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Radtke MD, Steinberg FM, Scherr RE. Methods for Assessing Health Outcomes Associated with Food Insecurity in the United States College Student Population: A Narrative Review. Adv Nutr 2024; 15:100131. [PMID: 37865221 PMCID: PMC10831897 DOI: 10.1016/j.advnut.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 10/03/2023] [Accepted: 10/12/2023] [Indexed: 10/23/2023] Open
Abstract
In the United States, college students experience disproportionate food insecurity (FI) rates compared to the national prevalence. The experience of acute and chronic FI has been associated with negative physical and mental health outcomes in this population. This narrative review aims to summarize the current methodologies for assessing health outcomes associated with the experience of FI in college students in the United States. To date, assessing the health outcomes of FI has predominately consisted of subjective assessments, such as self-reported measures of dietary intake, perceived health status, stress, depression, anxiety, and sleep behaviors. This review, along with the emergence of FI as an international public health concern, establishes the need for novel, innovative, and objective biomarkers to evaluate the short- and long-term impacts of FI on physical and mental health outcomes in college students. The inclusion of objective biomarkers will further elucidate the relationship between FI and a multitude of health outcomes to better inform strategies for reducing the pervasiveness of FI in the United States college student population.
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Affiliation(s)
- Marcela D Radtke
- Propel Postdoctoral Fellow, Department of Epidemiology and Population Health, Stanford School of Medicine, Stanford University, Palo Alto, CA, USA 94305
| | | | - Rachel E Scherr
- Family, Interiors, Nutrition & Apparel Department, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA, USA, 94132; Scherr Nutrition Science Consulting, San Francisco, CA, 94115.
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James Stubbs R, Horgan G, Robinson E, Hopkins M, Dakin C, Finlayson G. Diet composition and energy intake in humans. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220449. [PMID: 37661746 PMCID: PMC10475874 DOI: 10.1098/rstb.2022.0449] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 06/16/2023] [Indexed: 09/05/2023] Open
Abstract
Absolute energy from fats and carbohydrates and the proportion of carbohydrates in the food supply have increased over 50 years. Dietary energy density (ED) is primarily decreased by the water and increased by the fat content of foods. Protein, carbohydrates and fat exert different effects on satiety or energy intake (EI) in the order protein > carbohydrates > fat. When the ED of different foods is equalized the differences between fat and carbohydrates are modest. Covertly increasing dietary ED with fat, carbohydrate or mixed macronutrients elevates EI, producing weight gain and vice versa. In more naturalistic situations where learning cues are intact, there appears to be greater compensation for the different ED of foods. There is considerable individual variability in response. Macronutrient-specific negative feedback models of EI regulation have limited capacity to explain how availability of cheap, highly palatable, readily assimilated, energy-dense foods lead to obesity in modern environments. Neuropsychological constructs including food reward (liking, wanting and learning), reactive and reflective decision making, in the context of asymmetric energy balance regulation, give more comprehensive explanations of how environmental superabundance of foods containing mixtures of readily assimilated fats and carbohydrates and caloric beverages elevate EI through combined hedonic, affective, cognitive and physiological mechanisms. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.
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Affiliation(s)
| | - Graham Horgan
- Biomathematics and Statistics Scotland, Rowett Institute, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD Scotland, UK
| | - Eric Robinson
- School of Food Science and Nutrition, Faculty of Environment, University of Leeds, Leeds LS2 9JT, UK
| | - Mark Hopkins
- Institute of Population health, University of Liverpool, Liverpool L69 3GF, UK
| | - Clarissa Dakin
- School of Psychology, Faculty of Medicine and Health and
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Jia W, Li B, Zheng Y, Mao ZH, Sun M. Estimating Amount of Food in a Circular Dining Bowl from a Single Image. Madima 23 (2023) 2023; 2023:1-9. [PMID: 38288389 PMCID: PMC10823382 DOI: 10.1145/3607828.3617789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Unhealthy diet is a top risk factor causing obesity and numerous chronic diseases. To help the public adopt healthy diet, nutrition scientists need user-friendly tools to conduct Dietary Assessment (DA). In recent years, new DA tools have been developed using a smartphone or a wearable device which acquires images during a meal. These images are then processed to estimate calories and nutrients of the consumed food. Although considerable progress has been made, 2D food images lack scale reference and 3D volumetric information. In addition, food must be sufficiently observable from the image. This basic condition can be met when the food is stand-alone (no food container is used) or it is contained in a shallow plate. However, the condition cannot be met easily when a bowl is used. The food is often occluded by the bowl edge, and the shape of the bowl may not be fully determined from the image. However, bowls are the most utilized food containers by billions of people in many parts of the world, especially in Asia and Africa. In this work, we propose to premeasure plates and bowls using a marked adhesive strip before a dietary study starts. This simple procedure eliminates the use of a scale reference throughout the DA study. In addition, we use mathematical models and image processing to reconstruct the bowl in 3D. Our key idea is to estimate how full the bowl is rather than how much food is (in either volume or weight) in the bowl. This idea reduces the effect of occlusion. The experimental data have shown satisfactory results of our methods which enable accurate DA studies using both plates and bowls with reduced burden on research participants.
