An Overview of the State of the Art of Automated Capture of Dietary Intake Information

REFERENCES

• Amft, O. and Troster, G. (2009). On-body sensing solutions for automatic dietary monitoring. IEEE Pervasive Comput. 8(2):62–70.
   Web of Science ®Google Scholar
• Barreiro-Hurle, J., Gracia, A. and de-Magistris, T. (2010). Does nutrition information on food products lead to healthier food choices? Food Policy 35(3):221–29.
   Web of Science ®Google Scholar
• Blumenthal, K. and Volpp, K.G. (2010). Enhancing the effectiveness of food labelling in restaurants. J. Am. Med. Assoc. 303(6):553–554.
   Web of Science ®Google Scholar
• Bosch, M., Zhu, F., Khanna, N., Boushey, C. J. and Delp, E. J. (2011). Combining global and local features for food identification in dietary assessment. In: Image Processing (ICIP), 2011 18th IEEE International Conference on pp. 1789–1792. IEEE.
   Google Scholar
• Brinkerhoff, K., Brewster, P., Clark, E., Jordan, K., Cummins, M. and Hurdle, J. (2011). Linking supermarket sales data to nutritional information: an informatics feasibility study. AMIA Annu. Symp. Proc. 2011:598–606.
   PubMedGoogle Scholar
• Burke, L. E., Wang, J., Sevick, M. A. (2011). Self-monitoring in weight loss: a systematic review of the literature. J. Am. Diet. Assoc. 111(19):92–102.
   PubMed Web of Science ®Google Scholar
• Chae, J., Woo, I., Kim, S., Maciejewski, R., Zhu, F., Delp, E. J. and Ebert, D. S. (2011). Volume estimation using food specific shape templates in mobile image-based dietary assessment. In: Proceedings of SPIE Vol. 7873, p. 78730K. NIH Public Access.
   Google Scholar
• Chang, K.-h., Liu, S.-y., Chu, H.-h., Hsu, J. Y.-j., Chen, C., Lin, T.-y., Chen, C.-y. and Huang, P. (2006). The diet-aware dining table: observing dietary behaviors over a tabletop surface. In: Proceedings of the 4th international conference on Pervasive Computing, ser. PERVASIVE’06, pp. 366–382. Berlin, Heidelberg: Taylor & Francis.
   Google Scholar
• Chatterjee, S. and Price, A. (2009). Healthy living with persuasive technologies: framework, issues, and challenges. J. Am. Med. Inform. Assoc. 16(2):171–8.
   PubMed Web of Science ®Google Scholar
• Chi, P.-Y., Chen, J.-H., Chu, H.-H. and Lo, J.-L. (2008). Enabling calorie-aware cooking in a smart kitchen, In: Persuasive Technology, ser. Lecture Notes in Computer Science, vol. 5033, pp. 116–127. Oinas-Kukkonen H. , Hasle P. , Harjumaa M. , Segersthl K.and Hrstrm P. , Eds., Taylor & Francis, Berlin / Heidelberg.
   Google Scholar
• Deterding, S., Sicart, M., Nacke, L., O’Hara, K. and Dixon, D. (2011). Gamification: using game-design elements in non-gaming contexts. Proceedings of the CHI extended abstracts.2425–2428.
   Google Scholar
• Donnelly, K., van der Roest, J., Hoskuldsson, S. and Olsen, P. (2011). Food industry information exchange and the role of meta–data and data lists. Int. J. Metadata Semantics Ontologies, 6(2):146–153.
   Google Scholar
• Dutton, W. H. and Eynon, R. (2009). Networked individuals and institutions: a cross-sector comparative perspective on patterns and strategies in government and research, Inform. Soc. 25(3):1–11.
   Web of Science ®Google Scholar
• Finch, C. E. (2007). The Biology of Human Longevity: Inflammation, Nutrition, and Aging in the Evolution of Lifespans. Taylor & Francis, San Diego.
   Google Scholar
• Fogg, B. J. (2003). Persuasive Technology: Using Computers to Change What We Think and Do. Taylor & Francis, San Francisco.
   Google Scholar
• Food and Drug Administration, US. (2012). National drug code directory. Retrieved 16 June 2012, from http://www.fda.gov/Drugs/InformationOnDrugs/ucm142438.htm.
   Google Scholar
• Greenwood, D. C., Ransley, J. K., Gilthorpe, M. S. and Cade, J. E. (2006). Use of itemized till receipts to adjust for correlated dietary measurement error. Am. J. Epidemiol. 164(10):1012–1018.
