ArticlePublic health nutrition2021
An automatic electronic instrument for accurate measurements of food volume and density.
Article in Public health nutrition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 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
- Article
- A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms.Scientific reports · 2023Article
- A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation.Sensors (Basel, Switzerland) · 2022Article
- A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment.Healthcare (Basel, Switzerland) · 2021Review
- Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System.Electronics · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveAccurate measurements of food volume and density are often required as 'gold standards' for calibration of image-based dietary assessment and food database development. Currently, there is no specialised laboratory instrument for these measurements. We present the design of a new volume of density (VD) meter to bridge this technological gap.
designOur design consists of a turntable, a load sensor, a set of cameras and lights installed on an arc-shaped stationary support, and a microcomputer. It acquires an array of food images, reconstructs a 3D volumetric model, weighs the food and calculates both food volume and density, all in an automatic process controlled by the microcomputer. To adapt to the complex shapes of foods, a new food surface model, derived from the electric field of charged particles, is developed for 3D point cloud reconstruction of either convex or concave food surfaces.
resultsWe conducted two experiments to evaluate the VD meter. The first experiment utilised computer-synthesised 3D objects with prescribed convex and concave surfaces of known volumes to investigate different food surface types. The second experiment was based on actual foods with different shapes, colours and textures. Our results indicated that, for synthesised objects, the measurement error of the electric field-based method was <1 %, significantly lower compared with traditional methods. For real-world foods, the measurement error depended on the types of food volumes (detailed discussion included). The largest error was approximately 5 %.
conclusionThe VD meter provides a new electronic instrument to support advanced research in nutrition science.
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