Radio Frequency Tagging-enabled patient monitoring: integrating mobility tracking with early warning systems for enhanced safety.

Abstract

Ensuring patient safety in healthcare environments requires continuous monitoring systems capable of identifying early warning signs of clinical risk. Traditional surveillance methods often fail to capture meaningful patterns in patient movement, limiting their ability to prevent incidents such as falls, prolonged immobility, or unnoticed health deterioration. Radio Frequency Tagging technology has been increasingly adopted for real-time patient tracking; however, existing systems are generally limited to location detection and lack predictive insights into patient behaviour. To overcome these limitations, this study presents a Radio Frequency Tagging-based patient monitoring framework that integrates mobility tracking with an early warning mechanism to enable proactive health-care interventions. The proposed system uses a spatio-temporal probabilistic network to learn typical movement patterns from historical tagging data and applies a dynamic similarity analysis to detect deviations in real time. Anomaly scores generated from these comparisons are combined with clinical indicators to produce automated alerts for healthcare providers, supporting timely and informed responses. The framework is evaluated on a hybrid dataset comprising simulated tagging traces and publicly available physiological and activity data, including measurements of heart rate, respiration, and physical movement. The Dynamic Time Warping-based anomaly detection system achieved consistently high performance across all patient categories, with accuracy ranging from 87.5% to 91.0%, sensitivity between 88.4% and 96.2%, and F1-scores up to 93.5%, demonstrating its strong capability to effectively distinguish between normal and abnormal movement patterns across diverse clinical conditions. By combining location-based surveillance with predictive modelling and clinical scoring, the framework offers a context-aware and reliable tool for continuous patient monitoring, thereby enhancing safety and supporting data-driven clinical decision-making.