Mobile Application for Controlling a Healthy Diet in Peru Using Image Recognition

Overweight is one of the big ills that affects the world population, especially the Peruvian population, and this is caused mainly by peoples ignorance of the amounts and nutritional values to consume according to their current condition. For this, there are various solutions focused on controlling...

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Autores principales:	Leonardo Cornejo, Rosa Urbano, Willy Ugarte
Formato:	article
Lenguaje:	EN
Publicado:	FRUCT 2021
Materias:	mobile app image recognition food classification transfer learning peruvian food Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/16714337ba7b454f9342390edb811980
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spelling	oai:doaj.org-article:16714337ba7b454f9342390edb8119802021-11-20T15:59:33ZMobile Application for Controlling a Healthy Diet in Peru Using Image Recognition2305-72542343-073710.23919/FRUCT53335.2021.9599959https://doaj.org/article/16714337ba7b454f9342390edb8119802021-10-01T00:00:00Zhttps://www.fruct.org/publications/fruct30/files/Cor.pdfhttps://doaj.org/toc/2305-7254https://doaj.org/toc/2343-0737Overweight is one of the big ills that affects the world population, especially the Peruvian population, and this is caused mainly by peoples ignorance of the amounts and nutritional values to consume according to their current condition. For this, there are various solutions focused on controlling a healthy diet for people, among the best known are the mobile food control applications such as MyFitnessPal, Fat Secret and MyNetDiary. These apps are quite useful for monitoring peoples food intake, as their databases have lots of food nutrition information. However, most of the information they have is focused on a foreign public, which may have different eating habits than Peruvians. That is why we present the application NutriCAM, which monitors the consumption of meals by users and provides the functionality of image recognition for meals, for the user to have a more friendly way to record and monitor its consumption, mainly focused on Peruvian gastronomy. The results are a Peruvian food recognition model based on the training of the pre-trained Convolutional Neural Network ResNet-50 and a dataset of 3600 food images, and a mobile application focused on the control of nutrition that caused 70% of improvement or maintenance in the current condition of 10 users.Leonardo CornejoRosa UrbanoWilly UgarteFRUCTarticlemobile appimage recognitionfood classificationtransfer learningperuvian foodTelecommunicationTK5101-6720ENProceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 30, Iss 1, Pp 32-41 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	mobile app image recognition food classification transfer learning peruvian food Telecommunication TK5101-6720
spellingShingle	mobile app image recognition food classification transfer learning peruvian food Telecommunication TK5101-6720 Leonardo Cornejo Rosa Urbano Willy Ugarte Mobile Application for Controlling a Healthy Diet in Peru Using Image Recognition
description	Overweight is one of the big ills that affects the world population, especially the Peruvian population, and this is caused mainly by peoples ignorance of the amounts and nutritional values to consume according to their current condition. For this, there are various solutions focused on controlling a healthy diet for people, among the best known are the mobile food control applications such as MyFitnessPal, Fat Secret and MyNetDiary. These apps are quite useful for monitoring peoples food intake, as their databases have lots of food nutrition information. However, most of the information they have is focused on a foreign public, which may have different eating habits than Peruvians. That is why we present the application NutriCAM, which monitors the consumption of meals by users and provides the functionality of image recognition for meals, for the user to have a more friendly way to record and monitor its consumption, mainly focused on Peruvian gastronomy. The results are a Peruvian food recognition model based on the training of the pre-trained Convolutional Neural Network ResNet-50 and a dataset of 3600 food images, and a mobile application focused on the control of nutrition that caused 70% of improvement or maintenance in the current condition of 10 users.
format	article
author	Leonardo Cornejo Rosa Urbano Willy Ugarte
author_facet	Leonardo Cornejo Rosa Urbano Willy Ugarte
author_sort	Leonardo Cornejo
title	Mobile Application for Controlling a Healthy Diet in Peru Using Image Recognition
title_short	Mobile Application for Controlling a Healthy Diet in Peru Using Image Recognition
title_full	Mobile Application for Controlling a Healthy Diet in Peru Using Image Recognition
title_fullStr	Mobile Application for Controlling a Healthy Diet in Peru Using Image Recognition
title_full_unstemmed	Mobile Application for Controlling a Healthy Diet in Peru Using Image Recognition
title_sort	mobile application for controlling a healthy diet in peru using image recognition
publisher	FRUCT
publishDate	2021
url	https://doaj.org/article/16714337ba7b454f9342390edb811980
work_keys_str_mv	AT leonardocornejo mobileapplicationforcontrollingahealthydietinperuusingimagerecognition AT rosaurbano mobileapplicationforcontrollingahealthydietinperuusingimagerecognition AT willyugarte mobileapplicationforcontrollingahealthydietinperuusingimagerecognition
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Mobile Application for Controlling a Healthy Diet in Peru Using Image Recognition

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