Signal and Image Processing Techniques for Non-Contact Vital Sign Monitoring in Biomedical and Healthcare Applications

Recent advances in vital-sign sensing technologies have accelerated the development of non-invasive, unobtrusive, and non-contact biomedical measurement systems. Conventional contact-based methods can impose physical or psychological stress on subjects, disturb natural physiological states, and potentially affect measurement accuracy. The same limitations apply to in vitro settings, where physical probes and labels can perturb delicate biological samples. Non-contact monitoring has therefore attracted growing attention as a promising approach for acquiring reliable physiological information while minimizing burden on both living subjects and in vitro systems.This Research Topic focuses on signal and image processing technologies for non-contact measurement and their biomedical, healthcare, and safety-related applications. It welcomes studies on sensing principles, algorithm development, system design, sensor integration, validation methods, and real-world applications using radar, thermal imaging, optical imaging, RGB/NIR cameras, depth sensors, wearable-free biomedical sensors, and multimodal sensing systems. We also welcome non-contact signal monitoring techniques applied to in vitro platforms, such as label-free imaging and optical, thermal, or impedance-based readouts for cell cultures, organoids, organ-on-a-chip devices, and bioreactors.Topics of interest include, but are not limited to, non-contact measurement of respiration, heart rate, body temperature, blood pressure, heart rate variability, stress, sleep-related physiological changes, and other vital or behavioral indicators. In vitro applications of interest include non-contact monitoring of cell viability, proliferation, metabolic activity, temperature, and cardiomyocyte contractility or electrophysiology in culture and microfluidic systems. Applications may include infant respiration monitoring, elderly care, home healthcare, sleep apnea detection, infectious disease screening, disaster rescue, animal monitoring, clinical or public-health surveillance, and laboratory and preclinical research using in vitro models.Particular emphasis will be placed on advanced signal and image processing methods, including motion artifact reduction, feature extraction, physiological signal reconstruction, machine learning, deep learning, sensor fusion, real-time monitoring, and edge-computing implementation. Contributions addressing accuracy, robustness, interpretability, clinical and biological validation, privacy, usability, and practical deployment challenges are especially encouraged.By bringing together recent advances in sensing hardware, computational methods, and biomedical applications across both in vivo and in vitro settings, this Research Topic aims to promote the development of reliable, low-burden, and practical non-contact monitoring technologies for next-generation healthcare, laboratory, and safety systems. Topic editors: Guanghao Sun, Eddie YK Ng, Takemi Matsui Source: https://www.frontiersin.org/research-topics/82872/signal-and-image-processing-techniques-for-non-contact-vital-sign-monitoring-in-biomedical-and-healthcare-applications