This project is designed to advance research on problematic eating behavior. The project investigates wearable sensors to measure eating behavior and developing models of behavior that comprise multiple observable behaviors such as eating alone or with friends, or chewing speed. These data can help scientists improve upon current traditional methods such as self-reported eating diaries, which tend to be inconsistent, sparse, and rarely timely. We capture human behavior using a custom wearable augmented camera. Wearable cameras provide rich data, but raise privacy concerns. The project will address these concerns by building a framework using machine learning and information theory while including human-reported privacy concerns. The framework will address wearers' concerns that may limit recording authentic behavior in real-world settings and will optimize algorithms to enhance the detection and classification of human behavior.

The project explores the acceptability of obfuscation techniques on varied activities and their requisite tasks. The proposed research will design a suite of computationally efficient task-specific algorithms that use raw images in computationally restrictive (in situ) and obfuscated images in unrestrictive environments (offline) to build information-performance curves for the scalable development of personalized ground truth wearable cameras. The project also will develop a modular, plug-and-play, low-complexity and efficient obfuscation computing hardware device to facilitate and accelerate the use of the proposed methods and algorithms. This work will validate an overeating behavior model in a real-world setting using the design framework and device, providing visual confirmation of eating behaviors, showing how it can be used to test existing models. This project is likely to be useful to other domains in the social sciences, fundamentally changing the way researchers build and validate behavioral models in real-world settings. There are potential applications in health (especially preventive medicine), social, and economic sciences: energy balance, infant development, medication adherence, consumer behavior, and human-environment interaction.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
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Sara Kiesler
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Northwestern University at Chicago
United States
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