Understanding how humans interact with the physical world is essential for teaching intelligent systems to perform complex tasks effectively. Everyday activities, such as grasping a coffee mug and taking a sip, often involve the seamless integration of visual perception and motor control, a process that current intelligent systems struggle to replicate. This project aims to bridge the gap by developing a new family of visual sensing and learning approaches to track hand motion and interpret daily interactions, all using wearable cameras. Further, this project will demonstrate a key application in occupational safety and health by adapting the technology to assess injury risks in the workplace. By advancing the sensing and analysis of human interactions, this research will deepen the understanding of human intelligent behavior, drive the development of more capable intelligent systems, and expand practical applications of such systems. The project’s outputs, including open-source algorithms and hardware platforms, will be disseminated through public competitions, online courses, and diverse outreach activities. This project will advocate a new paradigm of proprioceptive vision, where an intelligent system integrates physical awareness with visual perception to understand how its actions relate to its observations. The project’s goal will be achieved through two interconnected research thrusts. Thrust 1 will develop computer vision approaches to harness wrist-mounted, spatia