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Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system
Food recognition and weight estimation based on image methods have always been hotspots in the field of computer vision and medical nutrition, and have good application prospects in digital nutrition therapy and health detection. With the development of deep learning technology, image-based recognit...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9709194/ https://www.ncbi.nlm.nih.gov/pubmed/36466396 http://dx.doi.org/10.3389/fnut.2022.965801 |
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author | Zhang, Qinqiu He, Chengyuan Qin, Wen Liu, Decai Yin, Jun Long, Zhiwen He, Huimin Sun, Ho Ching Xu, Huilin |
author_facet | Zhang, Qinqiu He, Chengyuan Qin, Wen Liu, Decai Yin, Jun Long, Zhiwen He, Huimin Sun, Ho Ching Xu, Huilin |
author_sort | Zhang, Qinqiu |
collection | PubMed |
description | Food recognition and weight estimation based on image methods have always been hotspots in the field of computer vision and medical nutrition, and have good application prospects in digital nutrition therapy and health detection. With the development of deep learning technology, image-based recognition technology has also rapidly extended to various fields, such as agricultural pests, disease identification, tumor marker recognition, wound severity judgment, road wear recognition, and food safety detection. This article proposes a non-wearable food recognition and weight estimation system (nWFWS) to identify the food type and food weight in the target recognition area via smartphones, so to assist clinical patients and physicians in monitoring diet-related health conditions. In addition, the system is mainly designed for mobile terminals; it can be installed on a mobile phone with an Android system or an iOS system. This can lower the cost and burden of additional wearable health monitoring equipment while also greatly simplifying the automatic estimation of food intake via mobile phone photography and image collection. Based on the system’s ability to accurately identify 1,455 food pictures with an accuracy rate of 89.60%, we used a deep convolutional neural network and visual-inertial system to collect image pixels, and 612 high-resolution food images with different traits after systematic training, to obtain a preliminary relationship model between the area of food pixels and the measured weight was obtained, and the weight of untested food images was successfully determined. There was a high correlation between the predicted and actual values. In a word, this system is feasible and relatively accurate for one automated dietary monitoring and nutritional assessment. |
format | Online Article Text |
id | pubmed-9709194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97091942022-12-01 Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system Zhang, Qinqiu He, Chengyuan Qin, Wen Liu, Decai Yin, Jun Long, Zhiwen He, Huimin Sun, Ho Ching Xu, Huilin Front Nutr Nutrition Food recognition and weight estimation based on image methods have always been hotspots in the field of computer vision and medical nutrition, and have good application prospects in digital nutrition therapy and health detection. With the development of deep learning technology, image-based recognition technology has also rapidly extended to various fields, such as agricultural pests, disease identification, tumor marker recognition, wound severity judgment, road wear recognition, and food safety detection. This article proposes a non-wearable food recognition and weight estimation system (nWFWS) to identify the food type and food weight in the target recognition area via smartphones, so to assist clinical patients and physicians in monitoring diet-related health conditions. In addition, the system is mainly designed for mobile terminals; it can be installed on a mobile phone with an Android system or an iOS system. This can lower the cost and burden of additional wearable health monitoring equipment while also greatly simplifying the automatic estimation of food intake via mobile phone photography and image collection. Based on the system’s ability to accurately identify 1,455 food pictures with an accuracy rate of 89.60%, we used a deep convolutional neural network and visual-inertial system to collect image pixels, and 612 high-resolution food images with different traits after systematic training, to obtain a preliminary relationship model between the area of food pixels and the measured weight was obtained, and the weight of untested food images was successfully determined. There was a high correlation between the predicted and actual values. In a word, this system is feasible and relatively accurate for one automated dietary monitoring and nutritional assessment. Frontiers Media S.A. 2022-11-16 /pmc/articles/PMC9709194/ /pubmed/36466396 http://dx.doi.org/10.3389/fnut.2022.965801 Text en Copyright © 2022 Zhang, He, Qin, Liu, Yin, Long, He, Sun and Xu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Nutrition Zhang, Qinqiu He, Chengyuan Qin, Wen Liu, Decai Yin, Jun Long, Zhiwen He, Huimin Sun, Ho Ching Xu, Huilin Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system |
title | Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system |
title_full | Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system |
title_fullStr | Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system |
title_full_unstemmed | Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system |
title_short | Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system |
title_sort | eliminate the hardware: mobile terminals-oriented food recognition and weight estimation system |
topic | Nutrition |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9709194/ https://www.ncbi.nlm.nih.gov/pubmed/36466396 http://dx.doi.org/10.3389/fnut.2022.965801 |
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