A Hybrid Near-Far Field Channel Model for RIS-Assisted Healthcare Monitoring and Precision Positioning in Internet of Everything

Recent advancements in wireless communications have revolutionized healthcare delivery through Internet of Everything (IoE) connectivity, yet achieving precise medical device positioning and reliable patient monitoring remains a fundamental challenge. As communication frequencies increase and reconfigurable intelligent surface (RIS) apertures expand in healthcare settings, the operating conditions gradually transition from traditional far-field (FF) radiation patterns to near-field (NF) scenarios. This article proposes a healthcare channel hybrid model (HCHM) framework that seamlessly integrates NF and FF characteristics. The weighting factor adapts dynamically according to the ratio of near-field to FF route gains, facilitating appropriate tradeoffs between positioning precision and monitoring coverage. We present a complete channel model tailored for healthcare IoE applications by incorporating an adaptive weighting factor that accounts for the proximity and placement needs of medical devices. Based on 10000 Monte Carlo simulations in a typical hospital environment ( $30\times 30$ RIS array, 100-MHz bandwidth, 2–200 m monitoring range), with 95% confidence intervals, HCHM achieves positioning accuracies of $0.3~\pm ~0.02$ mm in critical care scenarios while maintaining reliable patient monitoring across extended distances. Simulation results demonstrate that HCHM achieves significant improvements in system performance and model robustness, with positioning accuracy enhanced by 45% and monitoring reliability improved by 42% compared to conventional approaches, making it particularly suitable for next-generation healthcare IoE applications.