Association for Computing Machinery (ACM)

Platemate

Jon Noronha ¹

Eric Hysen ²

Haoqi Zhang ³

Krzysztof Gajos ³

Hide authors affiliations Show authors affiliations: 3 affiliations

Harvard University & Microsoft, Cambridge, MA, USA |

Harvard University & Google, Cambridge, MA, USA |

Harvard University, Cambridge, MA, USA |

Publication type: Proceedings Article

Publication date: 2011-10-16

Association for Computing Machinery (ACM)

DOI: 10.1145/2047196.2047198

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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.

Found

1 citation

Kostakos Vassilis

277 publications, 6 639 citations, 1 review

h-index: 42

University of Melbourne

Research interests

Human-Computer Interaction

Top-30

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Association for Computing Machinery (ACM)