PlateMate allows users to
take photos of their meals and receive estimates of food intake and
composition. Accurate awareness of this information is considered a
prerequisite to successful change of eating habits, but current
methods for food logging via self-reporting, expert observation, or
algorithmic analysis are time-consuming, expensive, or inaccurate.
PlateMate crowdsources nutritional analysis from photographs using
Amazon Mechanical Turk, automatically coordinating untrained workers
to estimate a meal's calories, fat, carbohydrates, and protein. To
make PlateMate possible, we developed the Management framework for
crowdsourcing complex tasks, which supports PlateMate's decomposition
of the nutrition analysis workflow. Two evaluations show that the
PlateMate system is nearly as accurate as a trained dietitian and
easier to use for most users than traditional self-reporting, while
remaining robust for general use across a wide variety of meal types.
Jon Noronha, Eric Hysen, Haoqi Zhang, and Krzysztof Z. Gajos. PlateMate: Crowdsourcing Nutrition Analysis from Food Photographs. In Proceedings of the 24th annual ACM symposium on User interface software and technology, UIST '11, pages 1-12, New York, NY, USA, 2011. ACM.
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