Cited by 355Mobile Health and mHealth ApplicationsInnovation in Digital Healthcare SystemsNutrition, Health and Food Behavior

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The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation

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Fengqing Zhu, Bosch, Marc, Insoo Woo, SungYe Kim, Boushey, Carol J, Ebert, David S, Delp, Edward J·2010·IEEE Journal of Selected Topics in Signal Processing

DOI 10.1109/jstsp.2010.2051471PMC PMC2941896

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Abstract

There is a growing concern about chronic diseases and other health problems related to diet including obesity and cancer. The need to accurately measure diet (what foods a person consumes) becomes imperative. Dietary intake provides valuable insights for mounting intervention programs for prevention of chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper, we describe a novel mobile telephone food record that will provide an accurate account of daily food and nutrient intake. Our approach includes the use of image analysis tools for identification and quantification of food that is consumed at a meal. Images obtained before and after foods are eaten are used to estimate the amount and type of food consumed. The mobile device provides a unique vehicle for collecting dietary information that reduces the burden on respondents that are obtained using more classical approaches for dietary assessment. We describe our approach to image analysis that includes the segmentation of food items, features used to identify foods, a method for automatic portion estimation, and our overall system architecture for collecting the food intake information.

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Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482
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Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,330
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,616
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Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,935
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,058
Active contours without edges
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Libsvm : A library for support vector machines
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Authors: Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C. J., Ebert, D. S., & Delp, E. J.
Affiliations: Purdue University West Lafayette (US)
ORCID: Fengqing Zhu
Bosch, Marc
Boushey, Carol J
Ebert, David S
Delp, Edward J
Venue: IEEE Journal of Selected Topics in Signal Processing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2010-08
Volume: 4
Issue: 4
Pages: 756-766
ISSN: 1932-4553 (ISSN-L)
1941-0484
DOI: 10.1109/jstsp.2010.2051471
PMC: PMC2941896
OpenAlex: W2109257383
Cited by: 355
References: 61
Citations by year: 2026: 4 · 2025: 10 · 2024: 11 · 2023: 18 · 2022: 23 · 2021: 28 · 2020: 15 · 2019: 23 · 2018: 28 · 2017: 40 · 2016: 30 · 2015: 36 · 2014: 29 · 2013: 23 · 2012: 20
FWCI: 34.17
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Journal of Selected Topics in Signal Processing (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: Purdue University
National Cancer Institute (U01CA130784)
National Institute of Diabetes and Digestive and Kidney Diseases (R01DK073711)
Topics: Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
Health Sciences › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
Keywords: dietary assessment, food intake estimation, image analysis, food segmentation, food recognition, mobile health, chronic disease prevention, obesity, cancer prevention, nutrient intake

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The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation

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