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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Advances in surface EMG: recent progress in detection and processing techniques.

Roberto Merletti1, Matteo Aventaggiato, Alberto Botter

  • 1Laboratory for Engineering of the Neuromuscular System (LISiN), Department of Electronics, Politecnico di Torino, Italy. roberto.merletti@polito.it

Critical Reviews in Biomedical Engineering
|December 8, 2010
PubMed
Summary
This summary is machine-generated.

This review covers advances in surface electromyography (EMG) detection and processing. It details electrode-skin interface, signal acquisition, noise removal, and EMG-force relationships for future research.

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Signal Processing

Background:

  • Surface electromyography (sEMG) is crucial for understanding muscle activity.
  • Existing reviews have gaps in detailing current sEMG detection and processing techniques.
  • This article is the first part of a three-part review on sEMG.

Purpose of the Study:

  • To provide a state-of-the-art overview of surface EMG detection and processing techniques.
  • To identify areas for future research in sEMG signal analysis.
  • To cover advancements in the electrode-skin interface and signal acquisition.

Main Methods:

  • Review of current literature on surface EMG detection and processing.
  • Detailed examination of electrode-skin interface parameters (equivalent circuits, skin treatment, gels).
  • Analysis of signal detection modalities, spatial filters, amplifiers, and noise reduction techniques.

Main Results:

  • Comprehensive overview of the electrode-skin interface and its impact on signal quality.
  • Discussion of advanced techniques for noise removal, including power line interference and outlier detection.
  • Exploration of signal segmentation, decomposition into action potential trains, and the relationship between sEMG and force.

Conclusions:

  • Significant advancements have been made in sEMG detection and processing.
  • There are identified gaps in the current literature, highlighting opportunities for future research.
  • This review provides a foundation for understanding the current landscape and future directions in sEMG.