Extracting health-related metrics is an emerging computer vision research topic that has grown rapidly recently. Without needing physical contact, cameras have been used to measure vital signs remotely (e.g. heart & respiration rates, blood oxygenation saturation, body temperature, etc.) from images/video of the skin or body. This leads to contactless, continuous and comfortable heath monitoring. Cameras can also leverage computer vision and machine learning techniques to measure human behaviors/activities and high-level visual semantic/contextual information, facilitating better understanding of people and scenes for health monitoring and provides a unique advantage compared to the contact bio-sensors. RF (Radar, WiFi, RFID) and acoustic based methods for health monitoring have also been proposed. The rapid development of computer vision and RF sensing also give rise to new multi-modal learning techniques that expand the sensing capability by combining two modalities, while minimizing the need of human labels. The hybrid approach may further improve the performance of monitoring, such as using the camera images as beacon to gear human activity learning for the RF signals. Contactless monitoring will bring a rich set of compelling healthcare applications that directly improve upon contact-based monitoring solutions and improve people’s care experience and quality of life, such as in care units of the hospital, sleep/senior centers, assisted-living homes, telemedicine and e-health, fitness and sports, driver monitoring in automotive, etc.