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Related Experiment Video

Updated: Aug 23, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

705

sEMG-Based Hand Posture Recognition and Visual Feedback Training for the Forearm Amputee.

Jongman Kim1, Sumin Yang1, Bummo Koo1

  • 1Department of Biomedical Engineering and Institute of Medical Engineering, Yonsei University, Wonju 26493, Korea.

Sensors (Basel, Switzerland)
|October 27, 2022
PubMed
Summary

Surface electromyography (sEMG) based gesture recognition improves with visual feedback training. This method reduces signal variability, enhancing accuracy for human-computer interaction and prosthetic control.

Keywords:
artificial neural networkforearm amputeehand posturepattern recognitionsurface electromyographyvisual feedback training

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Human-Computer Interaction

Background:

  • Surface electromyography (sEMG) based gesture recognition is crucial for human-computer interactions, particularly in rehabilitation and prosthetic control.
  • High variability in sEMG signals from untrained users often hinders the performance of recognition algorithms.

Purpose of the Study:

  • To develop a hand posture recognition algorithm using multichannel sEMG sensors.
  • To evaluate the effectiveness of radar plot-based visual feedback training in improving sEMG gesture recognition accuracy.

Main Methods:

  • Developed a hand posture recognition algorithm with multichannel sEMG sensors.
  • Employed radar plot-based visual feedback for training participants (healthy adults and a bilateral forearm amputee).
  • Trained artificial neural network classifiers using single and combined feature vectors.

Main Results:

  • Classification accuracy significantly improved in the bilateral forearm amputee after three days of training.
  • Visual feedback training effectively reduced sEMG signal variability, enhancing recognition performance.
  • The radar plot enabled a bilateral forearm amputee to participate in rehabilitation training.

Conclusions:

  • Radar plot-based visual feedback training is an efficient method for improving sEMG-based hand posture recognition.
  • This training approach can aid amputees in controlling electric prostheses and participating in rehabilitation.
  • The study highlights the potential of visual feedback to overcome sEMG signal variability challenges.