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Tutorial. Surface electromyogram (sEMG) amplitude estimation: Best practices.

Edward A Clancy1, Evelyn L Morin2, Gelareh Hajian3

  • 1Worcester Polytechnic Institute, Worcester, MA, USA.

Journal of Electromyography and Kinesiology : Official Journal of the International Society of Electrophysiological Kinesiology
|August 8, 2023
PubMed
Summary
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This tutorial guides novices in extracting electromyogram (EMG) amplitude from surface EMG signals during voluntary contractions. It details best practices for accurate sEMG envelope calculation, crucial for understanding muscle neural excitation.

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Rehabilitation Science

Background:

  • Surface electromyogram (sEMG) amplitude quantifies muscle neural excitation.
  • Novices require clear instructions for sEMG amplitude extraction.
  • Prior tutorials covered sEMG signal origins and recording basics.

Purpose of the Study:

  • Provide accessible instructions and best practices for sEMG amplitude extraction.
  • Target clinicians, engineers, and non-engineers new to sEMG analysis.
  • Offer insights into high-density sEMG and electrically elicited contractions.

Main Methods:

  • Review physiological origins of voluntary sEMG signals.
  • Describe sEMG recording, conditioning, and analog-to-digital conversion.
Keywords:
ARV EMGCMAPEMG amplitudeEMG envelopeElectromyogramHDsEMGMAV EMGM−waveRMS EMGTeachingTutorial

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  • Detail state-of-the-art methods for computing sEMG amplitude, including pre-conditioning and noise attenuation.
  • Main Results:

    • Guidance on signal pre-conditioning for accurate amplitude estimation.
    • Comparison of absolute value vs. square-law detection methods.
    • Selection criteria for appropriate sEMG amplitude smoothing filters and noise reduction techniques.

    Conclusions:

    • Best practices for reliable sEMG amplitude estimation are provided.
    • The tutorial aims to demystify sEMG amplitude extraction for a wider audience.
    • Accurate sEMG amplitude is vital for understanding muscle function and peripheral parameters.