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

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An exploratory study to design a novel hand movement identification system.

Mahdi Khezri¹, Mehran Jahed

¹Department of Electrical Engineering, Biomedical Engineering Group, Sharif University of Technology, Tehran, Iran. mahdi_khezri_ee@yahoo.com

Computers in Biology and Medicine

|April 4, 2009

Summary

This study introduces a novel system for recognizing surface electromyogram (sEMG) patterns to improve prosthetic hand movements. Combining time and time-frequency domain features with a fuzzy inference system (FIS) classifier achieved the best performance.

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Area of Science:

Biomedical Engineering
Signal Processing
Machine Learning

Background:

Surface electromyogram (sEMG) signals reflect muscle electrical activity.
Current prosthetic hands have limited functionality, often restricted to basic open/close actions.
Advanced sEMG pattern recognition is needed to enhance prosthetic arm capabilities.

Purpose of the Study:

To develop a novel sEMG pattern recognition system for improved prosthetic hand movement control.
To investigate the effectiveness of different feature extraction methods for sEMG signals.
To compare the performance of artificial neural networks (ANN) and fuzzy inference systems (FIS) for classifying hand movements.

Main Methods:

Extracted sEMG signals for various hand movements.
Investigated time domain, time-frequency domain, and combined features.
Applied Principle Component Analysis (PCA) for dimensionality reduction.
Utilized ANN and FIS as intelligent classifiers.

Main Results:

Compound features, when combined with PCA and FIS, demonstrated superior performance.
The proposed system effectively recognized different hand movements from sEMG signals.
FIS classifier outperformed ANN for this specific sEMG pattern recognition task.

Conclusions:

A novel sEMG pattern recognition system using compound features and FIS offers enhanced performance for prosthetic applications.
The integration of PCA for feature reduction and FIS for classification is a promising approach.
This method can significantly improve the accuracy and capabilities of prosthetic arm movements.