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

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.