Implementation and evaluation of digital twin framework for Internet of Things based healthcare systems

Abstract

The integration of digital twins (DTs) in healthcare is critical but remains limited in real-time patient monitoring due to challenges in achieving low-latency telemetry transmission and efficient resource management. This paper addresses these limitations by presenting a novel cloud-based DT framework that optimises real-time healthcare monitoring, providing a timely solution for critical healthcare needs. The framework incorporates a Pyomo-based dynamic optimisation model, which reduces telemetry latency by 32% and improves response time by 52%, surpassing existing systems. Leveraging low-cost, low-latency multimodal sensors, the system continuously monitors critical physiological parameters, including SpO2, heart rate, and body temperature, enabling proactive health interventions. A DT definition language (Digital Twin Definition Language)-based time series analysis and twin graph platform further enhance sensor connectivity and scalability. Additionally, the integration of machine learning (ML) strengthens predictive accuracy, achieving 98% real-time accuracy and 99.58% under cross-validation (cv = 20) using the XGBoost algorithm. Empirical results demonstrate substantial improvements in processing time, system stability, and learning capacity, with real-time predictions completed in 17 ms. This framework represents a significant advancement in healthcare monitoring, offering a responsive and scalable solution to latency and resource constraints in real-time applications. Future research could explore incorporating additional sensors and advanced ML models to further expand its impact in healthcare applications.