Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features

Simple item page
dc.contributor.authorChen, Guangzong
dc.contributor.authorJia, Wenyan
dc.contributor.authorZhao, Yifan
dc.contributor.authorMao, Zhi-Hong
dc.contributor.authorLo, Benny
dc.contributor.authorAnderson, Alex K.
dc.contributor.authorFrost, Gary
dc.contributor.authorJobarteh, Modou L.
dc.contributor.authorMcCrory, Megan A.
dc.contributor.authorSazonov, Edward
dc.contributor.authorSteiner-Asiedu, Matilda
dc.contributor.authorAnsong, Richard S.
dc.contributor.authorBaranowski, Thomas
dc.contributor.authorBurke, Lora
dc.contributor.authorSun, Mingui
dc.contributor.otherUniversity of Pittsburgh
dc.contributor.otherImperial College London
dc.contributor.otherUniversity of Georgia
dc.contributor.otherBoston University
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.contributor.otherUniversity of Ghana
dc.contributor.otherUnited States Department of Agriculture (USDA)
dc.contributor.otherBaylor College of Medicine
dc.date.accessioned2023-09-28T19:31:09Z
dc.date.available2023-09-28T19:31:09Z
dc.date.issued2021
dc.description.abstractMalnutrition, including both undernutrition and obesity, is a significant problemin 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.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationChen, G., Jia, W., Zhao, Y., Mao, Z.-H., Lo, B., Anderson, A. K., Frost, G., Jobarteh, M. L., McCrory, M. A., Sazonov, E., Steiner-Asiedu, M., Ansong, R. S., Baranowski, T., Burke, L., & Sun, M. (2021). Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features. In Frontiers in Artificial Intelligence (Vol. 4). Frontiers Media SA. https://doi.org/10.3389/frai.2021.644712
dc.identifier.doi10.3389/frai.2021.644712
dc.identifier.orcidhttps://orcid.org/0000-0002-5080-108X
dc.identifier.urihttps://ir.ua.edu/handle/123456789/11371
dc.languageEnglish
dc.language.isoen_US
dc.publisherFrontiers
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectegocentric image
dc.subjectwearable device
dc.subjecttechnology-based dietary assessment
dc.subjectlow- and middle-income country
dc.subjectartificial intelligence
dc.subjectDOUBLE BURDEN
dc.subjectMALNUTRITION
dc.subjectTECHNOLOGY
dc.subjectCOMPUTER
dc.subjectSENSORS
dc.subjectDIET
dc.subjectComputer Science, Artificial Intelligence
dc.subjectComputer Science, Information Systems
dc.titleFood/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Featuresen_US
dc.typeArticle
dc.typetext
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
PMC8047062-frai-04-644712.pdf
Size:
2.2 MB
Format:
Adobe Portable Document Format
Download
Collections
Research and Publications - Department of Computer Science