EMG-Based Hand Gesture Recognition through Diverse Domain Feature Enhancement and Machine Learning-Based Approach

TL;DR

This paper introduces a novel EMG-based hand gesture recognition method that combines diverse feature extraction, efficient feature selection, and machine learning to achieve high accuracy and improve system usability.

Contribution

It presents a comprehensive feature extraction and selection strategy that enhances EMG-based gesture recognition accuracy using machine learning.

Findings

Achieved 97.43% accuracy with KNN classifier.

Explored 23 feature extraction techniques and selected optimal features.

Demonstrated improved performance over existing systems.

Abstract

Surface electromyography (EMG) serves as a pivotal tool in hand gesture recognition and human-computer interaction, offering a non-invasive means of signal acquisition. This study presents a novel methodology for classifying hand gestures using EMG signals. To address the challenges associated with feature extraction where, we explored 23 distinct morphological, time domain and frequency domain feature extraction techniques. However, the substantial size of the features may increase the computational complexity issues that can hinder machine learning algorithm performance. We employ an efficient feature selection approach, specifically an extra tree classifier, to mitigate this. The selected potential feature fed into the various machine learning-based classification algorithms where our model achieved 97.43\% accuracy with the KNN algorithm and selected feature. By leveraging a comprehensive feature extraction and selection strategy, our methodology enhances the accuracy and usability of EMG-based hand gesture recognition systems. The higher performance accuracy proves the effectiveness of the proposed model over the existing system. \keywords{EMG signal, machine learning approach, hand gesture recognition.