Children’s Nutrition Research Center Staff Publications
Language
English
Publication Date
12-5-2024
Journal
npj Digital Medicine
DOI
10.1038/s41746-024-01346-8
PMID
39638852
PMCID
PMC11621677
PubMedCentral® Posted Date
12-5-2024
PubMedCentral® Full Text Version
Post-print
Abstract
We have developed a population-level method for dietary assessment using low-cost wearable cameras. Our approach, EgoDiet, employs an egocentric vision-based pipeline to learn portion sizes, addressing the shortcomings of traditional self-reported dietary methods. To evaluate the functionality of this method, field studies were conducted in London (Study A) and Ghana (Study B) among populations of Ghanaian and Kenyan origin. In Study A, EgoDiet's estimations were contrasted with dietitians' assessments, revealing a performance with a Mean Absolute Percentage Error (MAPE) of 31.9% for portion size estimation, compared to 40.1% for estimates made by dietitians. We further evaluated our approach in Study B, comparing its performance to the traditional 24-Hour Dietary Recall (24HR). Our approach demonstrated a MAPE of 28.0%, showing a reduction in error when contrasted with the 24HR, which exhibited a MAPE of 32.5%. This improvement highlights the potential of using passive camera technology to serve as an alternative to the traditional dietary assessment methods.
Keywords
Nutrition, Public health, Health care
Published Open-Access
yes
Recommended Citation
Lo, Frank P-W; Qiu, Jianing; Jobarteh, Modou L; et al., "AI-Enabled Wearable Cameras for Assisting Dietary Assessment in African Populations" (2024). Children’s Nutrition Research Center Staff Publications. 266.
https://digitalcommons.library.tmc.edu/staff_pub/266
Included in
Biochemical Phenomena, Metabolism, and Nutrition Commons, Dietetics and Clinical Nutrition Commons, Endocrinology, Diabetes, and Metabolism Commons, Nutrition Commons, Public Health Commons