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Affiliation(s)
- Wenyan Jia
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Boyang Li
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yaguang Zheng
- Rory Meyers College of Nursing, New York University, New York, NY, USA
| | - Zhi-Hong Mao
- Departments of Electrical and Computer Engineering, and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mingui Sun
- Departments of Neurosurgery Electrical and Computer Engineering and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
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Jobarteh ML, McCrory MA, Lo B, Triantafyllidis KK, Qiu J, Griffin JP, Sazonov E, Sun M, Jia W, Baranowski T, Anderson AK, Maitland K, Frost G. Evaluation of Acceptability, Functionality, and Validity of a Passive Image-Based Dietary Intake Assessment Method in Adults and Children of Ghanaian and Kenyan Origin Living in London, UK. Nutrients 2023; 15:4075. [PMID: 37764857 PMCID: PMC10537234 DOI: 10.3390/nu15184075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Accurate estimation of dietary intake is challenging. However, whilst some progress has been made in high-income countries, low- and middle-income countries (LMICs) remain behind, contributing to critical nutritional data gaps. This study aimed to validate an objective, passive image-based dietary intake assessment method against weighed food records in London, UK, for onward deployment to LMICs. METHODS Wearable camera devices were used to capture food intake on eating occasions in 18 adults and 17 children of Ghanaian and Kenyan origin living in London. Participants were provided pre-weighed meals of Ghanaian and Kenyan cuisine and camera devices to automatically capture images of the eating occasions. Food images were assessed for portion size, energy, nutrient intake, and the relative validity of the method compared to the weighed food records. RESULTS The Pearson and Intraclass correlation coefficients of estimates of intakes of food, energy, and 19 nutrients ranged from 0.60 to 0.95 and 0.67 to 0.90, respectively. Bland-Altman analysis showed good agreement between the image-based method and the weighed food record. Under-estimation of dietary intake by the image-based method ranged from 4 to 23%. CONCLUSIONS Passive food image capture and analysis provides an objective assessment of dietary intake comparable to weighed food records.
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Affiliation(s)
- Modou L. Jobarteh
- Department of Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Megan A. McCrory
- Department of Health Sciences, Boston University, Boston, MA 02215, USA;
| | - Benny Lo
- Hamlyn Centre, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK; (B.L.); (J.Q.)
| | - Konstantinos K. Triantafyllidis
- Section for Nutrition Research, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2BX, UK; (K.K.T.); (J.P.G.); (G.F.)
| | - Jianing Qiu
- Hamlyn Centre, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK; (B.L.); (J.Q.)
| | - Jennifer P. Griffin
- Section for Nutrition Research, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2BX, UK; (K.K.T.); (J.P.G.); (G.F.)
| | - Edward Sazonov
- Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA;
| | - Mingui Sun
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15261, USA; (M.S.); (W.J.)
| | - Wenyan Jia
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15261, USA; (M.S.); (W.J.)
| | - Tom Baranowski
- USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Alex K. Anderson
- Department of Nutritional Sciences, University of Georgia, Athens, GA 30602, USA;
| | | | - Gary Frost
- Section for Nutrition Research, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2BX, UK; (K.K.T.); (J.P.G.); (G.F.)
