Methodologies and Techniques for Metrological Characterization of Wearable Devices

Wearable sensors are increasingly evolving from health assistance tools toward medical diagnostic devices, raising crucial requirements on metrological characterization. However, current charac terization approaches largely remain based on static and offline methodologies mostly designed for controlled laboratory settings, which may not fully capture the dynamic and complex in vivo conditions of real-world wearable use. This gap poses a major challenge for ensuring calibration reliability and supporting the broader clinical adoption of wearable sensing technologies. This review provides an analytical overview of metrological characterization strategies across biophysical, biochemical, bioelectrical, and multimodal wearable systems. It compares methodological approaches across these categories and analyzes their dif ferent requirements across medical, consumer, and research scenarios. The review reveals that calibration strategies are inherently scenario-dependent, with medical applications demanding full-range multipoint traceability, consumer devices relying on lightweight adaptive algorithms, and research settings requiring flexible protocols. Furthermore, this review critically examines emerging frontiers, including calibration under real-life conditions, self-calibrating sensors, AI-driven metrology, and standardization. A key finding is that the field is moving toward a unified paradigm of dynamic, intelligent, and autonomous metrology, in which calibration becomes adaptive, context-aware, and capable of maintaining measurement reliability with minimal human intervention. Finally, it outlines a roadmap toward a new “dynamic, in vivo, and AI-integrated” calibration paradigm. This review aims to provide a comprehensive overview that combines theoretical depth with practical guidance to advance wearable technologies from laboratory tools toward clinically relevant instruments