Study of the vibromyographic signal as a means for quantifying muscular effort.

The present work describes an effort to quantify the level of muscular activation by monitoring and processing muscular vibrations under isometric conditions. The history and nature of vibromyography (VMG) are examined, and a brief review of current VMG literature is presented. In addition, prelimin...

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Published in:Hong Kong Physiotherapy Journal Vol. 18; no. 1; pp. 33 - 37
Main Authors: Sarver JJ, Seliktar R
Format: research tables/charts Journal Article
Published: Elsevier B.V. 2000
Online Access:View this record in EBSCOhost
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      pub: Elsevier B.V.
      place: Philadelphia, Pennsylvania
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        atl: Study of the vibromyographic signal as a means for quantifying muscular effort.
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        au:
          Sarver JJ
          Seliktar R
        affil: Biomedical Engineering, Drexel University, 3200 Chestnut St, Philadelphia, PA 19104
      sug:
        subj:
          Electromyography Methods
          Muscle, Skeletal Physiology
          Signal Processing, Computer Assisted
          Human
      ab: The present work describes an effort to quantify the level of muscular activation by monitoring and processing muscular vibrations under isometric conditions. The history and nature of vibromyography (VMG) are examined, and a brief review of current VMG literature is presented. In addition, preliminary results from an ongoing study are presented. Although several subjects were included in this feasibility study, the results from only one subject are presented here. The subject was asked to reach 25°%, 50%, 75%, and 100% of his maximum isometric elbow extension. A uniaxial accelerometer was placed over his right triceps brachii and monitored the transverse component of the underlying muscle's acceleration. Electromyographic (EMG) data were also recorded from the long and lateral heads of the same muscle. Results were then processed using traditional algorithms and the ability of VMG and EMG to discriminate among the four different effort levels were compared. The data indicated that VMG was able to discriminate between the 75% and 100% effort levels better than EMG. Currently, this protocol is being applied to additional subjects using more advanced signal processing as well as discriminant analyses to improve effort level discrimination as well as fatigue detection.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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