Auto-regression analysis upon EMG power spectra during dynamic exercise

Akira Nagata, Masami Miyazaki

    Research output: Contribution to journalArticle

    Abstract

    Surface electromyograms were recorded as interferance signals, which contained many sorts of motor units during dynamic exercise. Those complex signals have been transformed to a simple power spectra, that have been used as the index of muscle fatigue or recruitment of the motor unit. This study applies the Auto-Regression Model (ARM) for those EMGs Power Spectra, and proposes a new classification of motor units. As the dynamic exercise, the cranking movement of the upper limb or the pedalling movement of the lower limb were used. At the result of analysis, EMGs power spectra were divided into three elements of the low (5-35 Hz), middle(36-70 Hz), and high(over 76 Hz) frequency bands with computer caliculation of ARM.

    Original languageEnglish
    Pages (from-to)1061
    Number of pages1
    JournalJournal of Biomechanics
    Volume22
    Issue number10
    Publication statusPublished - 1989

    Fingerprint

    Neurophysiological Recruitment
    Muscle Fatigue
    Electromyography
    Power spectrum
    Upper Extremity
    Regression analysis
    Foot
    Lower Extremity
    Regression Analysis
    Frequency bands
    Muscle
    Fatigue of materials

    ASJC Scopus subject areas

    • Orthopedics and Sports Medicine

    Cite this

    Auto-regression analysis upon EMG power spectra during dynamic exercise. / Nagata, Akira; Miyazaki, Masami.

    In: Journal of Biomechanics, Vol. 22, No. 10, 1989, p. 1061.

    Research output: Contribution to journalArticle

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