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Food intake calorie prediction using generalized regression neural network

Many devices have been proposed to monitor the calorie intake and eating behaviors. These wearable devices uses various sensing modalities, such as acoustic, visual, inertial, EEG (electroglottography), EMG (electromyography), capacitive and piezoelectric sensors. In this paper, Generalized Regr...

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Main Authors: Gunawan, Teddy Surya, Kartiwi, Mira, Abdul Malik, Noreha, Ismail, Nanang
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2019
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/72546/1/72546%20Food%20Intake%20Calorie%20Prediction.pdf
http://irep.iium.edu.my/72546/2/72546%20Food%20Intake%20Calorie%20Prediction%20SCOPUS.pdf
http://irep.iium.edu.my/72546/
https://ieeexplore.ieee.org/document/8688787
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spelling my.iium.irep.725462019-06-12T02:03:36Z http://irep.iium.edu.my/72546/ Food intake calorie prediction using generalized regression neural network Gunawan, Teddy Surya Kartiwi, Mira Abdul Malik, Noreha Ismail, Nanang T Technology (General) Many devices have been proposed to monitor the calorie intake and eating behaviors. These wearable devices uses various sensing modalities, such as acoustic, visual, inertial, EEG (electroglottography), EMG (electromyography), capacitive and piezoelectric sensors. In this paper, Generalized Regression Neural Network (GRNN) will be utilized to predict the food intake calorie from the input of digital image. GRNN was utilized due its fast training compared to standard feedforward networks. The food image database comprises of 568 food including sweet, savory, processed, whole foods, and beverages. The calorie has the ranged from 0 kcal (plain water) to 11830 (roasted goose) with median 235.5 kcal. The optimum spread parameter for GRNN was found to be 0.46 when the 568 images was distributed randomly, i.e. 80% training and 20% testing. Due to very large variation of the calorie needs to be predicted, GRNN has rather large prediction error. This could be alleviated using more training data, use other features like texture and segmentation, or deep neural network. IEEE 2019-04-15 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/72546/1/72546%20Food%20Intake%20Calorie%20Prediction.pdf application/pdf en http://irep.iium.edu.my/72546/2/72546%20Food%20Intake%20Calorie%20Prediction%20SCOPUS.pdf Gunawan, Teddy Surya and Kartiwi, Mira and Abdul Malik, Noreha and Ismail, Nanang (2019) Food intake calorie prediction using generalized regression neural network. In: 2018 IEEE 5th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), 28th-30th November 2018, Songkla, Thailand. https://ieeexplore.ieee.org/document/8688787 10.1109/ICSIMA.2018.8688787
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T Technology (General)
spellingShingle T Technology (General)
Gunawan, Teddy Surya
Kartiwi, Mira
Abdul Malik, Noreha
Ismail, Nanang
Food intake calorie prediction using generalized regression neural network
description Many devices have been proposed to monitor the calorie intake and eating behaviors. These wearable devices uses various sensing modalities, such as acoustic, visual, inertial, EEG (electroglottography), EMG (electromyography), capacitive and piezoelectric sensors. In this paper, Generalized Regression Neural Network (GRNN) will be utilized to predict the food intake calorie from the input of digital image. GRNN was utilized due its fast training compared to standard feedforward networks. The food image database comprises of 568 food including sweet, savory, processed, whole foods, and beverages. The calorie has the ranged from 0 kcal (plain water) to 11830 (roasted goose) with median 235.5 kcal. The optimum spread parameter for GRNN was found to be 0.46 when the 568 images was distributed randomly, i.e. 80% training and 20% testing. Due to very large variation of the calorie needs to be predicted, GRNN has rather large prediction error. This could be alleviated using more training data, use other features like texture and segmentation, or deep neural network.
format Conference or Workshop Item
author Gunawan, Teddy Surya
Kartiwi, Mira
Abdul Malik, Noreha
Ismail, Nanang
author_facet Gunawan, Teddy Surya
Kartiwi, Mira
Abdul Malik, Noreha
Ismail, Nanang
author_sort Gunawan, Teddy Surya
title Food intake calorie prediction using generalized regression neural network
title_short Food intake calorie prediction using generalized regression neural network
title_full Food intake calorie prediction using generalized regression neural network
title_fullStr Food intake calorie prediction using generalized regression neural network
title_full_unstemmed Food intake calorie prediction using generalized regression neural network
title_sort food intake calorie prediction using generalized regression neural network
publisher IEEE
publishDate 2019
url http://irep.iium.edu.my/72546/1/72546%20Food%20Intake%20Calorie%20Prediction.pdf
http://irep.iium.edu.my/72546/2/72546%20Food%20Intake%20Calorie%20Prediction%20SCOPUS.pdf
http://irep.iium.edu.my/72546/
https://ieeexplore.ieee.org/document/8688787
_version_ 1643620162967961600
score 13.244368

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