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Front Caster Capable of Reducing Horizontal Forces on Step Climbing

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Book cover Big Data Analysis and Deep Learning Applications (ICBDL 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 744))

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Abstract

Obstacles such as ramps, steps and irregular floor surfaces are commonly encountered in homes, offices and other public spaces. These obstacles frequently limit the daily activities of people who use mobility aids. In this paper, a new offsetting mechanism for climbing a step while reducing the required horizontal climbing force and its step-climbing wheel prototype are proposed. Specifically, the proposed wheel allows to integrate into an existing personal assist devices. The proposed step-climbing wheel can help to reduce this limitation. The physical and mental burdens of caregivers and medical staff can also be reduced by making the users of the mechanism more self-sufficient. This paper provides details on this offsetting mechanism and its prototype. The mechanism is analyzed mathematically and its functionality is verified by experiments using a prototype.

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Acknowledgements

This work is partially supported by the Research Grant-A awarded by the Tateisi Science and Technology Foundation.

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Authors and Affiliations

  1. Department of Environmental Robotics, University of Miyazaki, 1-1 Gakuen Kibanadai-nishi, Miyazaki, 889-2192, Japan

    Geunho Lee, Masaki Shiraishi, Hiroki Tamura & Kikuhito Kawasue

  2. Ohnuma Robo & Design LLC, Miyazaki, Japan

    Geunho Lee

Authors
  1. Geunho Lee
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  2. Masaki Shiraishi
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  3. Hiroki Tamura
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  4. Kikuhito Kawasue
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Corresponding author

Correspondence to Geunho Lee .

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Editors and Affiliations

  1. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Thi Thi Zin

  2. Department of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences (HVL), Bergen, Norway

    Jerry Chun-Wei Lin

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© 2019 Springer Nature Singapore Pte Ltd.

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Lee, G., Shiraishi, M., Tamura, H., Kawasue, K. (2019). Front Caster Capable of Reducing Horizontal Forces on Step Climbing. In: Zin, T., Lin, JW. (eds) Big Data Analysis and Deep Learning Applications. ICBDL 2018. Advances in Intelligent Systems and Computing, vol 744. Springer, Singapore. https://doi.org/10.1007/978-981-13-0869-7_26

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