ArticlePublic health nutrition2019
Automatic food detection in egocentric images using artificial intelligence technology.
Article in Public health nutrition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 100 citations in OpenAlex.
- Deep neural network for food image classification and nutrient identification: A systematic review.Reviews in endocrine & metabolic disorders · 2023Pooled it
- The validity of measuring energy and nutrient intake with the Remote Food Photography Method (RFPM) in adolescents.The American journal of clinical nutrition · 2026Trial
- Evaluating AI models for food and alcohol advertisement classification against human benchmarks.Scientific reports · 2026Article
- Integration of modern technologies to advance dietary assessment.Nature food · 2026Review
- Experience of Using Wearable Devices for Dietary Management for Chinese Americans With Type 2 Diabetes: One-Group Prospective Cohort Study.JMIR diabetes · 2025Article
- AI-enabled wearable cameras for assisting dietary assessment in African populations.NPJ digital medicine · 2024Article
- Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review.Journal of medical Internet research · 2024Article
- Integrated image and sensor-based food intake detection in free-living.Scientific reports · 2024Article
- Revolutionising food advertising monitoring: a machine learning-based method for automated classification of food videos.Public health nutrition · 2023Article
- Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Detecting Eating and Social Presence with All Day Wearable RGB-T....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2023Article
- Capturing children food exposure using wearable cameras and deep learning.PLOS digital health · 2023Article
- Food Image Segmentation Using Multi-Modal Imaging Sensors with Color and Thermal Data.Sensors (Basel, Switzerland) · 2023Article
- Improved Wearable Devices for Dietary Assessment Using a New Camera System.Sensors (Basel, Switzerland) · 2022Article
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- Development of an Innovative Online Dietary Assessment Tool for France: Adaptation of myfood24.Nutrients · 2022Article
- Application of the deep learning algorithm in nutrition research - using serum pyridoxal 5'-phosphate as an example.Nutrition journal · 2022Article
- Perspective: Opportunities and Challenges of Technology Tools in Dietary and Activity Assessment: Bridging Stakeholder Viewpoints.Advances in nutrition (Bethesda, Md.) · 2022Article
- A Cross-Sectional Reproducibility Study of a Standard Camera Sensor Using Artificial Intelligence to Assess Food Items: The FoodIntech Project.Nutrients · 2022Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
objectiveTo develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment.
designTo study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network.
resultsA cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively.
conclusionsThe AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.