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International Conference on Intelligent Robotics and Applications

ICIRA 2016: Intelligent Robotics and Applications pp 247–256Cite as

A Study on Classification of Food Texture with Recurrent Neural Network

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9834))

Abstract

This study constructs a food texture evaluation system using a food texture sensor having sensor elements of 2 types. Characteristics of food are digitized by using the food texture sensor in imitation of the structure of the human tooth. Classification of foods is carried out by the recurrent neural network. The recurrent neural network receives the time-series outputs from the food texture sensor, and outputs classification signals. In the experiment, 3 kinds of food are classified by the recurrent neural network.

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Authors and Affiliations

  1. Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, 657-8501, Japan

    Shuhei Okada, Hiroyuki Nakamoto, Futoshi Kobayashi & Fumio Kojima

Authors
  1. Shuhei Okada
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  2. Hiroyuki Nakamoto
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  3. Futoshi Kobayashi
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  4. Fumio Kojima
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Corresponding author

Correspondence to Hiroyuki Nakamoto .

Editor information

Editors and Affiliations

  1. Tokyo Metropolitan University , Tokyo, Japan

    Naoyuki Kubota

  2. Kyushu University , Fukuoka, Japan

    Kazuo Kiguchi

  3. University of Portsmouth , Portsmouth, United Kingdom

    Honghai Liu

  4. Tokyo Metropolitan University , Tokyo, Japan

    Takenori Obo

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Okada, S., Nakamoto, H., Kobayashi, F., Kojima, F. (2016). A Study on Classification of Food Texture with Recurrent Neural Network. In: Kubota, N., Kiguchi, K., Liu, H., Obo, T. (eds) Intelligent Robotics and Applications. ICIRA 2016. Lecture Notes in Computer Science(), vol 9834. Springer, Cham. https://doi.org/10.1007/978-3-319-43506-0_21

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  • DOI: https://doi.org/10.1007/978-3-319-43506-0_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43505-3

  • Online ISBN: 978-3-319-43506-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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