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Related Experiment Video

Updated: Dec 10, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Dynamic Japanese Sign Language Recognition Throw Hand Pose Estimation Using Effective Feature Extraction and

Manato Kakizaki1, Abu Saleh Musa Miah1, Koki Hirooka1

  • 1School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
Summary

This study introduces a novel dynamic Japanese Sign Language (JSL) recognition system. It achieves high accuracy in recognizing JSL gestures, improving communication for the deaf and hard-of-hearing community.

Keywords:
dynamic hand gesture recognitioneffective feature selectionhand skeleton pointsjapanese sign language (JSL)machine learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Japanese Sign Language (JSL) is crucial for Japan's deaf community.
  • Dynamic aspects of JSL have been largely excluded in previous recognition studies due to complexity.
  • Existing dynamic JSL recognition systems show unsatisfactory performance.

Purpose of the Study:

  • To develop an accurate dynamic Japanese Sign Language recognition system.
  • To overcome challenges associated with the complexity and dynamic nature of JSL.
  • To enhance communication accessibility for the deaf and hard-of-hearing population.

Main Methods:

  • Utilized hand pose estimation via MediaPipe on RGB camera footage.
  • Proposed four novel feature types applicable to both static and dynamic gestures.
  • Employed Random Forest (RF) for feature selection and Support Vector Machine (SVM) for classification.

Main Results:

  • Achieved 97.20% recognition accuracy on a proprietary JSL dataset.
  • Achieved 98.40% recognition accuracy on the LSA64 dynamic dataset.
  • Demonstrated the effectiveness of the proposed feature extraction and selection methods.

Conclusions:

  • The developed system effectively recognizes dynamic JSL gestures, addressing prior limitations.
  • This approach has significant potential to bridge communication gaps for JSL users.
  • The methodology offers broader implications for global sign language recognition advancements.