@inproceedings{5b9049de6e8a40ddbb504123fc181b2d,
title = "EatAR tango: Portion estimation on mobile devices with a depth sensor: MobileHCI '17: Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services",
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.",
keywords = "Augmented reality, Mobile, Portion estimation, Nutrition, Depth sensors, Nutrition informations, User study, Volume estimations, Human computer interaction",
author = "R. Dinic and M. Domhardt and S. Ginzinger and T. St{\"u}tz",
note = "Conference code: 130275 Cited By :3 Export Date: 14 December 2023 References: Aizawa, K., Maruyama, Y., Li, H., Morikawa, C., Food balance estimation by using personal dietary tendencies in a multimedia food log (2013) IEEE Transactions On Multimedia, 15 (8), pp. 2176-2185; Anthimopoulos, M., Dehais, J., Diem, P., Mougiakakou, S., Segmentation and recognition of multi-food meal images for carbohydrate counting: Bioinformatics and Bioengineering (BIBE) (2013) 2013 IEEE 13th International Conference On; Bangor, A., Kortum, P.T., Miller, J.T., An empirical evaluation of the system usability scale (2008) International Journal of Human-Computer Interaction, 24 (6), pp. 574-594. , http://doi.org/10.1080/10447310802205776; Jo Boushey, C., Harray, A.J., Anne Kerr, D., How willing are adolescents to record their dietary intake? the mobile food record (2015) JMIR MHealth and UHealth, 3 (2), p. e47. , http://doi.org/10.2196/mhealth.4087; Brooke, J., SUS-A quick and dirty usability scale (1996) Usability Evaluation in Industry; Chae, J., Woo, I., Kim, S., Volume estimation using food specific shape templates in mobile image-based dietary assessment (2011) Proceedings of SPIE, 7873, p. 78730K; Dehais, J., Shevchik, S., Diem, P., Mougiakakou, S.G., Food volume computation for self dietary assessment applications: Bioinformatics and Bioengineering (BIBE) (2013) 2013 IEEE 13th International Conference On; Dehais, J., Anthimopoulos, M., Shevchik, S., Mougiakakou, S., Two-view 3d reconstruction for food volume estimation (2017) IEEE Transactions On Multimedia, 19 (5), pp. 1090-1099. , http://doi.org/10.1109/TMM.2016.2642792; Domhardt, M., Tiefengrabner, M., Dinic, R., Training of carbohydrate estimation for people with diabetes using mobile augmented reality (2015) Journal of Diabetes Science and Technology, 9 (3), pp. 516-524. , http://doi.org/10.1177/1932296815578880; He, Y., Xu, C., Khanna, N., Boushey, C.J., Delp, E.J., Context based food image analysis (2013) Image Processing (ICIP), 2013 20th IEEE International Conference On, pp. 2748-2752. , http://doi.org/10.1109/ICIP.2013.6738566; Myers, A., Johnston, N., Rathod, V., Im2Calories: Towards an automated mobile vision food diary (2015) ICCV; Rhyner, D., Loher, H., Dehais, J., Carbohydrate estimation by a mobile phone-based system versus self-estimations of individuals with type 1 diabetes mellitus: A comparative study (2016) Journal of Medical Internet Research, 18 (5), p. e101. , http://doi.org/10.2196/jmir.5567; Schap, T.E., Six, B.L., Delp, E.J., Ebert, D.S., Kerr, D.A., Boushey, C.J., Adolescents in the United States can identify familiar foods at the time of consumption and when prompted with an image 14 h postprandial, but poorly estimate portions (2011) Public Health Nutrition, 14 (7), pp. 1184-1191; Schoeller, D.A., Bandini, L.G., Dietz, W.H., Inaccuracies in self-reported intake identified by comparison with the doubly labelled water method (1990) Canadian Journal of Physiology and Pharmacology, 68 (7), pp. 941-949; Six, B.L., Schap, T.E., Kerr, D.A., Boushey, C.J., Evaluation of the Food and Nutrient Database for Dietary Studies for use with a mobile telephone food record (2011) Journal of Food Composition and Analysis: An Official Publication of the United Nations University, International Network of Food Data Systems, 24 (8), pp. 1160-1167; St{\"u}tz, T., Dinic, R., Domhardt, M., Ginzinger, S., Can mobile augmented reality systems assist in portion estimation? (2014) A User Study. International Symposium On Mixed and Augmented Reality-Media, Art, Social Science, Humanities and Design (ISMAR-mash'D), IEEE, pp. 51-57. , http://doi.org/10.1109/ISMAR-AMH.2014.6935438; 19th International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2017, MobileHCI 2017 ; Conference date: 04-09-2017 Through 07-09-2017",
year = "2017",
doi = "10.1145/3098279.3125434",
language = "English",
isbn = "978-1-4503-5075-4",
booktitle = "MobileHCI '17: Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services",
publisher = "Association for Computing Machinery",
address = "United States",
url = "https://mobilehci.acm.org/2017/",
}
SDG 3 Good Health and Well-being