skip to main content
research-article

Supporting Healthy Grocery Shopping via Mobile Augmented Reality

Published:21 October 2015Publication History
Skip Abstract Section

Abstract

Augmented reality (AR) applications have recently become popular on modern smartphones. We explore the effectiveness of this mobile AR technology in the context of grocery shopping, in particular as a means to assist shoppers in making healthier decisions as they decide which grocery products to buy. We construct an AR-assisted mobile grocery-shopping application that makes real-time, customized recommendations of healthy products to users and also highlights products to avoid for various types of health concerns, such as allergies to milk or nut products, low-sodium or low-fat diets, and general caloric intake. We have implemented a prototype of this AR-assisted mobile grocery shopping application and evaluated its effectiveness in grocery store aisles. Our application's evaluation with typical grocery shoppers demonstrates that AR overlay tagging of products reduces the search time to find healthy food items, and that coloring the tags helps to improve the user's ability to quickly and easily identify recommended products, as well as products to avoid. We have evaluated our application's functionality by analyzing the data we collected from 15 in-person actual grocery-shopping subjects and 104 online application survey participants.

References

  1. G. Agapito, B. Calabrese, I. Care, D. Falcone, P. H. Guzzi, N. Ielpo, T. Lamprinoudi, M. Milano, M. Simeoni, and M. Cannataro. 2014. Profiling basic health information of tourists: Towards a recommendation system for the adaptive delivery of medical certified nutrition contents. In Proceedings of the 2014 International Conference on High Performance Computing Simulation (HPCS). 616--620. DOI:http://dx.doi.org/10.1109/HPCSim.2014.6903744Google ScholarGoogle ScholarCross RefCross Ref
  2. Junho Ahn, Mike Gartrell, James Williamson, and Richard Han. 2012. ARFusion: An indoor mobile augmented reality system supported by pedometry and context-awareness, an AR-assisted grocery shopping application. Institutional Review Board on the University of Colorado at Boulder 12-0102.Google ScholarGoogle Scholar
  3. Junho Ahn and Richard Han. 2011. RescueMe: An indoor mobile augmented-reality evacuation system by personalized pedometry. In Proceedings of the IEEE Asia-Pacific Services Computing Conference (APSCC).Google ScholarGoogle ScholarCross RefCross Ref
  4. Eileen Anderson, Richard Wientt, Janet Wojcik, Sheila Wientt, and Todd Bowden. 2001. A computerized social cognitive intervention for nutrition behavior: Direct and mediated effects on fat, fiber, fruits, and vegetables, self-efficacy, and outcome expectations among food shoppers. Ann. Behav. Med. 23, 2, 88--100.Google ScholarGoogle ScholarCross RefCross Ref
  5. Amy Barton, Lynn Gilbert, Julaluk Baramee, and Theresa Granger. 2006. Cardiovascular risk in hispanic and non-hispanic preschoolers. National Institute of Health (May-June 2006).Google ScholarGoogle Scholar
  6. Stephane Beauregard. 2007. Omnidirectional pedestrian navigation for first responders. In Proceedings of the 4th Workshop on Positioning, Navigation and Communication (WPNC'07).Google ScholarGoogle ScholarCross RefCross Ref
  7. J. Bobadilla, F. Ortega, A. Hernando, and A. GutiéRrez. 2013. Recommender systems survey. Know.-Based Syst. 46 (2013), 109--132. DOI:http://dx.doi.org/10.1016/j.knosys.2013.03.012 Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Cristina Botella, Juani Breton-Lopez, Soledad Quero, Rosa Banos, Azucena Garcia-Palacios, Irene Zaragoza, and Alcaniz Raya. 2011. Treating cockroach phobia using a serious game on a mobile phone and augmented reality exposure: A single case study. Comput. Hum. Behav. 27, 1, 217--227. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. DanKam. 2010. DanKam AR application for color blindness. http://news.cnet.com/8301-27080_3-20026054-245.html.Google ScholarGoogle Scholar
  10. DietGuideline 2010. U.S. Department of Agriculture. 2010 Dietary guidelines for Americans. http://www.cnpp.usda.gov/DGAs2010-PolicyDocument.htm. (2010).Google ScholarGoogle Scholar
  11. Jill Freyne and Shlomo Berkovsky. 2010. Intelligent food planning: Personalized recipe recommendation. In Proceedings of the 15th International Conference on Intelligent User Interfaces (IUI'10). 321--324. DOI:http://dx.doi.org/10.1145/1719970.1720021 Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Christoph Fuchs, Nils Aschenbruck, Peter Martini, and Monika Wieneke. 2011. A survey on indoor tracking for mission critical scenarios. Perva. Mobile Comput. 7, 1. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Subhashini Ganapathy, Glen Anderson, and Igor Kozintsev. 2011. MAR shopping assistant usage: Delay, error, and utility. In Proceedings of the IEEE Virtual Reality Conference (VR). 207--208. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Golfscape. 2014. Golfscape GPS AR range finder. http://golfscapeapp.com/.Google ScholarGoogle Scholar
  15. GoogleDocs 2014. Google Docs' survey tool. http://support.google.com/docs/bin/answer.py?hl=en&answer=87809.Google ScholarGoogle Scholar