   PubMed Web of Science ®Google Scholar
• Hawkes, C. (2012). Food policies for healthy populations and healthy economies. BMJ 344:e2801.
   PubMedGoogle Scholar
• Haytowitz, D. and Holden, J. (2012). USDA food composition and nutrient databases. Proceedings of the 36th National Nutrient Databank Conference. Retrieved 16 June 2012, from http://www.nutrientdataconf.org/PastConf/NDBC36/W-1_Haytowitz-Holden_NDDC2012.pdf.
   Google Scholar
• Illner, A. K., Freisling, H., Boeing, H., Huybrechts, I., Crispim, S. P. and Slimani, N. (2012). Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. Int. J. Epidemiol. 41(4):1187–1203.
   PubMed Web of Science ®Google Scholar
• Jarvinen, P., Jarvinen, T. H., Lahteenmaki, L., and Sodergard, C. (2008). HyperFit: hybrid media in personal nutrition and exercise management. In: Pervasive Computing Technologies for Healthcare, 2008. PervasiveHealth 2008. Second International Conference on, pp. 222–226. IEEE.
   Google Scholar
• Jordan, E. T., Ray, E. M., Johnson, P. and Evans, W. D. (2011). Text4Baby. Nursing for women's health, 15:206–212. doi:10.1111/j.1751-486X.2011.01635.x
   Google Scholar
• Karvetti, R. L. and Knuts, L. R. (1985). Validity of the 24-hour dietary recall. J. Am. Diet. Assoc. 185:1437–42.
   Google Scholar
• Kitamura, K., de Silva, C., Yamasaki, T. and Aizawa, K. (2010). Image processing based approach to food balance analysis for personal food logging. In: IEEE International Conference on Multimedia and Expo (ICME), pp 625–630. IEEE.
   Google Scholar
• Klasnja, P., Consolvo, S., McDonalid, D., Landay, J. and Pratt, W. (2009). Using mobile & personal sensing technologies to support health behavior change in everyday life: lessons learned. AMIA Annu. Symp. Proc. 2009:338–342.
   PubMedGoogle Scholar
• Kong, F. and Tan, J. (2012). DietCam: automatic dietary assessment with mobile camera phones. Pervasive Mobile Comput. 8(1):147–163.
   Web of Science ®Google Scholar
• Lassen, A. D., Poulsen, S., Ernst, L., Andersen, K. K., Biltoft-Jensen, A. and Tetens, I. (2010). Evaluation of a digital method to assess evening meal intake in a free-living adult population. FoodNutr. Res. 54.
   Google Scholar
• Lester, J., Tan, D., Patel, S. and Brush, A. (2010). Automatic classification of daily fluid intake In: Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2010 4th International Conference on, pp 1–8.
   Google Scholar
• Li, Y., Meng, X., Wang, L. and Li, Q. (2006). RecipeCrawler: collecting recipe data from WWW incrementally. In: Proc. of the 7th International Conference on Web-Age Information Management (WAIM), pp 263–274. Hong Kong, China.
   Google Scholar
• Lieffers, J. R. and Hanning, R. M. (2012). Dietary assessment and self-monitoring with nutrition applications for mobile devices. Can. J. Diet. Prac. Res. 73(3):253–260.
   PubMedGoogle Scholar
• Lindqvist, A-K., Kostenius, C. and Gard, G. (2012). Peers, parents and phones–Swedish adolescents and health promotion. Int. J. Qual. Stud. Health Well-being. 2012:7 10.3402/qhw.v7i0.17726.
   Web of Science ®Google Scholar
• Lopez-Meyer, P., Schuckers, S., Makeyev, O., Fontana, J. M. and Sazonov, E. (2012). Automatic identification of the number of food items in a meal using clustering techniques based on the monitoring of swallowing and chewing. Biomed. Signal Process. Control 7(5):474–480.
   PubMed Web of Science ®Google Scholar
• Mankoff, J., Hsieh, G., Hung, H.C., Lee, S. and Nitao, E. (2002). Using low-cost sensing to support nutritional awareness. In: Goos G., Hartmanis J., van Leeuwen J., eds. Ubicomp 2002: Proceedings of the 4th International Conference on Ubiquitous Computing, vol. 2498 of Lecture Notes in Computer Science, pp 371–6. Springer, Berlin, Heidelberg.
   Google Scholar
• Martin, C., Kaya, S. and Gunturk, B. (2009a). Quantification of food intake using food image analysis, Conf. Proc. IEEE Eng. Med. Biol. Soc. 6869–6872.
