Continuous real-time tracking of the eye and field-of-view of an individual is profoundly important to understanding how humans perceive and interact with the physical world. This work advances both the technology and engineering of cyber-physical systems by designing an innovative paradigm involving next-generation computational eyeglasses that interact with a user's mobile phone to provide the capability for real-time visual context sensing and inference. This research integrates novel research into low-power embedded systems, image representation, image processing and machine learning, and mobile sensing and inference, to advance the state-of-art in continuous sensing for CPS applications. The activity addresses several fundamental research challenges including: 1) design of novel, highly integrated, computational eyeglasses for tracking eye movements, the visual field of a user, and head movement patterns, all in real-time; 2) a unified compressive signal processing framework that optimizes sensing and estimation, while enabling re-targeting of the device to perform a broad range of tasks depending on the needs of an application; 3) design of a novel real-time visual context sensing system that extracts high-level contexts of interest from compressed data representations; and 4) a layer of intelligence that combines contexts extracted from the computational eyeglass together with contexts obtained from the mobile phone to improve energy-efficiency and sensing accuracy. This technology can revolutionize a range of disciplines including transportation, healthcare, behavioral science and market research. Continuous monitoring of the eye and field-of-view of an individual can enable detection of hazardous behaviors such as drowsiness while driving, mental health issues such as schizophrenia, addictive behavior and substance abuse, neurological disease progression, head injuries, and others. The research provides the foundations for such applications through the design of a prototype platform together with real-time sensor processing algorithms, and making these systems available through open source venues for broader use. Outreach for this project includes demonstrations of the device at science fairs for high-school students, and integration of the platform into undergraduate and graduate courses.