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.