMDC-IST},
        year={2024},
        month={8},
        keywords={remote access epilepsy seizure wireless sensors neural network},
        doi={10.4108/eai.25-10-2023.2348738}
    }
    
  • Prince Okoto Siaw
    Ebenezer Adjei
    Ahmad Aldelemy
    Claudia Barbosa
    Year: 2024
    Patient Localization using RFID in Wireless Insite Propagation & Neural Network
    IMDC-IST
    EAI
    DOI: 10.4108/eai.25-10-2023.2348738
Prince Okoto Siaw1,*, Ebenezer Adjei1, Ahmad Aldelemy1, Claudia Barbosa2
  • 1: Department of Engineering & Digital Technologies, University of Bradford, UK.
  • 2: The Instituto de Telecomunicaçŏes, Campus Universitário de Santiago, Aveiro 3810-193, Portugal
*Contact email: p.o.siaw@bradford.ac.uk

Abstract

Due to strains on healthcare and staff shortages, longer lifespans and improved quality of life have led to inadequate healthcare, especially for individuals with epileptic seizures, particularly those living alone. To address this, engineers are focusing on wireless communication and technology to develop tools that alleviate pressure on healthcare. This study concentrates on monitoring patients during and after certain major or minor surgeries using wireless sensors (RFID) and an application in a smart room setup. The system relies on two sophisticated power measurement models for location detection, including lying down and falling. A neural network in MATLAB learns power transmission variations for accurate prediction. Simulations replicate empty room scenarios for lying down and falling positions. Data is stored in cloud databases and accessible through a real-time app using APIs and IoT technology. This research aims to enhance patient monitoring and response during incidents while lessening strain on healthcare systems.