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. We present 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.