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Detecting and Characterizing Eating Episodes through Feeding Gestures
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Northwestern University EECS 349 Machine Learning Final Project
Background
Obesity, caused primarily by overeating, is a preventable chronic disease yielding staggering healthcare costs. And while existing passive sensors combined with machine learning algorithms are not yet able to reliably passively detect caloric overeating, they are beginning to reliably detect how and when people eat. With the ubiquitous nature of wrist-worn sensors, existing literature has focused on detecting eating episodes in hopes of aiding users in dietary recall.
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