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Sun M, Jia W, Chen G, Hou M, Chen J, Mao ZH. Improved Wearable Devices for Dietary Assessment Using a New Camera System. Sensors (Basel) 2022; 22:8006. [PMID: 36298356 PMCID: PMC9609969 DOI: 10.3390/s22208006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/12/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
An unhealthy diet is strongly linked to obesity and numerous chronic diseases. Currently, over two-thirds of American adults are overweight or obese. Although dietary assessment helps people improve nutrition and lifestyle, traditional methods for dietary assessment depend on self-report, which is inaccurate and often biased. In recent years, as electronics, information, and artificial intelligence (AI) technologies advanced rapidly, image-based objective dietary assessment using wearable electronic devices has become a powerful approach. However, research in this field has been focused on the developments of advanced algorithms to process image data. Few reports exist on the study of device hardware for the particular purpose of dietary assessment. In this work, we demonstrate that, with the current hardware design, there is a considerable risk of missing important dietary data owing to the common use of rectangular image screen and fixed camera orientation. We then present two designs of a new camera system to reduce data loss by generating circular images using rectangular image sensor chips. We also present a mechanical design that allows the camera orientation to be adjusted, adapting to differences among device wearers, such as gender, body height, and so on. Finally, we discuss the pros and cons of rectangular versus circular images with respect to information preservation and data processing using AI algorithms.
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Affiliation(s)
- Mingui Sun
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Wenyan Jia
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Guangzong Chen
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Mingke Hou
- Department of Mechanical Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Jiacheng Chen
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Zhi-Hong Mao
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
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Till S, Mkhize M, Farao J, Shandu LD, Muthelo L, Coleman TL, Mbombi M, Bopape M, Klingberg S, van Heerden A, Mothiba T, Densmore M, Verdezoto Dias NX. Digital Health Technologies for Maternal and Child Health in African and other LMICs: A Cross-disciplinary Scoping Review with Stakeholder Consultation (Preprint). J Med Internet Res 2022; 25:e42161. [PMID: 37027199 PMCID: PMC10131761 DOI: 10.2196/42161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/16/2022] [Accepted: 01/31/2023] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Maternal and child health (MCH) is a global health concern, especially impacting low- and middle-income countries (LMIC). Digital health technologies are creating opportunities to address the social determinants of MCH by facilitating access to information and providing other forms of support throughout the maternity journey. Previous reviews in different disciplines have synthesized digital health intervention outcomes in LMIC. However, contributions in this space are scattered across publications in different disciplines and lack coherence in what digital MCH means across fields. OBJECTIVE This cross-disciplinary scoping review synthesized the existing published literature in 3 major disciplines on the use of digital health interventions for MCH in LMIC, with a particular focus on sub-Saharan Africa. METHODS We conducted a scoping review using the 6-stage framework by Arksey and O'Malley across 3 disciplines, including public health, social sciences applied to health, and human-computer interaction research in health care. We searched the following databases: Scopus, PubMed, Google Scholar, ACM Digital Library, IEEE Xplore, Web of Science, and PLOS. A stakeholder consultation was undertaken to inform and validate the review. RESULTS During the search, 284 peer-reviewed articles were identified. After removing 41 duplicates, 141 articles met our inclusion criteria: 34 from social sciences applied to health, 58 from public health, and 49 from human-computer interaction research in health care. These articles were then tagged (labeled) by 3 researchers using a custom data extraction framework to obtain the findings. First, the scope of digital MCH was found to target health education (eg, breastfeeding and child nutrition), care and follow-up of health service use (to support community health workers), maternal mental health, and nutritional and health outcomes. These interventions included mobile apps, SMS text messaging, voice messaging, web-based applications, social media, movies and videos, and wearable or sensor-based devices. Second, we highlight key challenges: little attention has been given to understanding the lived experiences of the communities; key role players (eg, fathers, grandparents, and other family members) are often excluded; and many studies are designed considering nuclear families that do not represent the family structures of the local cultures. CONCLUSIONS Digital MCH has shown steady growth in Africa and other LMIC settings. Unfortunately, the role of the community was negligible, as these interventions often do not include communities early and inclusively enough in the design process. We highlight key opportunities and sociotechnical challenges for digital MCH in LMIC, such as more affordable mobile data; better access to smartphones and wearable technologies; and the rise of custom-developed, culturally appropriate apps that are more suited to low-literacy users. We also focus on barriers such as an overreliance on text-based communications and the difficulty of MCH research and design to inform and translate into policy.
