Signal Quality in Remote Health Devices Using Wearable Antenna Placement: Agent Modeling

Abstract

The rapid proliferation of remote health monitoring systems has revolutionized personalized medicine, enabling continuous physiological tracking outside traditional clinical environments. A critical determinant of the efficacy of these systems is the reliability of the wireless communication link between the wearable sensor and the data aggregation node. Wearable antennas, however, suffer from severe signal degradation due to the complex and dynamic electromagnetic properties of the human body, including tissue absorption, multipath fading, and shadowing caused by postural articulation. Traditional computational electromagnetics approaches, such as finite difference time domain methods, are computationally prohibitive for modeling continuous dynamic movements over extended periods. This paper proposes a novel predictive framework utilizing Agent-Based Modeling to simulate and predict signal quality based on wearable antenna placement. By abstracting the human body and the communication nodes as autonomous, interacting agents governed by localized electromagnetic and biomechanical rules, the proposed framework dynamically assesses signal-to-noise ratios and packet delivery ratios across various simulated postures and placements. Our methodology incorporates specific tissue dielectric profiles and dynamic mobility models to capture the spatiotemporal variations in signal propagation. Through extensive simulations validated against empirical baseline datasets, the agent-based approach demonstrates high predictive accuracy while drastically reducing computational overhead. The findings provide comprehensive guidelines for optimal antenna placement in the design of next-generation remote health devices, ensuring robust continuous data transmission for critical healthcare applications.