Logistic regression analysis of multiple interosseous hand-muscle activities using surface electromyography during finger-oriented tasks

Masayuki Yokoyama, Masao Yanagisawa

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    1 Citation (Scopus)


    Intrinsic hand muscles are densely located in the hand, and the myoelectric observation from the surface is sometimes unreliable because of some outside influences that may interfere with the signals. In the present study, we evaluated the activities of multiple interosseous hand-muscles which densely located in the hand, through analyzing the surface electromyographic signals during finger-oriented tasks using univariate and multivariate logistic regression models. Ten healthy subjects participated in our experiment, and isometrically exercised each finger one by one in flexed form. The result of a univariate analysis with the power and amplitude domain predictor variables of the surface electromyographic signals showed significant consistency between the activated finger and the inserted finger of the dorsal interosseous muscles to the proximal phalanx (P < 0.001). Meanwhile, the results of a multivariate analysis showed a higher correlation of the regression model of the fourth dorsal interosseous muscle during the action of the ring finger using frequency-domain variables (the Nagelkerke R2 = 0.716 when the median frequency was used), compared to the model without the frequency-domain variables (the Nagelkerke R2 = 0.583). Our result showed that the logistic regression models have a particular possibility for the analysis of the surface electromyographic signals of densely located hand-muscle activities related to the finger-oriented tasks.

    Original languageEnglish
    Pages (from-to)117-123
    Number of pages7
    JournalJournal of Electromyography and Kinesiology
    Publication statusPublished - 2019 Feb 1



    • Interosseous hand muscle
    • Logistic regression analysis
    • Muscle analysis
    • Surface electromyography

    ASJC Scopus subject areas

    • Neuroscience (miscellaneous)
    • Biophysics
    • Clinical Neurology

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