DIGITAL TWINS FOR HARDWARE-IN-THE-LOOP MODELING OF MOBILE MEDICAL DIAGNOSTIC SYSTEMS

Abstract

The rapid development of mobile medical diagnostic systems, wearable sensors, the Internet of Medical Things, edge computing, and artificial intelligence enables a transition from episodic physiological measurements toward continuous personalized patient monitoring. At the same time, increasing hardware-software complexity requires safe validation of sensing, computing, communication, and diagnostic components before their use with real patients. Hardware-in-the-Loop has already been applied to pacemakers, artificial pancreas systems, automated ventilation, circulatory support, and other medical devices; however, physiological models used in medical HIL environments are typically specialized test models rather than dynamically synchronized digital twins of individual patients. Contemporary Patient Digital Twins increasingly use wearable and IoMT data for personalization, monitoring, and prediction, but they are rarely used as real-time physiological HIL environments for validating physical mobile medical hardware. This paper proposes an integrated Patient Digital Twin–Hardware-in-the-Loop architecture in which a dynamically personalized twin operates in two interconnected modes: as an operational model synchronized with a physical patient for monitoring and diagnosis, and as an executable physiological environment for HIL validation of real sensing, computing, and communication hardware. The architecture integrates a Patient Digital Twin, a Device Digital Twin, mobile/edge processing, synchronization, an HIL interface, diagnostic services, cloud/history services, and a VVUQ layer. The paper formalizes twin state representation, synchronization error, Context-of-Use-dependent fidelity conditions, real-time constraints, and multi-objective mobile model selection. An experimental protocol is proposed to compare software-only simulation, generic physiological HIL, patient-specific static HIL, and dynamically synchronized Patient DT–HIL using fault injection, Verification, Validation and Uncertainty Quantification, diagnostic performance metrics, latency measurements, and energy profiling. The proposed concept provides a methodological basis for reusing one personalized physiological model throughout development, verification, and operation of a mobile medical diagnostic system.