Wearable technologies are rapidly transforming healthcare, sports science, occupational safety, and personal wellbeing, while similar sensing approaches are increasingly explored for monitoring physiological processes in plants and crops. Devices capable of continuously collecting parameters such as heart rate, respiratory activity, blood oxygen saturation, body temperature, movement, or plant-related signals such as elongation, sap flow, and hydration status are progressively integrated into everyday environments and digital monitoring platforms. These systems support emerging paradigms such as telemedicine, remote patient monitoring, and the use of plants as biological sensors or precision agriculture, enabling proactive and data-driven management of biological systems.

From a measurement science perspective, the widespread adoption of wearable systems raises critical questions regarding accuracy, metrological traceability, uncertainty estimation, and the comparability between measurements obtained in a laboratory and uncontrolled, real-world conditions. Issues such as motion artifacts, sensor-skin or sensor-plant interface variability, and the lack of standardized validation protocols challenge the reliability of wearable-derived data. In addition, the large volume of data generated by wearable devices requires robust methods for signal processing, knowledge extraction, and advanced data analysis, nowadays particularly focused on artificial intelligence, to ensure meaningful interpretation of biosignals in real-world contexts.

This Special Session brings together researchers addressing both instrumentation challenges and metrological aspects of wearable monitoring across human and environmental applications, fostering interdisciplinary exchange across biomedical engineering, measurement science, agriculture technologies, and digital health.

Wearable sensors, Physiological monitoring, Biosignal analysis, Metrological characterization, Signal processing, Artifact reduction, Multimodal sensing, Plant monitoring, Artificial intelligence, Machine learning, Digital health, Remote monitoring