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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network.

Yuting Liu1,2, Du Jiang1,2, Haojie Duan2,3

  • 1Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.

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|August 27, 2021
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This study introduces a new dynamic gesture recognition model using a Convolutional 3D (C3D) network with a CBAM attention mechanism. The model improves recognition accuracy for human-computer interaction, enhancing real-time performance.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Gesture recognition is crucial for human-computer interaction, primarily using visual technology.
  • Dynamic gesture recognition faces challenges in computational complexity and real-time performance due to multiframe video analysis.

Purpose of the Study:

  • To propose an efficient dynamic gesture recognition model addressing computational and performance limitations.
  • To enhance the accuracy and real-time capabilities of gesture recognition systems.

Main Methods:

  • A novel dynamic gesture recognition model based on CBAM-C3D (Convolutional 3D with Convolutional Block Attention Module) was developed.
  • Key frame extraction, multimodal joint training, and network optimization using Batch Normalization (BN) layers were employed.
  • The model integrates temporal and spatial feature extraction for dynamic gesture analysis.

Main Results:

  • The proposed CBAM-C3D model achieved a recognition accuracy of 72.4% on the EgoGesture dataset.
  • This represents a significant improvement over existing main dynamic gesture recognition methods.
  • The effectiveness of the attention mechanism and network optimization techniques was validated.

Conclusions:

  • The CBAM-C3D model offers a superior approach to dynamic gesture recognition, balancing accuracy and efficiency.
  • The integration of attention mechanisms and optimization strategies effectively addresses the computational challenges in dynamic gesture analysis.
  • This research validates the proposed algorithm for advanced human-computer interaction applications.