Edge-intelligent AIoT digital twins for scalable and privacy-aware healthcare monitoring

The convergence of artificial intelligence, sensing technologies, and networked systems is transforming healthcare monitoring beyond traditional clinical settings. Digital twins offer a powerful paradigm for personalized health modeling, while advances in Artificial Intelligence of Things (AIoT) enable intelligent sensing and analysis within everyday environments. However, existing healthcare systems typically treat these domains separately, either employing cloud-centric digital twins or AIoT platforms based on continuous data streaming. To the best of our knowledge, no prior work explicitly integrates digital twin modeling with edge-intelligent AIoT architectures for healthcare, leaving a critical gap between these complementary research areas. Such cloud-centric designs lead to high power consumption, excessive communication bandwidth, privacy risks, and poor scalability for long-term, population-level deployment. This paper presents an edge-intelligent, event-driven AIoT digital twin architecture for scalable and privacy-aware healthcare monitoring. The proposed system performs feature extraction, inference, and event detection locally on resource-constrained AIoT devices, transmitting only semantic health state updates and periodic summaries to maintain digital twins. We evaluate the architecture through computational simulations of a fall and instability monitoring scenario. Results show that the event-driven edge design reduces per-patient communication achieving orders-of-magnitude lower device-side energy consumption and bandwidth usage compared to continuous cloud- and edge-centric baselines. Sensitivity analysis demonstrates robustness across event frequencies, window sizes, and feature extraction costs, while population-scale analysis confirms that system costs scale with event frequency rather than sensor sampling rate. These findings indicate that event-driven edge intelligence is a foundational architectural principle for digital twin healthcare syste…