ArticleNutrients2019
Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment.
Article in Nutrients, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 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
22 citing papers in PubMed, 4 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Pooled it
- Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection.Sensors (Basel, Switzerland) · 2022Pooled it
- Systematic Literature Review of Food-Intake Monitoring in an Aging Population.Sensors (Basel, Switzerland) · 2019Pooled it
- Snacking Behaviors in Relation to Stress and Physical Activity Using Automated Dietary Assessment: Observational Study.JMIR formative research · 2026Observational
- Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review.Journal of medical Internet research · 2024Article
- Transforming Big Data into AI-ready data for nutrition and obesity research.Obesity (Silver Spring, Md.) · 2024Review
- Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Dietary Behavior Assessments in Children-A Mixed-Method Research Exploring the Perspective of Pediatric Dieticians on Innovative Technologies.Current developments in nutrition · 2023Article
- Improved Wearable Devices for Dietary Assessment Using a New Camera System.Sensors (Basel, Switzerland) · 2022Article
- Masticatory Behaviors and Gender Differences in People with Obesity as Measured via an Earphone-Style Light-Sensor-Based Mastication Meter.Nutrients · 2022Article
- Article
- Impact of Masticatory Behaviors Measured With Wearable Device on Metabolic Syndrome: Cross-sectional Study.JMIR mHealth and uHealth · 2022Article
- Enhancing Nutrition Care Through Real-Time, Sensor-Based Capture of Eating Occasions: A Scoping Review.Frontiers in nutrition · 2022Article
- Microstructural changes in human ingestive behavior after Roux-en-Y gastric bypass during liquid meals.JCI insight · 2021 · on this mapObservational
- Short and Long-Term Innovations on Dietary Behavior Assessment and Coaching: Present Efforts and Vision of the Pride and Prejudice Consortium.International journal of environmental research and public health · 2021Article
- Emerging trends of technology-based dietary assessment: a perspective study.European journal of clinical nutrition · 2021Review
- Occurrence of and Reasons for "Missing Events" in Mobile Dietary Assessments: Results From Three Event-Based Ecological Momentary Assessment Studies.JMIR mHealth and uHealth · 2020Article
- Automatic Count of Bites and Chews From Videos of Eating Episodes.IEEE access : practical innovations, open solutions · 2020Article
- The development of food image detection and recognition model of Korean food for mobile dietary management.Nutrition research and practice · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Video observations have been widely used for providing ground truth for wearable systems for monitoring food intake in controlled laboratory conditions; however, video observation requires participants be confined to a defined space. The purpose of this analysis was to test an alternative approach for establishing activity types and food intake bouts in a relatively unconstrained environment. The accuracy of a wearable system for assessing food intake was compared with that from video observation, and inter-rater reliability of annotation was also evaluated. Forty participants were enrolled. Multiple participants were simultaneously monitored in a 4-bedroom apartment using six cameras for three days each. Participants could leave the apartment overnight and for short periods of time during the day, during which time monitoring did not take place. A wearable system (Automatic Ingestion Monitor, AIM) was used to detect and monitor participants' food intake at a resolution of 30 s using a neural network classifier. Two different food intake detection models were tested, one trained on the data from an earlier study and the other on current study data using leave-one-out cross validation. Three trained human raters annotated the videos for major activities of daily living including eating, drinking, resting, walking, and talking. They further annotated individual bites and chewing bouts for each food intake bout. Results for inter-rater reliability showed that, for activity annotation, the raters achieved an average (±standard deviation (STD)) kappa value of 0.74 (±0.02) and for food intake annotation the average kappa (Light's kappa) of 0.82 (±0.04). Validity results showed that AIM food intake detection matched human video-annotated food intake with a kappa of 0.77 (±0.10) and 0.78 (±0.12) for activity annotation and for food intake bout annotation, respectively. Results of one-way ANOVA suggest that there are no statistically significant differences among the average eating duration estimated from raters' annotations and AIM predictions (
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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.