The Proceedings of the Symposium on Micro-Nano Science and Technology
Online ISSN : 2432-9495
2019.10
Session ID : 21pm1PN320
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Identification of tactile samples corresponding to each sensory evaluation using machine learning
*Shuto YamanakaKeiichiro YanagibashiNorihisa Miki
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Abstract

Humans t actile sensation is composed of tactile elements such as roughness, hardness, wetness and friction sensation. How humans feel the tactile sensation is strongly dependent on the human cognitive characteristics. In this study, we considered that there is a tendency specific to each person in how they receive each tactile element and its combination. As an experiment, sensory evaluation was conducted on each tactile sensation using tactile samples with different micro grooves. By using machine learning to the results, we made it possible to identify tactile samples with convex widths and pitches corresponding to each sensory evaluation based on a tendency specific to each participant.

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© 2019 The Japan Society of Mechanical Engineers
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