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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Hand gesture recognition via deep data optimization and 3D reconstruction.

Zaid Mustafa1, Heba Nsour2, Sheikh Badar Ud Din Tahir3,4,5

  • 1Department of Computer Information Systems, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Al-Balqa, Jordan.

Peerj. Computer Science
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a robust hand gesture recognition (HGR) method using fused features and artificial neural networks. The novel approach achieves high accuracy on benchmark datasets, improving real-time performance for human-computer interaction.

Keywords:
Grey wolf optimization (GWO)Hand gesture recognition (HGR)Leave-one-subject-out (LOSO)Machine learning (ML)

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Hand gesture recognition (HGR) is crucial for real-world interaction across various domains like VR, AR, and robotics.
  • Current HGR methods often rely on RGB data and optical flow, demanding significant computational resources and impacting real-time performance.

Purpose of the Study:

  • To develop a robust and computationally efficient hand gesture recognition approach.
  • To enhance the accuracy and real-time capabilities of HGR systems.

Main Methods:

  • Preprocessing included denoising, foreground extraction, and hand detection.
  • Hand segmentation was performed to identify key landmarks.
  • Three fused features (geometric, 3D point modeling, angular point) were utilized with Grey Wolf Optimization for artificial neural networks.

Main Results:

  • The proposed HGR method achieved 89.92% accuracy on the IPN hand dataset.
  • The system demonstrated 89.76% recognition accuracy on the Jester dataset.

Conclusions:

  • The novel HGR approach effectively utilizes multi-fused features and optimization techniques.
  • The method offers a promising solution for accurate and efficient hand gesture recognition, overcoming computational limitations of existing techniques.