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Affiliation(s)
- Sarina Till
- School of Information Technology, Independent Institute of Education, Durban, South Africa
- Department of Computer Science, University of Cape Town, Cape Town, South Africa
| | - Mirriam Mkhize
- Human Sciences Research Council, Centre for Community Based Research, Sweet Waters, South Africa
| | - Jaydon Farao
- Department of Computer Science, University of Cape Town, Cape Town, South Africa
| | - Londiwe Deborah Shandu
- Human Sciences Research Council, Centre for Community Based Research, Sweet Waters, South Africa
| | - Livhuwani Muthelo
- Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
| | | | - Masenyani Mbombi
- Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
| | - Mamara Bopape
- Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
| | - Sonja Klingberg
- South African Medical Research Council/Wits Developmental Pathways for Health Research Unit, University of the Witwatersrand, Johannesburg, South Africa
| | - Alastair van Heerden
- Human Sciences Research Council, Centre for Community Based Research, Sweet Waters, South Africa
- South African Medical Research Council/Wits Developmental Pathways for Health Research Unit, University of the Witwatersrand, Johannesburg, South Africa
| | - Tebogo Mothiba
- Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
| | - Melissa Densmore
- Department of Computer Science, University of Cape Town, Cape Town, South Africa
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Cerminaro C, Sazonov E, McCrory MA, Steiner-Asiedu M, Bhaskar V, Gallo S, Laing E, Jia W, Sun M, Baranowski T, Frost G, Lo B, Anderson AK. Feasibility of the automatic ingestion monitor (AIM-2) for infant feeding assessment: a pilot study among breast-feeding mothers from Ghana. Public Health Nutr 2022; 25:1-11. [PMID: 35616087 PMCID: PMC9991851 DOI: 10.1017/s1368980022001264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 03/23/2022] [Accepted: 05/12/2022] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Passive, wearable sensors can be used to obtain objective information in infant feeding, but their use has not been tested. Our objective was to compare assessment of infant feeding (frequency, duration and cues) by self-report and that of the Automatic Ingestion Monitor-2 (AIM-2). DESIGN A cross-sectional pilot study was conducted in Ghana. Mothers wore the AIM-2 on eyeglasses for 1 d during waking hours to assess infant feeding using images automatically captured by the device every 15 s. Feasibility was assessed using compliance with wearing the device. Infant feeding practices collected by the AIM-2 images were annotated by a trained evaluator and compared with maternal self-report via interviewer-administered questionnaire. SETTING Rural and urban communities in Ghana. PARTICIPANTS Participants were thirty eight (eighteen rural and twenty urban) breast-feeding mothers of infants (child age ≤7 months). RESULTS Twenty-five mothers reported exclusive breast-feeding, which was common among those < 30 years of age (n 15, 60 %) and those residing in urban communities (n 14, 70 %). Compliance with wearing the AIM-2 was high (83 % of wake-time), suggesting low user burden. Maternal report differed from the AIM-2 data, such that mothers reported higher mean breast-feeding frequency (eleven v. eight times, P = 0·041) and duration (18·5 v. 10 min, P = 0·007) during waking hours. CONCLUSION The AIM-2 was a feasible tool for the assessment of infant feeding among mothers in Ghana as a passive, objective method and identified overestimation of self-reported breast-feeding frequency and duration. Future studies using the AIM-2 are warranted to determine validity on a larger scale.
