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

The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.

The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation

Open access

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

Open this paper in CoChat — free account

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1109/jstsp.2010.2051471

Cites

61 of 61 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Pattern Classification
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482
Snakes: Active contour models
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
Normalized cuts and image segmentation
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
Chan, T. F.-C., & Vese, L. A. (2001) · IEEE Transactions on Image Processing · doi:10.1109/83.902291 · Cited by 10,279
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123
Geodesic Active Contours
Caselles, V., Kimmel, R., & Sapiro, G. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007979827043 · Cited by 5,285
Pattern Classification
Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001) · Advanced textbooks in control and signal processing · doi:10.1007/978-1-4471-0347-9_3 · Cited by 5,053

Flagged first, then by citations

Show all 61 references

Research works printed but not linked: 2

kim, 0, Interactive and image-based dietary assessment system using mobile devices
2004, Usda Food and Nutrient Database for Dietary Studies 3 0

Sorted by citations

Items 301–355 of 355

Optimized Food Recognition System for Diabetic Patients
Anusha, B., Sabena, S., & SaiRamesh, L. (2018) · Communications in computer and information science · doi:10.1007/978-981-10-8657-1_38 · Cited by 2
A Novel Approach to Calculating Energy Density from Food Images Reduces Analysis Time and Cost
Pan, Z., Marden, T. J., Mande, A., & HIGGINS, J. A. (2019) · Food and Nutrition Sciences · doi:10.4236/fns.2019.102018 · Cited by 3
A multi-device framework to increase self-awareness for health and disease prevention
Metola, E., Bernardos, A. M., & Casar, J. R. (2013) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2013.6720649 · Cited by 2
Portable Sensors for Breath Analysis
Prabhakar, A. (2013) · Cited by 2
Ambient Assisted Living—From Technology to Intervention
García, N. M., Sousa, P., & Felizardo, V. (2015) · Rehabilitation science in practice series · doi:10.1201/b18520-2 · Cited by 2
Distinguishing Nigerian Food Items and Calorie Content with Hyperspectral Imaging
Wang, X., Rohani, N., Manerikar, A. J., Katsagellos, A., Cossairt, O., & Alshurafa, N. I. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70742-6_45 · Cited by 2
Using Hidden Markov Models to Segment and Classify Wrist Motions Related to Eating Activities
García, R., & Ignacio, R. (2014) · Cited by 2
Investigation of Methodologies of Food Volume Estimation and Dataset for Image-Based Dietary Assessment
Kadam, P., Petkar, N., & Phansalkar, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-0882-7_43 · Cited by 2
Using Location Lifelogs to Make Meaning of Food and Physical Activity Behaviors
Andrew, A. H., Eustice, K., & Hickl, A. (2013) · Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques · doi:10.4108/pervasivehealth.2013.252134 · Cited by 2
Automated Dietary Monitoring System: A Novel Framework
Mamud, S., Chatterjee, P., Bandyopadhyay, S., & Bhandari, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-8289-9_52 · Cited by 2
An ensemble feature method for food classification
Martinel, N., Micheloni, C., & Piciarelli, C. (2017) · Machine Graphics and Vision · doi:10.22630/mgv.2017.26.1.2 · Cited by 2
Does the use of mobile applications or mobile health technology improve diet quality in adults? A protocol for a systematic literature review
Scarry, A., Rice, J., Connor, E. M. O., & Tierney, A. C. (2022) · HRB Open Research · doi:10.12688/hrbopenres.13472.1 · Cited by 2
A study of time-based features and regularity of manipulation to improve the detection of eating activity periods during free living
Reyes, J. J. (2014) · TigerPrints (Clemson University) · Cited by 2
Impact of dietary practices and physical activity on obesity and overweight in Omani adults and effectiveness of lifestyle interventions using smart phone applications
Zuhaibi, K. A. (2018) · Nottingham ePrints (University of Nottingham) · Cited by 1
An Intelligent Passive Food Intake Assessment System with Egocentric CamerasPreprint
