Directional control of an omnidirectional walking support walker: Adaptation to individual differences with fuzzy learning

Yinlai Jiang; Shuoyu Wang; Kenji Ishida; Yo Kobayashi; Masakatsu G. Fujie

doi:10.1080/01691864.2013.876935

Directional control of an omnidirectional walking support walker: Adaptation to individual differences with fuzzy learning

Yinlai Jiang^*, Shuoyu Wang, Kenji Ishida, Yo Kobayashi, Masakatsu G. Fujie

^*Corresponding author for this work

Waseda Research Institute for Science and Engineering

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

Abstract

We are developing a method to recognize a users directional intention to control an omnidirectional walker (ODW) according to the force interaction between the ODW and the user. Since the characteristics in the force interaction are different among persons especially for those with walking difficulty, a fuzzy learning method is developed in this study to adapt to the individual difference in forearm pressures in order to improve the usability of the method. The experiment results show that fuzzy learning can significantly improve the accuracy of recognition by updating the fuzzy rules according to the characteristics in the force interaction.

Original language	English
Pages (from-to)	479-485
Number of pages	7
Journal	Advanced Robotics
Volume	28
Issue number	7
DOIs	https://doi.org/10.1080/01691864.2013.876935
Publication status	Published - 2014 Apr 3

Keywords

directional control
forearm pressure
fuzzy learning
human robot interface
omnidirectional walker

ASJC Scopus subject areas

Control and Systems Engineering
Human-Computer Interaction
Computer Science Applications
Hardware and Architecture
Software

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10.1080/01691864.2013.876935

Cite this

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abstract = "We are developing a method to recognize a users directional intention to control an omnidirectional walker (ODW) according to the force interaction between the ODW and the user. Since the characteristics in the force interaction are different among persons especially for those with walking difficulty, a fuzzy learning method is developed in this study to adapt to the individual difference in forearm pressures in order to improve the usability of the method. The experiment results show that fuzzy learning can significantly improve the accuracy of recognition by updating the fuzzy rules according to the characteristics in the force interaction.",

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AU - Kobayashi, Yo

AU - Fujie, Masakatsu G.

PY - 2014/4/3

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N2 - We are developing a method to recognize a users directional intention to control an omnidirectional walker (ODW) according to the force interaction between the ODW and the user. Since the characteristics in the force interaction are different among persons especially for those with walking difficulty, a fuzzy learning method is developed in this study to adapt to the individual difference in forearm pressures in order to improve the usability of the method. The experiment results show that fuzzy learning can significantly improve the accuracy of recognition by updating the fuzzy rules according to the characteristics in the force interaction.

AB - We are developing a method to recognize a users directional intention to control an omnidirectional walker (ODW) according to the force interaction between the ODW and the user. Since the characteristics in the force interaction are different among persons especially for those with walking difficulty, a fuzzy learning method is developed in this study to adapt to the individual difference in forearm pressures in order to improve the usability of the method. The experiment results show that fuzzy learning can significantly improve the accuracy of recognition by updating the fuzzy rules according to the characteristics in the force interaction.

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KW - forearm pressure

KW - fuzzy learning

KW - human robot interface

KW - omnidirectional walker

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Directional control of an omnidirectional walking support walker: Adaptation to individual differences with fuzzy learning

Abstract

Keywords

ASJC Scopus subject areas

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