PlateMate: Crowdsourcing Nutrition Analysis from Food Photographs

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PlateMate: Crowdsourcing Nutrition Analysis from Food Photographs

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Title: PlateMate: Crowdsourcing Nutrition Analysis from Food Photographs
Author: Noronha, Jon; Hysen, Eric; Zhang, Haoqi; Gajos, Krzysztof Z.

Note: Order does not necessarily reflect citation order of authors.

Citation: Noronha, Jon, Eric Hysen, Haoqi Zhang, and Krzysztof Z. Gajos. 2011. PlateMate: Crowdsourcing nutrition analysis from food photographs. In UIST '11 Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology: October 16-19, 2011, Santa Barbara, CA, 1-12. New York, NY: Association for Computing Machinery.
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Abstract: We introduce PlateMate, a system that allows users to take photos of their meals and receive estimates of food intake and composition. Accurate awareness of this information can help people monitor their progress towards dieting goals, 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 nutrition analysis workflow. Results of our evaluations show that PlateMate is nearly as accurate as a trained dietitian and easier to use for most users than traditional self-reporting.
Published Version: doi:10.1145/2047196.2047198
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:8720824

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  • FAS Scholarly Articles [7374]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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