demonstration

EatAR tango: portion estimation on mobile devices with a depth sensor

Authors:

Michael Domhardt,

Simon Ginzinger,

Thomas StützAuthors Info & Claims

MobileHCI '17: Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services

Article No.: 46, Pages 1 - 7

https://doi.org/10.1145/3098279.3125434

Published: 04 September 2017 Publication History

Abstract

The accurate assessment of nutrition information is a challenging task, but crucial for people with certain diseases, such as diabetes. An important part of the assessment of nutrition information is portion estimation, i.e. volume estimation. Given the volume and the food type, the nutrition information can be computed on the basis of the food type specific nutrition density. Recently mobile devices with depth sensors have been made available for the public (Google's project tango platform). In this work, an app for mobile devices with a depth sensor is presented which assists users in portion estimation. Furthermore, we present the design of a user study for the app and preliminary results.

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Thomas Stütz, Radomir Dinic, Michael Domhardt, and Simon Ginzinger. 2014. Can mobile augmented reality systems assist in portion estimation? A user study. International Symposium on Mixed and Augmented Reality - Media, Art, Social Science, Humanities and Design (ISMAR-MASH'D), IEEE, 51-57.

Cited By

Gilal NQaraqe MSchneider JAgus M(2024)Autocleandeepfood: auto-cleaning and data balancing transfer learning for regional gastronomy food computingThe Visual Computer10.1007/s00371-024-03560-7Online publication date: 9-Jul-2024
https://doi.org/10.1007/s00371-024-03560-7
Gilal NAl-Thelaya KAl-Saeed JAbdallah MSchneider JShe JAwan JAgus M(2023)Evaluating machine learning technologies for food computing from a data set perspectiveMultimedia Tools and Applications10.1007/s11042-023-16513-483:11(32041-32068)Online publication date: 19-Sep-2023
https://doi.org/10.1007/s11042-023-16513-4
Gutiérrez FVerbert KHtun N(2018)PHARAProceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct10.1145/3236112.3236161(339-345)Online publication date: 3-Sep-2018
https://dl.acm.org/doi/10.1145/3236112.3236161
Show More Cited By

Index Terms

EatAR tango: portion estimation on mobile devices with a depth sensor

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Information & Contributors

Information

Published In

cover image ACM Conferences

MobileHCI '17: Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services

September 2017

874 pages

ISBN:9781450350754

DOI:10.1145/3098279

General Chairs:
Matt Jones
Swansea University
,
Manfred Tscheligi
University of Salzburg and AIT
,
Program Chairs:
Yvonne Rogers
University College London
,
Roderick Murray-Smith
University of Glasgow

Copyright © 2017 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 September 2017

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Österreichische Forschungsförderungsgesellschaft

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MobileHCI '17

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SIGCHI

MobileHCI '17: 19th International Conference on Human-Computer Interaction with Mobile Devices and Services

September 4 - 7, 2017

Vienna, Austria

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MobileHCI '17 Paper Acceptance Rate 45 of 224 submissions, 20%;

Overall Acceptance Rate 202 of 906 submissions, 22%

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Cited By

Gilal NQaraqe MSchneider JAgus M(2024)Autocleandeepfood: auto-cleaning and data balancing transfer learning for regional gastronomy food computingThe Visual Computer10.1007/s00371-024-03560-7Online publication date: 9-Jul-2024
https://doi.org/10.1007/s00371-024-03560-7
Gilal NAl-Thelaya KAl-Saeed JAbdallah MSchneider JShe JAwan JAgus M(2023)Evaluating machine learning technologies for food computing from a data set perspectiveMultimedia Tools and Applications10.1007/s11042-023-16513-483:11(32041-32068)Online publication date: 19-Sep-2023
https://doi.org/10.1007/s11042-023-16513-4
Gutiérrez FVerbert KHtun N(2018)PHARAProceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct10.1145/3236112.3236161(339-345)Online publication date: 3-Sep-2018
https://dl.acm.org/doi/10.1145/3236112.3236161
Dinic RStutz T(2017)EatAR Tango: Results on the Accuracy of Portion Estimation2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct)10.1109/ISMAR-Adjunct.2017.90(284-287)Online publication date: Oct-2017
https://doi.org/10.1109/ISMAR-Adjunct.2017.90

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