Lo, F. P.-W., Jobarteh, M. L., Sun, Y., Qiu, J., Jiang, S., Frost, G. S., & Lo, B. P. L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.03142 · Cited by 2
Improving Diet Quality of People Living With Obesity by Building Effective Dietetic Service Delivery Using Technology in a Primary Health Care Setting: Protocol for a Randomized Controlled Trial
Kerr, D. A., Collins, C. E., Begley, A. M., Mullan, B. A., Dhaliwal, S. S., Pulker, C. E., Zhu, F. M., Fialkowski, M. K., Prince, R. L., Norman, R., et al. (2025) · JMIR Research Protocols · doi:10.2196/64735 · Cited by 2
Self-citation
Using Stochastic Optimization to Improve the Detection of Small Checkerboards
Hassannejad, H., Matrella, G., Mordonini, M., & Cagnoni, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24309-2_6 · Cited by 1
A Printer Indexing System for Color Calibration with Applications in Dietary Assessment
Fang, S., Liu, C., Zhu, F. M., Boushey, C. J., & Delp, E. J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23222-5_44 · Cited by 1
Self-citation
Image-based Dietary Assessment System for Chinese Children
Xu, P., Chen, D., Liu, X., & Loo, J. K. K. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622579 · Cited by 1
Analysis of mobile applications reporting on nutritional recipes: a review of the scientific literature
Huamaní-Cahuana, J., & Cabanillas-Carbonell, M. (2021) · E3S Web of Conferences · doi:10.1051/e3sconf/202122901060 · Cited by 1
On the Design of Voice-based Conversational Agents for Prevention and Management of Obesity and Overweight Condition Adolescents
Levitikos, M., Giambattista, A., & Chatzigiannakis, I. (2022) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops53856.2022.9767336 · Cited by 1
Sensor Datasets for Human Daily Safety and Well-Being
Cauli, N., Massa, S. M., Recupero, D. R., & Riboni, D. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-38281-9_1 · Cited by 1
Developing the Design Reasoning in Data Life-Cycle Ethical Management Framework
Purzer, Ş., Zoltowski, C., Zakharov, W., & Arigye, J. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--47172 · Cited by 1
Cooking Method Detection Based on Electronic Nose in Smart Kitchen
Gao, Y., Hu, Y., & Lu, Q. (2023) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2023.20053 · Cited by 1
Learning to Rank Food Images
Allegra, D., Erba, D., Farinella, G. M., Grazioso, G., Maci, P. D., Stanco, F., & Tomaselli, V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30645-8_57 · Cited by 1
Learning Eating Environments Through Scene Clustering
Yarlagadda, S. K., Baireddy, S., Güera, D., Boushey, C. J., Kerr, D. A., & Zhu, F. M. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054402 · Cited by 1
Self-citation
Machine Learning Approach to Track Malnutrition in Children with Rural Background
Nagini, S., Nalluri, S., Reddy, B. R., & Lekha, A. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-33-4046-6_30 · Cited by 1
Evaluating a Novel Device for Calorie Reduction: The Bite Counter Study
Mayette, G. (2016) · doi:10.23860/thesis-mayette-gregory-2016 · Cited by 1
Evaluation Of Issues, Usability And Functionality Of Dietary Related Mobile Applications: A Systematic Literature Review.Preprint
Tahir, G. A., Loo, C. K., Moy, F. M., & Kong, N. (2020) · arXiv (Cornell University) · Cited by 1
A Review of Critical Features and General Issues of Freely Available mHealth Apps For Dietary AssessmentPreprint
Tahir, G. A., Loo, C. K., Moy, F. M., & Kong, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.09883 · Cited by 1
Adaptive biofeedback with signal processing and biosensors in mobile health
Krishnan, R. (2012)
Integrated Sensors and Software for Dietary Intake Assessment
Shang, J. (2012) · ResearchWorks at the University of Washington (University of Washington)
Dietary assessment: developing, validating, and improving a 24-hour Web food report questionnaire
DESAI, J. R. (2013) · University of Minnesota Digital Conservancy (University of Minnesota)
An Assessment and Analysis of Dietary Practices of Irish Jockeys
O’Loughlin, G. (2014) · Arrow@dit (Dublin Institute of Technology)
Conference on 'Translating nutrition: integrating research, practice and policy' Symposium 1: Innovation in diet and lifestyle assessment New technology in dietary assessment: a review of digital methods in improving food record accuracy
