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Related Experiment Video

Updated: Oct 15, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Inter-classifier comparison for upper extremity EMG signal at different hand postures and arm positions using pattern

Ali Asghar1,2, Saad Jawaid Khan1, Fahad Azim2

  • 1Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Sindh, Pakistan.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine
|October 23, 2021
PubMed
Summary

Surface and intramuscular EMG signals enhance myoelectric control. This study found arm position and hand posture have no significant effect on classifier accuracy for pattern recognition, improving prosthetic control.

Keywords:
KNNLDAPattern recognitionSVMintramuscular EMGsurface EMG

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Signal Processing

Background:

  • Surface electromyography (sEMG) and intramuscular EMG signals are crucial for advanced pattern recognition and myoelectric control.
  • Limited data exists on how varying arm positions and hand postures influence EMG signal acquisition and classifier performance.

Purpose of the Study:

  • To investigate the impact of different arm positions and hand postures on EMG signal classification accuracy.
  • To identify a robust classifier for myoelectric control under fixed arm position (FAP) and fixed hand posture (FHP) scenarios.

Main Methods:

  • Twenty healthy males performed five motion classes (grasp, open, rest, extension, flexion) at four arm positions (0°, 45°, 90°, 135°).
  • Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) classifiers were evaluated.
  • Statistical analysis was performed using SPSS for pairwise comparisons.

Main Results:

  • No significant difference was found among SVM, KNN, and LDA classifiers.
  • SVM achieved the highest accuracy (75.35% at FAP, 58.32% at FHP).
  • KNN showed the highest accuracy (69.11% and 79.04%) when data was pooled for classifying different arm positions and hand postures, respectively.

Conclusions:

  • Changing arm position and hand posture does not significantly affect classifier accuracy in myoelectric control.
  • The findings suggest robustness in EMG-based pattern recognition across varied limb configurations.