Poor diet is a leading cause of preventable death and diseases, as well as preventable healthcare costs in the United States. Despite the importance of following a healthy dietary pattern, most U.S. adults do not meet national dietary guidelines and are either overweight or obese. There is a critical need for "just-in-time" (JIT) interventions to improve diet and eating behaviors as they occur. To maximize impact, JIT interventions should only be delivered when an individual is receptive, particularly when dietary quality is poor. However, which aspects of the food environment and dietary behavior have influence on dietary intake and quality are unknown, and how they relate to JIT intervention receptivity is unexplored. This would require collecting and analyzing near-continuous data about one's diet in the context of daily life, where behavior actually occurs, which is very challenging for researchers and burdensome for participants. Advances in wearable sensor technologies, equipped with novel computational methods could provide a pathway to capture and analyze the various exposures and patterns in the eating environment to fill this gap. The overall objective of this proposal is to create an integrated system of wearable sensor and computational methods to discover food environment exposures related to dietary quality that influence JIT intervention receptivity. Motivated by this vision, the objectives of this research include: 1) develop novel edge computing hardware and software for privacy-preserving compressive image capture and transmission, 2) develop new collaborative compression and analytics together with unsupervised continual learning to understand eating behavior, 3) determine whether sensed aspects of the environmental context during eating relate to dietary quality and receptivity to JIT interventions, particularly when the dietary quality is poor. The project is a collaborative effort combining expertise in wearable electronics, image processing, dietary patterns, and behavioral science.