An algorithm of walk phase estimation with only treadmill motor current

Eiichi Ohki*, Yasutaka Nakashima, Takeshi Ando, Masakatsu G. Fujie

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    10 Citations (Scopus)

    Abstract

    To develop a gait rehabilitation robot for hemiplegic patients, quantitative evaluations of patient ability is needed. Patient's walk phase, which includes time balance of stance and swing legs, is one of the most useful indexes. However, conventional methods measuring walk phase require laborious preparations. In this paper, a novel algorithm estimating walk phase on a treadmill by observing DC motor current is proposed. In comparison of this algorithm and conventional methods, it was verified that the proposed algorithm had as the same accuracy as foot switch. Moreover, the proposed algorithm could estimate stance phase in 0.2 (s) errors between measurements of force plate mostly (4 out of 5 healthy subjects). However, result from the 5th subject showed that the proposed algorithm had erroneously identified stance phase as swing phase when little body weight loaded on leg. This characteristic is often observed in hemiplegic gait. Therefore, the proposed algorithm might need improvement of motor current threshold. However, this algorithm had capable of estimating the time of loading body weight on leg, and thus could be useful as a quantitative evaluation tool.

    Original languageEnglish
    Title of host publication2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
    Pages4060-4066
    Number of pages7
    DOIs
    Publication statusPublished - 2009 Dec 11
    Event2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009 - St. Louis, MO
    Duration: 2009 Oct 112009 Oct 15

    Other

    Other2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
    CitySt. Louis, MO
    Period09/10/1109/10/15

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Vision and Pattern Recognition
    • Human-Computer Interaction
    • Control and Systems Engineering

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