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Affiliation(s)
- Caroline Cerminaro
- Department of Nutritional Sciences, University of Georgia, 280 Dawson Hall, 305 Sanford Drive, Athens, GA30602, USA
| | - Edward Sazonov
- Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL, USA
| | - Megan A McCrory
- Department of Health Sciences, Boston University, Boston, MA, USA
| | | | - Viprav Bhaskar
- Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL, USA
| | - Sina Gallo
- Department of Nutritional Sciences, University of Georgia, 280 Dawson Hall, 305 Sanford Drive, Athens, GA30602, USA
| | - Emma Laing
- Department of Nutritional Sciences, University of Georgia, 280 Dawson Hall, 305 Sanford Drive, Athens, GA30602, USA
| | - Wenyan Jia
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mingui Sun
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tom Baranowski
- USDA/ARS Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
| | - Gary Frost
- Department of Medicine, Imperial College London, London, UK
| | - Benny Lo
- The Hamlyn Center, Imperial College London, London, UK
| | - Alex Kojo Anderson
- Department of Nutritional Sciences, University of Georgia, 280 Dawson Hall, 305 Sanford Drive, Athens, GA30602, USA
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Jia W, Ren Y, Li B, Beatrice B, Que J, Cao S, Wu Z, Mao ZH, Lo B, Anderson AK, Frost G, McCrory MA, Sazonov E, Steiner-Asiedu M, Baranowski T, Burke LE, Sun M. A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation. Sensors (Basel) 2022; 22:1493. [PMID: 35214399 PMCID: PMC8877095 DOI: 10.3390/s22041493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/08/2022] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Knowing the amounts of energy and nutrients in an individual's diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image. This problem has not been studied systematically despite the bowl being the most utilized food container in many parts of the world, especially in Asia and Africa. In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper ruler centrally across the bottom and sides of the bowl and then taking an image. When observed from the image, the distortions in the width of the paper ruler and the spacings between ruler markers completely encode the size and shape of the bowl. A computational algorithm is developed to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method for food volume estimation involving round bowls as containers. A total of 228 images of amorphous foods were also used in a comparative experiment between our algorithm and an independent human estimator. The results showed that our algorithm overperformed the human estimator who utilized different types of reference information and two estimation methods, including direct volume estimation and indirect estimation through the fullness of the bowl.
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Affiliation(s)
- Wenyan Jia
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA; (W.J.); (Y.R.); (B.L.); (J.Q.); (S.C.); (Z.W.); (Z.-H.M.)
| | - Yiqiu Ren
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA; (W.J.); (Y.R.); (B.L.); (J.Q.); (S.C.); (Z.W.); (Z.-H.M.)
| | - Boyang Li
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA; (W.J.); (Y.R.); (B.L.); (J.Q.); (S.C.); (Z.W.); (Z.-H.M.)
| | - Britney Beatrice
- School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA;
| | - Jingda Que
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA; (W.J.); (Y.R.); (B.L.); (J.Q.); (S.C.); (Z.W.); (Z.-H.M.)
| | - Shunxin Cao
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA; (W.J.); (Y.R.); (B.L.); (J.Q.); (S.C.); (Z.W.); (Z.-H.M.)
| | - Zekun Wu
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA; (W.J.); (Y.R.); (B.L.); (J.Q.); (S.C.); (Z.W.); (Z.-H.M.)
| | - Zhi-Hong Mao
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA; (W.J.); (Y.R.); (B.L.); (J.Q.); (S.C.); (Z.W.); (Z.-H.M.)
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Benny Lo
- Hamlyn Centre, Imperial College London, London SW7 2AZ, UK;
| | - Alex K. Anderson
- Department of Nutritional Sciences, University of Georgia, Athens, GA 30602, USA;
| | - Gary Frost
- Section for Nutrition Research, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK;
| | - Megan A. McCrory
- Department of Health Sciences, Boston University, Boston, MA 02210, USA;
| | - Edward Sazonov
- Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA;
| | - Matilda Steiner-Asiedu
- Department of Nutrition and Food Science, University of Ghana, Legon Boundary, Accra LG 1181, Ghana;
| | - Tom Baranowski
- USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Lora E. Burke
- School of Nursing, University of Pittsburgh, Pittsburgh, PA 15260, USA;
| | - Mingui Sun
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA; (W.J.); (Y.R.); (B.L.); (J.Q.); (S.C.); (Z.W.); (Z.-H.M.)
