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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...

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Autores principales: Zhang, Qinqiu, He, Chengyuan, Qin, Wen, Liu, Decai, Yin, Jun, Long, Zhiwen, He, Huimin, Sun, Ho Ching, Xu, Huilin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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