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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Few-shot prototype adaptation for generalizable electromyography gesture recognition.

Hunmin Lee1, Brian Lim2, Ming Jiang3

  • 1Department of Computer Science, University of Minnesota, Minneapolis, 55455, USA. lee03915@umn.edu.

Scientific Reports
|March 7, 2026
PubMed
Summary
This summary is machine-generated.

EMG-Adapt enhances electromyography (EMG) gesture recognition using few-shot learning. This framework requires less calibration data for robust and efficient human-computer interfaces.

Keywords:
ElectromyographyFew-shot learningGesture recognitionHuman-computer interactionProsthetic controlPrototype learning

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Electromyography (EMG) based gesture recognition is crucial for advanced human-computer interfaces.
  • Current methods often require extensive user-specific calibration data, limiting practical application.
  • Few-shot learning presents a promising avenue for reducing data requirements.

Purpose of the Study:

  • To introduce EMG-Adapt, a novel framework for robust and data-efficient few-shot electromyography (EMG) gesture recognition.
  • To improve the generalization capabilities of EMG-based systems across different users and sessions.
  • To reduce the amount of labeled data needed for effective gesture recognition.

Main Methods:

  • Developed a few-shot prototype adaptation framework integrating prototype learning and meta-learning.
  • Implemented a cepstrum coefficient average feature extraction method for noise reduction.
  • Utilized deep prototype learning with hybrid loss functions and a meta-learning strategy for efficient adaptation.

Main Results:

  • EMG-Adapt significantly improved few-shot gesture recognition performance across five public EMG datasets.
  • Achieved state-of-the-art results in cross-session and cross-user generalization.
  • Demonstrated substantial reduction in required calibration data compared to conventional methods.
  • Maintained computational efficiency.

Conclusions:

  • EMG-Adapt offers a significant advancement for practical, user-friendly, and scalable EMG-based human-computer interfaces.
  • The framework shows strong potential for applications in prosthetics, assistive technologies, and virtual reality.
  • Future work will focus on self-supervised learning and online adaptation for enhanced real-world robustness.