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Visualizing Road Condition Information by Applying the AutoEncoder to Wheelchair Sensing Data for Road Barrier Assessment

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Advances in Artificial Intelligence (JSAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1357))

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Abstract

Providing accessibility information about sidewalks for people with difficulties with moving is an important social issue. Visualizing road surface conditions to show the accessibilities of the road is effective for this issue. However, conventional methods of collecting huge area accessibility information are based on manpower and have the problem of the large costs of time and money. To solve this problem, we have been proposing and implementing a system for estimating road surface conditions by machine learning with measured values of an acceleration sensor attached to wheelchairs. This paper examined the appropriateness of reconstruction errors which are calculated by Convolutional Variational AutoEncoder to assess the degree of road burden suitable for each user. The evaluation was conducted by calculating reconstruction errors from the traveling data of 14 wheelchair users and creating a map that reflects the information of the calculated errors. The evaluation results suggest that reconstruction errors can reflect the degree of the burden on each wheelchair user during traveling.

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Acknowledgments

We would like to thank all participants who helped to collect the sensing data. This study was supported by Tateishi Science and Technology Foundation in FY 2011–2012, the research grant by Chiyoda-ku (CHIYODAGAKU) in FY 2014–2016, and JSPS KAKENHI Grant-in-Aid for Scientific Research (B) Number 17H01946 and 20H04476.

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Authors and Affiliations

  1. Graduate School of Science and Engineering, Sophia University, 7-1 Kioicho, Chiyoda-ku, Tokyo, 102-8554, Japan

    Goh Sato, Takumi Watanabe, Hiroki Takahashi, Yojiro Yano & Ikuko Eguchi Yairi

  2. Graduate School of Technology Management for Innovation, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

    Yusuke Iwasawa

Authors
  1. Goh Sato
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  2. Takumi Watanabe
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  3. Hiroki Takahashi
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  4. Yojiro Yano
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  5. Yusuke Iwasawa
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  6. Ikuko Eguchi Yairi
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Corresponding author

Correspondence to Goh Sato .

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Editors and Affiliations

  1. Kansai University, Suita, Osaka, Japan

    Katsutoshi Yada

  2. Department of Applied Computer Science, Tokyo Polytechnic University, Atsugi, Kanagawa, Japan

    Daisuke Katagami

  3. Graduate School of System Design, Tokyo Metropolitan University, Hino, Tokyo, Japan

    Yasufumi Takama

  4. Department of Social Informatics, Kyoto University, Kyoto, Japan

    Takayuki Ito

  5. Division of Behavioral Science, Faculty of Letters, Chiba University, Chiba, Chiba, Japan

    Akinori Abe

  6. Department of Computer Science, Graduate School of System Design, Tokyo Metropolitan University, Hino, Tokyo, Japan

    Eri Sato-Shimokawara

  7. Mathematics and Informatics Center, The University of Tokyo, Tokyo, Japan

    Junichiro Mori

  8. Graduate School of Economics, Osaka University, Toyonaka, Osaka, Japan

    Naohiro Matsumura

  9. Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan

    Hisashi Kashima

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Sato, G., Watanabe, T., Takahashi, H., Yano, Y., Iwasawa, Y., Yairi, I.E. (2021). Visualizing Road Condition Information by Applying the AutoEncoder to Wheelchair Sensing Data for Road Barrier Assessment. In: Yada, K., et al. Advances in Artificial Intelligence. JSAI 2020. Advances in Intelligent Systems and Computing, vol 1357. Springer, Cham. https://doi.org/10.1007/978-3-030-73113-7_2

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