Digital photography based food intake prediction using artificial neural network

Introduction Many wearable devices monitoring have been proposed to complement self-reporting of users’ caloric intake and eating behaviours. These devices comprise varying sensing modalities, such as acoustic, visual, inertial, EEG, EMG, capacitive and piezoelectric sensors. In this research, food...

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Main Authors:	Gunawan, Teddy Surya, Kartiwi, Mira
Format:	Article
Language:	English English
Published:	Malaysian Medical Association 2017
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/59505/ http://irep.iium.edu.my/59505/ http://irep.iium.edu.my/59505/1/MJM_v72-Supp-1-2017.pdf http://irep.iium.edu.my/59505/2/Teddy_FoodIntakeNN.pptx

id	iium-59505
recordtype	eprints
spelling	iium-595052017-11-21T06:50:26Z http://irep.iium.edu.my/59505/ Digital photography based food intake prediction using artificial neural network Gunawan, Teddy Surya Kartiwi, Mira TK Electrical engineering. Electronics Nuclear engineering Introduction Many wearable devices monitoring have been proposed to complement self-reporting of users’ caloric intake and eating behaviours. These devices comprise varying sensing modalities, such as acoustic, visual, inertial, EEG, EMG, capacitive and piezoelectric sensors. In this research, food intake will be predicted from the input of digital photography using ANN. Methods In this study, image of selected food or leftovers are captured using digital camera or smartphone. These two images are later compared with images of averaged portions of food. Area based comparison or trained artificial neural network could then predicted the calorie and nutrient intake. Results Preliminary results show the effectiveness of measuring food intake using digital photography. It is found that more images are required to train the artificial neural network for various image capturing position to improve the prediction accuracy. Discussion The proposed method is rather simple and easy and provides quick feedback on food intake and dietary recommendations to achieve weight loss goal. It is believe that such findings would allow general public to better achieve and maintain their healthy lifestyle. Malaysian Medical Association 2017-08 Article PeerReviewed application/pdf en http://irep.iium.edu.my/59505/1/MJM_v72-Supp-1-2017.pdf application/pdf en http://irep.iium.edu.my/59505/2/Teddy_FoodIntakeNN.pptx Gunawan, Teddy Surya and Kartiwi, Mira (2017) Digital photography based food intake prediction using artificial neural network. Malaysian Journal of Medicine, 72 (Supplement 1). p. 20. ISSN 0300-5283 http://www.e-mjm.org/2017/v72s1/index.html
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Gunawan, Teddy Surya Kartiwi, Mira Digital photography based food intake prediction using artificial neural network
description	Introduction Many wearable devices monitoring have been proposed to complement self-reporting of users’ caloric intake and eating behaviours. These devices comprise varying sensing modalities, such as acoustic, visual, inertial, EEG, EMG, capacitive and piezoelectric sensors. In this research, food intake will be predicted from the input of digital photography using ANN. Methods In this study, image of selected food or leftovers are captured using digital camera or smartphone. These two images are later compared with images of averaged portions of food. Area based comparison or trained artificial neural network could then predicted the calorie and nutrient intake. Results Preliminary results show the effectiveness of measuring food intake using digital photography. It is found that more images are required to train the artificial neural network for various image capturing position to improve the prediction accuracy. Discussion The proposed method is rather simple and easy and provides quick feedback on food intake and dietary recommendations to achieve weight loss goal. It is believe that such findings would allow general public to better achieve and maintain their healthy lifestyle.
format	Article
author	Gunawan, Teddy Surya Kartiwi, Mira
author_facet	Gunawan, Teddy Surya Kartiwi, Mira
author_sort	Gunawan, Teddy Surya
title	Digital photography based food intake prediction using artificial neural network
title_short	Digital photography based food intake prediction using artificial neural network
title_full	Digital photography based food intake prediction using artificial neural network
title_fullStr	Digital photography based food intake prediction using artificial neural network
title_full_unstemmed	Digital photography based food intake prediction using artificial neural network
title_sort	digital photography based food intake prediction using artificial neural network
publisher	Malaysian Medical Association
publishDate	2017
url	http://irep.iium.edu.my/59505/ http://irep.iium.edu.my/59505/ http://irep.iium.edu.my/59505/1/MJM_v72-Supp-1-2017.pdf http://irep.iium.edu.my/59505/2/Teddy_FoodIntakeNN.pptx
first_indexed	2023-09-18T21:24:19Z
last_indexed	2023-09-18T21:24:19Z
_version_	1777412089150701568

Digital photography based food intake prediction using artificial neural network

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