During the last few decades, health monitoring technologies have evolved from isolated wearable devices toward integrated sensor ecosystems combining ambient sensing, smart objects, and non-invasive monitoring solutions. While these technologies are gaining increasing consensus for continuous health monitoring across the entire human lifespan, transitioning them from “consumer gadgets” to “clinical-grade tools” requires a rigorous metrological approach, to ensure stakeholders can appropriately interpret and exploit measurement results. This Special Session focuses on the metrological challenges and opportunities associated with heterogeneous sensor networks for lifelong health monitoring. Particular attention is devoted to the integration and characterization of wearable and non-contact sensing technologies, smart environments, smart objects, flexible electronics, and distributed multi-sensor platforms.

The session aims to address how different sensing modalities can cooperate to provide reliable and continuous assessment of physiological, behavioral, and environmental conditions while minimizing intrusiveness and maximizing usability and acceptability. A core objective is to discuss strategies for ensuring accuracy, reliability, robustness, and traceability of measurements in dynamic and real-life conditions, accounting for the physiological and ergonomic requirements of different age groups. This includes the characterization of smart fabrics and multi-sensor platforms designed for neonates (requiring high sensitivity and biocompatibility), adults (requiring motion artifact resilience/robustness), and the elderly or disabled (requiring long-term stability and non-intrusive form factors). The session also highlights the importance of quantifying measurement uncertainty by considering all sensing components and potential interfering factors that impact data quality in complex environments.

Topics of interest include:

• Uncertainty analysis in dynamic scenarios: methods for quantifying measurement uncertainty not limited to controlled laboratory settings, but extended to real-life, uncontrolled, and dynamic environments (e.g., ambulatory monitoring).
• In-situ and automated calibration to be possibly exploited in-situ and in an automated way.
• Novel strategies and hardware-software architectures for the in-place, periodic, or automated calibration of distributed sensor networks to ensure long-term data reliability.
• Age-Specific Metrological Evaluation: adaptation of measurement protocols and sensor characterization to age-related requirements, such as high sensitivity and biocompatibility for neonates or non-intrusiveness and skin-fragility considerations for the elderly.
• Signal integrity and hardware conditioning: Advanced electronic solutions, adaptive filtering, and smart conditioning to guarantee signal quality, mitigating environmental noise, and motion artifacts in wearable systems.
• Metrological assessment of AI and ML algorithms: performance evaluation and validation of machine learning models used for physiological state prediction, focusing on their robustness, explainability, and error propagation.
• Distributed multi-sensor platforms for diagnostics: integration and synchronization of heterogeneous sensors (wearable, ambient, and smart objects) for comprehensive behavioral and health status assessment.

Sensor networks, wearable sensors, physiological monitoring, behavioural monitoring, measurement accuracy, metrological characterization