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Robotics 2017, 6(4), 37; doi:10.3390/robotics6040037

Convolutional Neural Network based Estimation of Gel-like Food Texture by a Robotic Sensing System

1
Department of Mechanical Engineering, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita 565-0871, Japan
2
San-Ei Gen F.F.I., Inc., 1-1-11 Sanwa-cho, Toyonaka 561-8588, Japan
*
Author to whom correspondence should be addressed.
Received: 31 October 2017 / Revised: 24 November 2017 / Accepted: 29 November 2017 / Published: 1 December 2017
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Abstract

This paper presents a robotic sensing system that evaluates the texture of gel-like food, in which not only mechanical characteristics, but also geometrical characteristics of the texture are objectively and quantitatively evaluated. When a human chews a gel-like food, the person perceives the changes in the shape and contact force simultaneously on the tongue. Based on their impression, they evaluate the texture. To reproduce this procedure using a simple artificial mastication robot, the pressure distribution of the gel-like food is measured, and the information associated with both the geometrical and mechanical characteristics is simultaneously acquired. The relationship between the value of the human sensory evaluation of the texture and the pressure distribution image is then modeled by applying a convolutional neural network. Experimental results show that the proposed system succeeds in estimating the values of a human sensory evaluation for 23 types of gel-like food with a coefficient of determination greater than 0.92. View Full-Text
Keywords: food texture sensing; pressure distribution measurement; convolutional neural network; artificial mastication food texture sensing; pressure distribution measurement; convolutional neural network; artificial mastication
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Shibata, A.; Ikegami, A.; Nakauma, M.; Higashimori, M. Convolutional Neural Network based Estimation of Gel-like Food Texture by a Robotic Sensing System. Robotics 2017, 6, 37.

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