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA 15260, USA
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9
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Yang Z, Yu H, Cao S, Xu Q, Yuan D, Zhang H, Jia W, Mao ZH, Sun M. Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System. Electronics (Basel) 2021; 10:1556. [PMID: 34552763 PMCID: PMC8455030 DOI: 10.3390/electronics10131556] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
It is well known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed to allow an assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage being overweight, obesity, and related chronic diseases can be developed. In recent years, new wearable imaging and computational technologies have emerged. These technologies are capable of performing objective and passive dietary assessments with a much simplified procedure than traditional questionnaires. However, a critical task is required to estimate the portion size (in this case, the food volume) from a digital image. Currently, this task is very challenging because the volumetric information in the two-dimensional images is incomplete, and the estimation involves a great deal of imagination, beyond the capacity of the traditional image processing algorithms. In this work, we present a novel Artificial Intelligent (AI) system to mimic the thinking of dietitians who use a set of common objects as gauges (e.g., a teaspoon, a golf ball, a cup, and so on) to estimate the portion size. Specifically, our human-mimetic system "mentally" gauges the volume of food using a set of internal reference volumes that have been learned previously. At the output, our system produces a vector of probabilities of the food with respect to the internal reference volumes. The estimation is then completed by an "intelligent guess", implemented by an inner product between the probability vector and the reference volume vector. Our experiments using both virtual and real food datasets have shown accurate volume estimation results.
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Affiliation(s)
- Zhengeng Yang
- College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
- Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Hongshan Yu
- College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
| | - Shunxin Cao
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Qi Xu
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ding Yuan
- Image Processing Center, Beihang University, Beijing 100191, China
| | - Hong Zhang
- Image Processing Center, Beihang University, Beijing 100191, China
| | - Wenyan Jia
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Zhi-Hong Mao
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Mingui Sun
- Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
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Chen G, Jia W, Zhao Y, Mao ZH, Lo B, Anderson AK, Frost G, Jobarteh ML, McCrory MA, Sazonov E, Steiner-Asiedu M, Ansong RS, Baranowski T, Burke L, Sun M. Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features. Front Artif Intell 2021; 4:644712. [PMID: 33870184 PMCID: PMC8047062 DOI: 10.3389/frai.2021.644712] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/26/2021] [Indexed: 11/25/2022] Open
Abstract
Malnutrition, including both undernutrition and obesity, is a significant problem in low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). Our wearable device ("eButton" worn on the chest) acquires real-life images automatically during wake hours at preset time intervals. The recorded images, in amounts of tens of thousands per day, are post-processed to obtain the information of interest. Although we expect future Artificial Intelligence (AI) technology to extract the information automatically, at present we utilize AI to separate the acquired images into two binary classes: images with (Class 1) and without (Class 0) edible items. As a result, researchers need only to study Class-1 images, reducing their workload significantly. In this paper, we present a composite machine learning method to perform this classification, meeting the specific challenges of high complexity and diversity in the real-world LMIC data. Our method consists of a deep neural network (DNN) and a shallow learning network (SLN) connected by a novel probabilistic network interface layer. After presenting the details of our method, an image dataset acquired from Ghana is utilized to train and evaluate the machine learning system. Our comparative experiment indicates that the new composite method performs better than the conventional deep learning method assessed by integrated measures of sensitivity, specificity, and burden index, as indicated by the Receiver Operating Characteristic (ROC) curve.
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Affiliation(s)
- Guangzong Chen
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA, United States
| | - Wenyan Jia
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA, United States
| | - Yifan Zhao
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA, United States
| | - Zhi-Hong Mao
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA, United States
| | - Benny Lo
- Hamlyn Centre, Imperial College London, London, United Kingdom
| | - Alex K. Anderson
- Department of Foods and Nutrition, University of Georgia, Athens, GA, United States
| | - Gary Frost
- Section for Nutrition Research, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Modou L. Jobarteh
- Section for Nutrition Research, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Megan A. McCrory
- Department of Health Sciences, Boston University, Boston, MA, United States
| | - Edward Sazonov
- Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL, United States
| | | | - Richard S. Ansong
- Department of Nutrition and Food Science, University of Ghana, Legon-Accra, Ghana
| | - Thomas Baranowski
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States
| | - Lora Burke
- School of Nursing, University of Pittsburgh, Pittsburgh, PA, United States
| | - Mingui Sun
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA, United States
- Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA, United States
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