   PubMedGoogle Scholar
• Martin, C., Han, H., Coulon, S., Allen, H., Champagne, C. and Anton, S. (2009b). A novel method to remotely measure food intake of free-living individuals in real time: the remote food photography method. Brit. J. Nutr. 101(03):446–456.
   PubMed Web of Science ®Google Scholar
• Maruthur, N. M., Wang, N. Y., and Appel, L. J. (2009). Lifestyle interventions reduce coronary heart disease risk: results from the PREMIER trial. Circulation 119(15):2026–31.
   PubMed Web of Science ®Google Scholar
• Mutchler, L., Shim, J. and Ormond, D. (2011). Exploratory study on users’ behavior: smartphone usage. Proceedings of AMCIS 2011. pp. 10. AIS Electronic Library (AISeL).
   Google Scholar
• Nestle, M. (2010). Health care reform in action – calorie labeling goes national. New Engl. J. Med. 362:2343–2345.
   PubMed Web of Science ®Google Scholar
• National Institutes of Health. (2012). Unique Ingredient Identifier (UNII). Retrieved 16 June 2012, from http://fdasis.nlm.nih.gov/srs/srs.jsp.
   Google Scholar
• Ng, S.W. (2012). Addressing gaps in market level databases, Proceedings of the 36th National Nutrient Databank Conference, 2012. Retrieved 16 June 2012, from http://www.nutrientdataconf.org/PastConf/NDBC36/5-3_Ng_NNDC2012.pdf
   Google Scholar
• Ngo, J., Engelen, A., Molag, M., Roesle, J., García-Segovia, P. and Serra-Majem, L. (2009). A review of the use of information and communication technologies for dietary assessment. Br. J. Nutr. 101(Suppl 2):S102–12.
   PubMed Web of Science ®Google Scholar
• Noronha, J., Hysen, E., Zhang, H. and Gajos, K. Z. (2011). Platemate: crowdsourcing nutritional analysis from food photographs. In: Proc. UIST 2011, ACM pp 1–12.
   Google Scholar
• Paldanius, M., Lehtiö, A., Karukka, M. and Huuskonen, P. (2011). Personal chart: health data logging made easy with a mobile device. Hum.-Comput. Interact.–INTERACT 2011:592–595.
   Google Scholar
• Passler, S. and Fischer, W.-J. (2011). Acoustical method for objective food intake monitoring using a wearable sensor system, In: Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2011 5th International Conference on, may 2011, pp 266–269.
   Google Scholar
• Patterson, D. J., Fox, D., Kautz, H. and Philipose, M. (2005). Fine-grained activity recognition by aggregating abstract object usage Proc. 9th IEEE Int’l Symp. Wearable Computers, pp 44–51.
   Google Scholar
• Philipose, M., Fishkin, K. P., Perkowitz, M., Patterson, D., Fox, D., Kautz, H. and Hahnel, D. (2004). Inferring activities from interactions with objects. IEEE Pervasive Comput. 3(4):50–57.
   Web of Science ®Google Scholar
• Saeki, Y. and Takeda, F. (2005). Proposal of food intake measuring system in medical use and its discussion of practical capability, LNCS 3683:181–183.
   Google Scholar
• Shang, J., Sundara-Rajan, K., Lindsey, L., Mamishev, A., Johnson, E., Teredesai, A. and Kristal, A. (2011). A pervasive dietary data recording system. In: Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on, pp. 307–309. IEEE.
   Google Scholar
• Shay, L. E., Seibert, D., Watts, D., Sbrocco, T., and Pagliara, C. (2009). Adherence and weight loss outcomes associated with food-exercise diary preference in a military weight management program. Eat. Behav. 10(4):220–227.
   PubMed Web of Science ®Google Scholar
• Shils, M. E., Shike, M., Ross, A. C., Caballero, B., and Cousins, R. J., (ed.) (2006). Modern Nutrition in Health and Disease. 10th ed, p 982. Philadelphia, PA: Taylor & Francis.
   Google Scholar
• Silva, B. M., Lopes, I. M., Rodrigues, J. J. and Ray, P. (2011). SapoFitness: A mobile health application for dietary evaluation. In: e-Health Networking Applications and Services (Healthcom), 2011 13th IEEE International Conference on, pp. 375–380. IEEE.
