ArticlePublic health nutrition2019
Image-based food portion size estimation using a smartphone without a fiducial marker.
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 19 papers, 1 of them a synthesis that pooled it.
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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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review.Annals of medicine · 2023Pooled it
- Acceptability, Usability and Weight Loss Outcomes in a Randomized Cross-Over Study of Commercially Available Portion Size Tools in an Overweight South Asian Community.International journal of environmental research and public health · 2022Trial
- Vision-Based Automated Inspection of Box Meals for Food Portion Defects and Foreign Object Detection.Sensors (Basel, Switzerland) · 2026Article
- Article
- Multimodal LLM-driven IoT digital healthcare platform for intelligent dysphagia dietary monitoring.Frontiers in nutrition · 2026Article
- Visual Food Ingredient Prediction Using Deep Learning with Direct F-Score Optimization.Foods (Basel, Switzerland) · 2025Article
- DietAI24 as a framework for comprehensive nutrition estimation using multimodal large language models.Communications medicine · 2025Article
- 'The wisdom of crowds': a survey on the rating of nutritional values of meals in digital pictures.BMJ nutrition, prevention & health · 2025Article
- AI-enabled wearable cameras for assisting dietary assessment in African populations.NPJ digital medicine · 2024Article
- Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review.Journal of medical Internet research · 2024Article
- A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms.Scientific reports · 2023Article
- Multimedia Data-Based Mobile Applications for Dietary Assessment.Journal of diabetes science and technology · 2023Review
- Estimation of Plate and Bowl Dimensions for Food Portion Size Assessment in a Wearable Sensor System.IEEE sensors journal · 2023Article
- Evaluating augmented reality for 'real life' teaching of food portion concepts.Journal of human nutrition and dietetics : the official journal of the British Dietetic Association · 2022Article
- A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation.Sensors (Basel, Switzerland) · 2022Article
- Perspective: Opportunities and Challenges of Technology Tools in Dietary and Activity Assessment: Bridging Stakeholder Viewpoints.Advances in nutrition (Bethesda, Md.) · 2022Article
- Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system.Frontiers in nutrition · 2022Article
- Current Developments in Digital Quantitative Volume Estimation for the Optimisation of Dietary Assessment.Nutrients · 2020Review
- Man or machine? Will the digital transition be able to automatize dietary intake data collection?Public health nutrition · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveCurrent approaches to food volume estimation require the person to carry a fiducial marker (e.g. a checkerboard card), to be placed next to the food before taking a picture. This procedure is inconvenient and post-processing of the food picture is time-consuming and sometimes inaccurate. These problems keep people from using the smartphone for self-administered dietary assessment. The current bioengineering study presents a novel smartphone-based imaging approach to table-side estimation of food volume which overcomes current limitations.
designWe present a new method for food volume estimation without a fiducial marker. Our mathematical model indicates that, using a special picture-taking strategy, the smartphone-based imaging system can be calibrated adequately if the physical length of the smartphone and the output of the motion sensor within the device are known. We also present and test a new virtual reality method for food volume estimation using the International Food Unit™ and a training process for error control.
resultsOur pilot study, with sixty-nine participants and fifteen foods, indicates that the fiducial-marker-free approach is valid and that the training improves estimation accuracy significantly (P0·05).
conclusionsElimination of a fiducial marker and application of virtual reality, the International Food Unit™ and an automated training allowed quick food volume estimation and control of the estimation error. The estimated volume could be used to search a nutrient database and determine energy and nutrients in the diet.
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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.