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...
保存先:
| 主要な著者: | , , , |
|---|---|
| フォーマット: | Conference or Workshop Item |
| 言語: | English English |
| 出版事項: |
IEEE
2019
|
| 主題: | |
| オンライン･アクセス: | 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 |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
| 要約: | 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. |
|---|