Understanding people and extracting health-related metrics is an emerging research topic in computer vision that has grown rapidly recently. Without the need of any physical contact of the human body, cameras have been used to measure vital signs remotely (e.g. heart rate, heart rate variability, respiration rate, blood oxygenation saturation, pulse transit time, body temperature, etc.) from an image sequence of the skin or body, which leads to contactless, continuous and comfortable heath monitoring. The use of cameras also enables the measurement of human behaviors/activities and high-level visual semantic/contextual information leveraging computer vision and machine learning techniques. Understanding of the environment around the people is also a unique advantage of cameras compared to the contact bio-sensors (e.g., wearables), which facilitates better understanding of human and scene for health monitoring. In addition to camera based approach, Radio Frequency (RF) based methods for health monitoring have also been proposed, using Radar, WiFi, RFID, and acoustic signals. The contactless monitoring of camera and RF 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, called “AI health monitoring”. In this tutorial, we will give an overview of recent works in this emerging direction.