Stumbo, P. J. (2013)
Integrating cooperative users to develop framework for an effective mobile physiotherapy
Benferdia, Y. (2015)
Subject-Coded Versus Investigator-Coded Diet Analysis Among Overweight Individuals: A Comparison of Methods
Schohl, B. B. (2012) · Arizona State University Library Digital Repository (Arizona State University)
Novel Texture-based Probabilistic Object Recognition and Tracking Techniques for Food Intake Analysis and Traffic Monitoring
DiBiano, R. (2015) · doi:10.31390/gradschool_dissertations.1218
Identification System For Calculating Carbohydrates By Image Classification Methods
Srilekha, G., Prasad, L. V. N., & Rani, D. S. (2018) · International Computer Science and Engineering Conference · doi:10.1109/icsec.2018.8712800
Learning eating environments through scene clusteringPreprint
Yarlagadda, S. K., Baireddy, S., Güera, D., Boushey, C. J., Kerr, D. A., & Zhu, F. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.11367
Self-citation
Estimating Food Volume in a Bowl Based on Geometric Image Features
Li, B. (2021) · D-Scholarship@Pitt (University of Pittsburgh)
Vision-Based Food Analysis for Automatic Dietary Assessment.Preprint
Wang, W., Min, W., Li, T., Dong, X., Li, H., & Jiang, S. (2021) · arXiv (Cornell University)
Development of body composition and its relationship with physical activity in healthy Swedish children : A longitudinal study until 4.5 years of age including evaluation of methods to assess physical activity and energy intake
Henriksson, H. (2015) · Linköping University medical dissertations · doi:10.3384/diss.diva-117420
Bibliography
(2016) · Elsevier eBooks · doi:10.1016/b978-1-78548-091-1.50017-8
New calculation of calorie content and determining nutritional level from day-to-day intake of food using Image ProcessingPreprint
Roopa Jayasingh., J., Nagajayanthi, B., Neebha, T. M., Sagayam, K. M., & Elngar, A. A. (2022) · Research Square · doi:10.21203/rs.3.rs-1766525/v2
A hybrid model for long-term prediction of glycemic oscillation in individuals with type 1 diabetes and suggesting personalized recommendations.
Pereira, J. P. A. (2022) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.3.2022.tde-14032023-092608
Validating the Telephone Montreal Cognitive Assessment Scale–Hindi as a Remote Screening Tool for Mild Cognitive Impairment
Mulchandani, R., Grover, U., Ray, S., Gandotra, S., Dhamija, R. K., & Lyngdoh, T. (2025) · Cognitive and Behavioral Neurology · doi:10.1097/wnn.0000000000000389
Research on Deep Applications Based on Visual Recognition Technology
Yu, Y., Sebial, A. J., & Canillo, A. C. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-5318-8_43
Nutritional Value and Calorie Detection Using Machine Learning and Deep Learning Techniques
Muthaiah, U., Varanasi, S. S., Sreevas, B., & Vidhyasagar, B. S. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-96-5210-5_46
Temporal Eating Patterns and Ultra-Processed Food Consumption Assessed from Mobile Food Records of Australian Adults
Healy, J. D., Dhaliwal, S. S., Pollard, C. M., Harray, A. J., Blekkenhorst, L. C., Zhu, F. M., & Kerr, D. A. (2025) · Nutrients · doi:10.3390/nu17203302
Self-citation
Multi-Class Image Classification of Food Recognition and Nutritional Analysis Using DNN
Yadav, V., Jain, A., & Ratna, S. (2025) · doi:10.1109/iccds64403.2025.11209236
Classification of food attribute for healthcare using multi-scale CNN model
Yadav, K., Aggarwal, K., Bhardwaj, S., & Gill, A. K. (2026) · AIP conference proceedings · doi:10.1063/5.0322624
Lumping vs. Splitting Nutrients, Foods and Diet Patterns in Nutrition Research: Science Dialogue Mapping of Origins, Uses and Knowledge Gaps
Ayoub‐Charette, S., Chiavaroli, L., Cowan, A., Erndt‐Marino, J. D., Higgins, K., Vorland, C. J., DeVore, C. D., Brown, A. W., & Brown, A. W. (2026) · Journal of Nutrition · doi:10.21203/rs.3.rs-8750254/v2
DGC-CSWin: Dynamic Global Context-Enhanced CSWin for Fine-Grained Food Image Recognition
Guo, Y., & Xiao, Z. (2026) · Applied Sciences · doi:10.3390/app16199616
Artificial Intelligence-Driven Dietary Assessment Software: A Structured Narrative Review of Technological Advances, Persistent Measurement Error and Barriers to Clinical Translation
Khan, M. I., Turki, A., Alanezi, S., Almutairi, M. G., Ashfaq, F., & Khan, M. F. (2026) · Nutrients · doi:10.3390/nu18203406

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks