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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

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Research on gesture recognition algorithm based on MME-P3D.

Hongmei Jin¹, Ning He¹, Boyu Liu¹

¹College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China.

Mathematical Biosciences and Engineering : MBE

|March 29, 2024

Summary

This summary is machine-generated.

A new Multiscale-Motion Embedding Pseudo-3D (MME-P3D) algorithm enhances gesture recognition for mobile devices. This efficient model significantly reduces parameters and computational load, improving practical applications.

Keywords:

P3D convolution attention mechanism computer vision deep learning gesture recognition image processing

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Existing gesture recognition algorithms face challenges with high parameter counts and computational complexity, limiting their use on mobile and embedded devices.
Efficient extraction of spatio-temporal features is crucial for accurate gesture recognition.

Purpose of the Study:

To propose a novel Multiscale-Motion Embedding Pseudo-3D (MME-P3D) algorithm for efficient gesture recognition.
To reduce the parameter count and computational complexity of gesture recognition models for mobile and embedded applications.

Main Methods:

Developed a P3D-C feature extraction network by integrating a channel attention (CE) mechanism into the pseudo-3D (P3D) module.
Introduced a Multiscale Motion Embedding (MME) mechanism to improve the learning of global gesture movement dynamics.

Main Results:

Achieved high recognition accuracies of 91.12% on a conference gesture dataset and 83.06% on the Chalearn 2013 dataset.
Reduced parameter count by up to 82% and computational requirements by up to 83% compared to conventional 3D convolutional neural networks.

Conclusions:

The MME-P3D algorithm offers a significant reduction in complexity, making it suitable for deployment on resource-constrained mobile and embedded devices.
This advancement provides a more effective approach for practical hand gesture recognition technology.