ArticleEntropy (Basel, Switzerland)2022
Dietary Nutritional Information Autonomous Perception Method Based on Machine Vision in Smart Homes.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review.Annals of medicine · 2023Pooled it
- AI Digitized 3D-Printed Meal for Personalized Nutrition.Journal of food science · 2026Article
- TCN-attention-HAR: human activity recognition based on attention mechanism time convolutional network.Scientific reports · 2024Article
- Article
- Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
In order to automatically perceive the user's dietary nutritional information in the smart home environment, this paper proposes a dietary nutritional information autonomous perception method based on machine vision in smart homes. Firstly, we proposed a food-recognition algorithm based on YOLOv5 to monitor the user's dietary intake using the social robot. Secondly, in order to obtain the nutritional composition of the user's dietary intake, we calibrated the weight of food ingredients and designed the method for the calculation of food nutritional composition; then, we proposed a dietary nutritional information autonomous perception method based on machine vision (DNPM) that supports the quantitative analysis of nutritional composition. Finally, the proposed algorithm was tested on the self-expanded dataset CFNet-34 based on the Chinese food dataset ChineseFoodNet. The test results show that the average recognition accuracy of the food-recognition algorithm based on YOLOv5 is 89.7%, showing good accuracy and robustness. According to the performance test results of the dietary nutritional information autonomous perception system in smart homes, the average nutritional composition perception accuracy of the system was 90.1%, the response time was less than 6 ms, and the speed was higher than 18 fps, showing excellent robustness and nutritional composition perception performance.
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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.