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Related Experiment Video

Updated: Jan 11, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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A deep learning framework for finger motion recognition using forearm ultrasound imaging.

Yohan Lee1, Keunsoo Ko2, Jaeyeop Jang3

  • 1Department of Orthopedic Surgery, Seoul National University Boramae Medical Center, Seoul, Republic of Korea.

Scientific Reports
|November 12, 2025
PubMed
Summary

This study introduces a deep learning method using B-mode ultrasound for accurate finger motion classification. The approach achieves over 95% accuracy, offering a promising alternative to surface electromyography (sEMG) for gesture recognition and rehabilitation.

Keywords:
Deep learningFinger motion recognitionHuman-machine interfaceProstheses controlUltrasound imaging

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Medical Imaging

Background:

  • Surface electromyography (sEMG) has limitations for precise finger motion classification.
  • A-mode ultrasound shows potential but has drawbacks.
  • B-mode ultrasound offers broader anatomical visualization and reduced sensitivity to placement.

Purpose of the Study:

  • To develop and evaluate a deep learning-based finger motion classification method using forearm B-mode ultrasound imaging.
  • To overcome the limitations of sEMG for reliable hand gesture recognition.

Main Methods:

  • Acquired real-time B-mode ultrasound images of forearm muscles during nine distinct finger motions (five single, four multi-finger).
  • Developed a deep learning framework for classifying finger movements from ultrasound data.
  • Utilized 2D visualization to capture comprehensive muscle activity patterns.

Main Results:

  • The proposed framework achieved a high overall average classification accuracy of 95.64%.
  • An F1 score of 0.9563 was obtained, indicating robust performance.
  • Demonstrated the feasibility of using forearm ultrasound for accurate finger movement recognition.

Conclusions:

  • The B-mode ultrasound and deep learning method is highly effective for finger motion classification.
  • This technique shows significant potential for applications in virtual/augmented reality, robotic control, and physical rehabilitation.
  • Offers a more reliable and comprehensive approach compared to traditional sEMG methods.