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...
| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
IEEE
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/72546/ 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 |
| _version_ | 1848787631512485888 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T17:28:00Z |
| format | Proceeding Paper |
| id | iium-72546 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:28:00Z |
| publishDate | 2019 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-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 Proceeding Paper 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 |
| spellingShingle | T Technology (General) Gunawan, Teddy Surya Kartiwi, Mira Abdul Malik, Noreha Ismail, Nanang Food intake calorie prediction using generalized regression neural network |
| title | 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_short | Food intake calorie prediction using generalized regression neural network |
| title_sort | food intake calorie prediction using generalized regression neural network |
| topic | T Technology (General) |
| url | http://irep.iium.edu.my/72546/ http://irep.iium.edu.my/72546/ http://irep.iium.edu.my/72546/ 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 |