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Updated: Jun 6, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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SR-FSL: Sample reconstruction enhanced few-shot learning for real-time motor unit identification from surface

Yunfei Liu1, Zhaoyang Sheng1, Dongfang Li1

  • 1School of Microelectronics, University of Science and Technology of China, Hefei, 230027, Anhui, China.

Journal of Neuroengineering and Rehabilitation
|June 5, 2026
PubMed
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This study introduces a new few-shot learning method for online motor unit (MU) identification using high-density surface electromyogram (HD-sEMG). The approach achieves 93% accuracy with minimal data, improving deep learning applications.

Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Neuroscience

Background:

  • Deep learning (DL) shows potential for online motor unit (MU) identification from high-density surface electromyogram (HD-sEMG).
  • Current DL methods require substantial data, limiting practical application in real-time scenarios.

Purpose of the Study:

  • To develop a data-efficient few-shot learning method for online MU identification.
  • To enhance DL model performance using a novel sample reconstruction strategy for minimal experimental data.

Main Methods:

  • A spatio-temporal neural network was pre-trained on simulated HD-sEMG data.
  • A sample reconstruction strategy generated synthetic data to fine-tune the model with limited experimental data.
  • HD-sEMG signals were acquired from abductor pollicis brevis muscles using an 8x8 electrode array.
Keywords:
Deep learningHigh-density surface electromyogramMotor unitReal-time decomposition

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Last Updated: Jun 6, 2026

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Main Results:

  • The proposed method achieved approximately 93% accuracy in online MU identification.
  • Effective model fine-tuning was accomplished using only 6 seconds of experimental data.
  • The method significantly outperformed existing comparison techniques.

Conclusions:

  • This research offers an efficient solution for real-time MU identification.
  • The findings are expected to promote DL-based real-time HD-sEMG decomposition for neural-machine interfaces.
  • Applications include robotic motor control and rehabilitation medicine.