ArticleIEEE journal of biomedical and health informatics2021
"Automatic Ingestion Monitor Version 2" - A Novel Wearable Device for Automatic Food Intake Detection and Passive Capture of Food Images.
Article in IEEE journal of biomedical and health informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 86 citations in OpenAlex.
- Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection.Sensors (Basel, Switzerland) · 2022Pooled 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
- "How difficult it is to change dietary behaviour" experience of older people with sarcopenic obesity: a qualitative study.BMC geriatrics · 2024Trial
- When Ultra-Processed Foods are Eaten: The Missing Dimension in Exposure Measurement for Obesity Research.Current obesity reports · 2026Review
- Enhancing Nutritional Ingestive Behavior Microstructure Detection: Video Annotation and Passive Sensing Approaches.Nutrients · 2026Article
- NIH-FDA Nutrition Regulatory Science Workshop: advancing research and policy.The American journal of clinical nutrition · 2026Article
- Advances and opportunities in measuring dietary intake: from omics to AI.Nature metabolism · 2026Review
- Integration of modern technologies to advance dietary assessment.Nature food · 2026Review
- Objective Assessment of Media Screens Effect on Physical Activity in Preschool Children.IEEE access : practical innovations, open solutions · 2026Article
- Multi-Task NoisyViT for Enhanced Fruit and Vegetable Freshness Detection and Type Classification.Sensors (Basel, Switzerland) · 2025Article
- Wearable sensing in eating episode monitoring: an updated systematic review protocol.BMJ open · 2025Article
- Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools-a scoping review of potential applications.Frontiers in nutrition · 2025Review
- AI-enabled wearable cameras for assisting dietary assessment in African populations.NPJ digital medicine · 2024Article
- Article
- sEMG-based automatic characterization of swallowed materials.Biomedical engineering online · 2024Article
- A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor.Sensors (Basel, Switzerland) · 2024Article
- Transforming Big Data into AI-ready data for nutrition and obesity research.Obesity (Silver Spring, Md.) · 2024Review
- Contextualizing remote fall risk: Video data capture and implementing ethical AI.NPJ digital medicine · 2024Review
- Detecting Eating Episodes From Wrist Motion Using Daily Pattern Analysis.IEEE journal of biomedical and health informatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 2 countries.
Funding
Abstract
Use of food image capture and/or wearable sensors for dietary assessment has grown in popularity. "Active" methods rely on the user to take an image of each eating episode. "Passive" methods use wearable cameras that continuously capture images. Most of "passively" captured images are not related to food consumption and may present privacy concerns. In this paper, we propose a novel wearable sensor (Automatic Ingestion Monitor, AIM-2) designed to capture images only during automatically detected eating episodes. The capture method was validated on a dataset collected from 30 volunteers in the community wearing the AIM-2 for 24h in pseudo-free-living and 24h in a free-living environment. The AIM-2 was able to detect food intake over 10-second epochs with a (mean and standard deviation) F1-score of 81.8 ± 10.1%. The accuracy of eating episode detection was 82.7%. Out of a total of 180,570 images captured, 8,929 (4.9%) images belonged to detected eating episodes. Privacy concerns were assessed by a questionnaire on a scale 1-7. Continuous capture had concern value of 5.0 ± 1.6 (concerned) while image capture only during food intake had concern value of 1.9 ±1.7 (not concerned). Results suggest that AIM-2 can provide accurate detection of food intake, reduce the number of images for analysis and alleviate the privacy concerns of the users.
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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.