Food intake calorie prediction using generalized regression neural network

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.

	PDF - Published Version Restricted to Registered users only Download (1MB) | Request a copy
	PDF (SCOPUS) - Supplemental Material Restricted to Registered users only Download (187kB) | Request a copy

Abstract

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.

Item Type:	Conference or Workshop Item (Plenary Papers)
Additional Information:	5588/72546
Uncontrolled Keywords:	Keywords—digital photography; food intake; Generalized Regression Neural Network; Optimum Spread Parameter
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Prof.Ts.Dr Mira Kartiwi
Date Deposited:	12 Jun 2019 10:03
Last Modified:	12 Jun 2019 10:03
URI:	http://irep.iium.edu.my/id/eprint/72546

Actions (login required)

View Item