Advancing EMG Finger Movement Classification with Feature Extraction and Machine Learning

Section: Research Paper
Issue
Vol. 30 No. 1 (2025): Volume 30 Issue 1
Published
Mar 1, 2025
Pages
150-163

Abstract

In prosthetic finger development using electromyogram (EMG) data, a crucial challenge is accurately recognizing finger movements, thus requiring developed models that process EMG signals, facilitating independent finger gesture classification with high accuracy. To successfully classify an EMG signal, the feature selection should be carefully evaluated. However, many studies on EMG signal classification have employed a feature set that includes several redundant elements. In this study, several combinations of time domain features are employed for EMG signal reduction. In addition, two models of CNN namely: (CNN-1, CNN-2), DFNN, LSTM, and GRU architectures are proposed to provide high accuracy with minimal computational overhead and minimum parameters. Through careful model selection and hyperparameter optimization, the models effectiveness was enhanced. The models were evaluated based on accuracy, precision, recall, and F1-score metrics. Among the proposed models, CNN-1 resulted in a good balance in terms of accuracy, computational time, and memory size, with an accuracy of 97.3 in 0.96 minutes with 890.73 KB size of memory. Furthermore, a comparison against earlier work confirmed the efficacy of the approach.

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Authors

Rasha Moysar

Computer and Information Engineering Department, College of Electronics Engineering, Ninevah University, Mosul, Iraq

Mohammed Abdulmuttaleb Abdullah

Computer and Information Engineering Department, College of Electronics Engineering, Nineveh University, Mosul, Iraq

ORCID

Identifiers

https://doi.org/10.33899/arej.2024.152539.1368
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How to Cite

[1]
R. Moysar and M. Abdulmuttaleb Abdullah, “Advancing EMG Finger Movement Classification with Feature Extraction and Machine Learning”, AREJ, vol. 30, no. 1, pp. 150–163, Mar. 2025.
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