ArticleScientific reports2022
Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review.Annals of medicine · 2023Pooled it
- Enhancing Hospital Nutrition Assessment Through Artificial Intelligence: A Prospective Tray-Level Pilot Study.Nutrients · 2026Observational
- Application of Image Computing in Non-Destructive Detection of Chinese Cuisine.Foods (Basel, Switzerland) · 2025Review
- 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
- Image-Based Volume Estimation for Food in a Bowl.Journal of food engineering · 2024Article
- The innovative technology of dough preparation for bread by the accelerated ion-ozone cavitation method.Scientific reports · 2023Article
- Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Multimedia Data-Based Mobile Applications for Dietary Assessment.Journal of diabetes science and technology · 2023Review
- Food Image Segmentation Using Multi-Modal Imaging Sensors with Color and Thermal Data.Sensors (Basel, Switzerland) · 2023Article
- Continuous monitoring of eating and sleeping behaviors in the home environments of older adults: a case study demonstration.Frontiers in public health · 2023Article
- Article
- GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention.Sensors (Basel, Switzerland) · 2021Article
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7 authors.
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Abstract
Malnutrition is a multidomain problem affecting 54% of older adults in long-term care (LTC). Monitoring nutritional intake in LTC is laborious and subjective, limiting clinical inference capabilities. Recent advances in automatic image-based food estimation have not yet been evaluated in LTC settings. Here, we describe a fully automatic imaging system for quantifying food intake. We propose a novel deep convolutional encoder-decoder food network with depth-refinement (EDFN-D) using an RGB-D camera for quantifying a plate's remaining food volume relative to reference portions in whole and modified texture foods. We trained and validated the network on the pre-labelled UNIMIB2016 food dataset and tested on our two novel LTC-inspired plate datasets (689 plate images, 36 unique foods). EDFN-D performed comparably to depth-refined graph cut on IOU (0.879 vs. 0.887), with intake errors well below typical 50% (mean percent intake error: [Formula: see text]%). We identify how standard segmentation metrics are insufficient due to visual-volume discordance, and include volume disparity analysis to facilitate system trust. This system provides improved transparency, approximates human assessors with enhanced objectivity, accuracy, and precision while avoiding hefty semi-automatic method time requirements. This may help address short-comings currently limiting utility of automated early malnutrition detection in resource-constrained LTC and hospital settings.
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