  16. Levent Görgü, Abraham Campbell, Kealan McCusker, Mauro Dragone, Michael O'Grady, Noel O'Connor, and Greg O'Hare. 2010. FreeGaming: Mobile, collaborative, adaptive and augmented exergaming. In Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia (MoMM'10). 173--179. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Yanying Gu, Anthony Lo, and Ignas Niemegeers. 2009. A survey of indoor positioning systems for wireless personal networks. IEEE Commun. Surv. Tutori. 11, 1, 13--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Ramón Hervás, Alberto Garcia-Lillo, and José Bravo. 2011. Mobile augmented reality based on the semantic web applied to ambient assisted living. In Ambient Assisted Living, Jos Bravo, Ramn Hervs, and Vladimir Villarreal (Eds.), Lecture Notes in Computer Science, vol. 6693, Springer Berlin / Heidelberg, 17--24.Google ScholarGoogle Scholar
  19. Hwajung Hong, Hee Young Jeong, Rosa I. Arriaga, and Gregory D. Abowd. 2010. TriggerHunter: Designing an educational game for families with asthmatic children. In Proceedings of CHI EA'10. 3577--3582. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. IQEngines. 2013. IQEngines: Image recognition and visual search. http://www.iqengines.com.Google ScholarGoogle Scholar
  21. Vaiva Kalnikaite, Yvonne Rogers, Jon Bird, Nicolas Villar, Khaled Bachour, Stephen Payne, Peter Todd, Johannes Schoning, Antonio Kruger, and Stefan Kreitmayer. 2011. How to nudge in Situ: Designing Lambent devices to deliver salience information in supermarkets. In Proceedings of UbiComp'11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. David Katz, Valentine Njike, Zubaida Faridi, Lauren Rhee, Rebecca Reeves, and David Jenkins. 2009. The stratification of foods on the basis of overall nutritional quality: The overall nutritional quality index. Amer. J. Health Promot. (Nov.--Dec. 2009).Google ScholarGoogle Scholar
  23. Stefan Ladstaetter, Patrick Luley, Alexander Almer, and Lucas Paletta. 2010. Multisensor data fusion for high accuracy positioning on mobile phones. In Proceedings of MobileHCI. 395--396. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Jia-Kuan Lin, Po-Hsun Cheng, Yen Su, et al. 2011. Augmented reality serious game framework for rehabilitation with personal health records. In Proceedings of the 13th IEEE International Conference on e-Health Networking Applications and Services (Healthcom). IEEE, 197--200.Google ScholarGoogle Scholar
  25. T. Lobstein and S. Davies. 2009. Defining and labeling ‘healthy’ and ‘unhealthy’ food. Public Health Nutr. (2009).Google ScholarGoogle Scholar
  26. Jennifer Mankoff, Gary Hsieh, Ho Chak Hung, Sharon Lee, and Elizabeth Nitao. 2002. Using low-cost sensing to support nutritional awareness. In Proceedings of the 4th International Conference on Ubiquitous Computing. Springer-Verlag, 371--376. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. MechanicalTurk 2014. Amazon Mechanical Turk Survey Services. https://www.mturk.com/mturk/.Google ScholarGoogle Scholar
  28. Cliona Ni Mhurchu, Tony Blakely, Joanne Wall, Anthony Rodgers, Yannan Jiang, and Jenny Wilton. 2007. Strategies to promote healthier food purchases: A pilot supermarket intervention study. Public Health Nut. 10, 06, 608--615.Google ScholarGoogle ScholarCross RefCross Ref
  29. Barry Mulrooney, Mairéad McDermott, and Nick Earley. 2006. NutraStick: Portable diet assistant. In CHI'06 extended abstracts on Human factors in computing systems (CHI EA'06). ACM, New York, 1855--1860. DOI:http://dx.doi.org/10.1145/1125451.1125802 Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Yoosoo Oh, Ahyoung Choi, and Woontack Woo. 2010. u-BabSang: A context-aware food recommendation system. J. Supercomput. 54, 1, 61--81. DOI:http://dx.doi.org/10.1007/s11227-009-0314-5 Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. PCAST. 2010. Realizing the full potential of health information technology to improve healthcare for americans: The path forward. http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-health-it-report.pdf. (2010).Google ScholarGoogle Scholar
  32. M. Phanich, P. Pholkul, and S. Phimoltares. 2010. Food recommendation system using clustering analysis for diabetic patients. In Proceedings of the International Conference on Information Science and Applications (ICISA). 1--8. DOI:http://dx.doi.org/10.1109/ICISA.2010.5480416Google ScholarGoogle ScholarCross RefCross Ref
  33. QUIS 1987. Questionnaire for user interaction satisfaction. http://lap.umd.edu/quis/about.html.Google ScholarGoogle Scholar
  34. SkyMap. 2011. Google Sky Map AR astronomy application. http://www.google.com/mobile/skymap/.Google ScholarGoogle Scholar
  35. Richard Wientt, Eileen Smith Anderson-Bill, Patricia G. Bickley, Janet Walberg-Rankin, John F. Moore, and Michael Leahy. 1997. Nutrition for a Lifetime System ©: A multimedia system for altering food supermarket shoppers' purchases to meet nutritional guidelines. Comput. Hum. Behav. 13, 3, 371--392. DOI:http://dx.doi.org/DOI: 10.1016/S0747-5632(97)00015-0Google ScholarGoogle ScholarCross RefCross Ref
  36. WordLens. 2014. WordLens augmented reality language translation app for the iPhone. http://www.questvisual.com/.Google ScholarGoogle Scholar

Index Terms

  1. Supporting Healthy Grocery Shopping via Mobile Augmented Reality

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 12, Issue 1s
      Special Issue on Smartphone-Based Interactive Technologies, Systems, and Applications and Special Issue on Extended Best Papers from ACM Multimedia 2014
      October 2015
      317 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/2837676
      Issue’s Table of Contents

      Copyright © 2015 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 21 October 2015
      • Accepted: 1 June 2015
      • Revised: 1 April 2015
      • Received: 1 January 2015
      Published in tomm Volume 12, Issue 1s

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!