Performance Analysis of EMG Signal Classification Methods for Hand Gesture Recognition in Stroke Rehabilitation

Anggun Winursito, Universitas Negeri Yogyakarta, Indonesia
Fatchul Arifin, Universitas Negeri Yogyakarta, Indonesia
Muslikhin Muslikhin, Universitas Negeri Yogyakarta, Indonesia
Herjuna Artanto, Universitas Negeri Yogyakarta, Indonesia
Femilia Hardina Caryn, Universitas Negeri Yogyakarta, Indonesia

 10.21831/elinvo.v8i2.76811

Abstract


This study evaluates the performance of different classification methods in classifying healthy individuals and stroke patients. The hand gesture variations of the subjects were also analyzed based on electromyography (EMG) signals. Several classification methods were tested in this analysis to find out which method had the most suitable performance. The results showed that Decision Tree and Naive Bayes classifiers achieved the highest performance in classifying EMG signals from healthy individuals and stroke patients, with both methods showing high accuracy, precision, recall, and F1 score. Specifically, Decision Tree excelled in overall accuracy and recall, while Naive Bayes showed superior precision. For hand gesture recognition, SVM, KNN, and Random Forest classifiers showed similarly high performance, achieving accuracy, precision, recall, and F1 score above 82%. Naive Bayes also performed well, especially in precision, while Decision Tree performed poorly compared to other methods. This insight can form the basis for the development of more effective and personalized rehabilitation systems for stroke patients, by utilizing reliable and accurate EMG signal classification

Keywords


EMG signal; classifier; stroke rehabilitation; hand gesture recognition

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References


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DOI: https://doi.org/10.21831/elinvo.v8i2.76811

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