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

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

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[Convolutional neural network human gesture recognition algorithm based on phase portrait of surface electromyography

Liukai Xu^1,2, Keqin Zhang³, Zhaohong Xu³

¹Ningbo Artificial Intelligence Institute of Shanghai Jiao Tong University, Ningbo, Zhejiang 315000, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|August 30, 2021

Summary

This study introduces a new method for recognizing human gestures using surface electromyography (sEMG) signals. By combining convolutional neural networks (CNN) with sEMG energy kernel phase portraits, the approach significantly improves gesture recognition accuracy and efficiency.

Keywords:

convolutional neural network energy kernel gesture recognition surface electromyography

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Published on: July 26, 2013

Area of Science:

Biomedical Engineering
Signal Processing
Machine Learning

Background:

Surface electromyography (sEMG) signals are weak, non-stationary, and non-periodic, posing challenges for traditional classification.
Existing time and frequency domain sEMG classification methods exhibit low recognition rates and poor stability.

Purpose of the Study:

To propose a novel method for human gesture recognition using sEMG signals.
To enhance the accuracy and stability of sEMG-based gesture classification.

Main Methods:

Modeling and analysis of sEMG energy kernel phase portraits.
Processing phase portraits into grayscale images using matrix counting.
Preprocessing grayscale images with a moving average filter.
Utilizing a convolutional neural network (CNN) for sEMG gesture recognition.

Main Results:

The proposed CNN and energy kernel phase portrait method demonstrates superior recognition accuracy compared to area extraction methods.
The approach shows significant advantages in computational efficiency.
Experimental results validate the effectiveness of the proposed recognition framework.

Conclusions:

The combination of CNN and sEMG energy kernel phase portraits offers a robust and efficient solution for gesture recognition.
This method provides a new feasible approach for sEMG signal modeling, analysis, and real-time identification.