Developing a new foot muscle model of gait using a Bayesian network

Jun Inoue, Kazuya Kawamura, Masakatsu G. Fujie

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

    2 Citations (Scopus)

    Abstract

    In this paper, by means of a statistical method, we use sole pressure on each part, angle of joint, femoral and crural muscle activities to produce a new foot muscle activity model for use in the design of ankle-foot orthoses. We built a Bayesian network model[1] by examining the normal gait of a nondisabled subject. We measured the activity of the lower foot muscles using electromyography, joint angles and the pressure on different parts of the sole. From these data, we built three models, representing the stance phase, the control phase and the propulsive phase. The accuracy of these models was confirmed. The largest feature of this model is making every 10% level nodes of each measurement data. Normal Bayesian network can estimate only muscle active or not active. But this method can estimate activity level of muscle. From this feature this method has three advantages. First, our use of 10% increments in the levels of the measured factors enabled changes in these factors during gait to be reflected in the model. Second, variations in the influence of factors that differ between low and high muscle activity are represented. Third, it is easier to use than physical models; three-dimensional motion analysis is not required and the method is convenient for clinical use. In an evaluation of this model, we confirmed that this model can estimate all muscular activity level with an accuracy rate greater than 95%.

    Original languageEnglish
    Title of host publicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
    Pages3257-3262
    Number of pages6
    DOIs
    Publication statusPublished - 2012
    Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
    Duration: 2012 Oct 142012 Oct 17

    Other

    Other2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
    CountryKorea, Republic of
    CitySeoul
    Period12/10/1412/10/17

    Fingerprint

    Bayesian networks
    Muscle
    Electromyography
    Phase control
    Statistical methods

    Keywords

    • Bayesian estimation
    • electromyography
    • muscle model
    • orthosis

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Control and Systems Engineering
    • Human-Computer Interaction

    Cite this

    Inoue, J., Kawamura, K., & Fujie, M. G. (2012). Developing a new foot muscle model of gait using a Bayesian network. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 3257-3262). [6378293] https://doi.org/10.1109/ICSMC.2012.6378293

    Developing a new foot muscle model of gait using a Bayesian network. / Inoue, Jun; Kawamura, Kazuya; Fujie, Masakatsu G.

    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2012. p. 3257-3262 6378293.

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

    Inoue, J, Kawamura, K & Fujie, MG 2012, Developing a new foot muscle model of gait using a Bayesian network. in Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics., 6378293, pp. 3257-3262, 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012, Seoul, Korea, Republic of, 12/10/14. https://doi.org/10.1109/ICSMC.2012.6378293
    Inoue J, Kawamura K, Fujie MG. Developing a new foot muscle model of gait using a Bayesian network. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2012. p. 3257-3262. 6378293 https://doi.org/10.1109/ICSMC.2012.6378293
    Inoue, Jun ; Kawamura, Kazuya ; Fujie, Masakatsu G. / Developing a new foot muscle model of gait using a Bayesian network. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2012. pp. 3257-3262
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