Electronic Humidity Sensors for Biomedical Applications: Trends, Machine Learning Support, and Emerging Nanomaterial Strategies

Humidity sensors are considered an important component in monitoring the moisture released from the human body for various applications, including breathing pattern analysis, speech recognition, skin wetness detection, and noncontact human–machine interfaces. The demand for such sensors and sensing systems has increased significantly with the growth of wearable electronics. Despite the significant progress made in this area, numerous obstacles and critical research gaps remain to be addressed. Nanomaterials, which are mostly the receptors in modern humidity sensors due to their large surface area, tunable properties, and excellent electrical and mechanical properties, have emerged as a key enabler in enhancing sensor performance. This review discusses the latest developments in humidity sensors, focusing on how recent advancements in material science, device architecture, and computational modeling collectively enhance sensor functionality. In the context of wearable devices, machine learning (ML) models can perform sensor fusion, integrating humidity data with other physiological signals to more reliably detect skin wetness and interpret gestures for noncontact human–machine interfaces. The applications of these sensors in the biomedical field, along with an emphasis on the need for robust ML models, are also discussed in detail.