   Google Scholar
• Six, B., Schap, T., Zhu, F., Mariappan, A., Bosch, M., Delp, E., Ebert, D., Kerr, D. and Boushey, C. (2010). Evidence-based development of a mobile telephone food record. J. Am. Diet. Assoc.110(1):74–79.
   PubMed Web of Science ®Google Scholar
• Slining, M. (2012). Linking data sources. In: Proceedings of the 36th National Nutrient Databank Conference. Retrieved 16 June 2012, from http://www.nutrientdataconf.org/PastConf/NDBC36/5-6_Slining_NNDC2012.pdf.
   Google Scholar
• Steele, R. and Clarke, A. (2012). A real-time, composite healthy building measurement architecture drawing upon occupant smartphone-collected data. In: Proceedings of the 10th International Healthy Buildings Conference.
   Google Scholar
• Steele, R., Lo, A., Secombe, C., and Wong, Y. K. (2009). Elderly persons’ perception and acceptance of using wireless sensor networks to assist healthcare. Int. J. Med. Informat. 78(12):788–801.
   PubMed Web of Science ®Google Scholar
• Steele, R., Min, K. and Lo, A. (2012). Personal health record architectures: technology infrastructure implications and dependencies. J. Am. Soc. Inform. Sci. Technol. 63(6):1079–1091.
   Web of Science ®Google Scholar
• Stevens, J., Bryant, M., Wang, L., Borja, J. and Bentley, M. E. (2010). Exhaustive measurement of food items in the home using a universal product code scanner. Public Health Nutr. 14(2):314.
   PubMed Web of Science ®Google Scholar
• Strauss, J. and Thomas, D. (1998). Health, nutrition and economic development. J. Econ. Lit. 36(6):766–817.
   Google Scholar
• Sturm, R. (2002). The effects of obesity, smoking and drinking on medical problems and costs. Health Aff. 21(2):245–253.
   PubMed Web of Science ®Google Scholar
• Sun, M., Fernstrom, J. D., Jia, W., Hackworth, S. A., Yao, N., Li, Y. and Sclabassi, R. J. (2010). A wearable electronic system for objective dietary assessment. J. Am. Diet. Assoc. 110(1):45.
   PubMed Web of Science ®Google Scholar
• Sun, M., Liu, Q., Schmidt, K., Yang, J., Yao, N., Fernstrom, J., Fernstrom, M., DeLany, J. and Sclabassi, R. (2008). Determination of food portion size by image processing, 30th Annual International IEEE EMBS Conference, pp 871–874.
   Google Scholar
• Swan, M. (2012). Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0. J. SensorActuator Netw. 1(3):217–253.
   Google Scholar
• Villalobos, G., Almaghrabi, R., Hariri, B. and Shirmohammadi, S. (2011). A personal assistive system for nutrient intake monitoring In: Proceedings of the 2011 international ACM workshop on Ubiquitous meta user interfaces, ser. Ubi-MUI ‘11, pp 17–22. New York, NY, USA
   Google Scholar
• Weinstein, J. L., Phillips, V., MacLeod, E., Arsenault, M. and Ferris, A. M. (2006). A universal product code scanner is a feasible method of measuring household food inventory and food use patterns in low-income families. J. Am. Diet. Assoc. 106(3):443–445.
   PubMed Web of Science ®Google Scholar
• WHO. (2003). Diet, Nutrition and the Prevention of Chronic Diseases. Report of a Joint WHO/FAO Expert Consultation. WHO Technical Report Series 916. World Health Organisation, Geneva
   Google Scholar
• Yatani, K. and Truong, K. (2012). BodyScope: a wearable acoustic sensor for activity recognition. In: Proceedings of UbiComp.
   Google Scholar
• Yue, Y., Jia, W., Fernstrom, J. D., Sclabassi, R. J., Fernstrom, M. H., Yao, N. and Sun, M. (2010). Food volume estimation using a circular reference in image-based dietary studies. In: Bioengineering Conference, Proceedings of the 2010 IEEE 36th Annual Northeast, pp. 1–2. IEEE.
   Google Scholar
• Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C. J., Ebert, D. S. and Delp, E. J. (2010). The use of mobile devices in aiding dietary assessment and evaluation. Sel. Top. Signal Process. IEEE J. 4(4):756–766.
   PubMed Web of Science ®Google Scholar
• Zhu, F., Mariappan, A., Kerr, D., Boushey, C., Lutes, K., Ebert, D. and Delp, E. (2008). Technology-assisted dietary assessment. In: Proceedings of the IS&T/SPIE Conference on Computational Imaging VI, vol. 6814.
   Google Scholar