MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning

Abstract

Testing healthcare Internet of Things (IoT) applications at system and integration levels necessitates integrating numerous medical devices. Challenges of incorporating medical devices are: (i) their continuous evolution, making it infeasible to include all device variants and (ii) rigorous testing at scale requires multiple devices and their variants, which is time-intensive, costly, and impractical. Our collaborator, Oslo City's health department, faced these challenges in developing automated test infrastructure, which our research aims to address. In this context, we propose a meta-learning-based approach (MeDeT) to generate digital twins (DTs) of medical devices and adapt DTs to evolving devices. We evaluate MeDeT in Oslo City's context using five widely used medical devices integrated with a real-world healthcare IoT application. Our evaluation assesses MeDeT's ability to generate and adapt DTs across various devices and versions using different few-shot methods, the fidelity of these DTs, the scalability of operating 1,000 DTs concurrently, and the associated time costs. Results show that MeDeT can generate DTs with over 96% fidelity, adapt DTs to different devices and newer versions with reduced time cost (around one minute), and operate 1,000 DTs in a scalable manner while maintaining the fidelity level, thus serving in place of physical devices for testing.