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Journals / International Journal of Applied Mathematics Electronics and Computers / 2019 / Cilt: 7 - Sayı: 4

An Ensemble Classifier for Finger Movement Recognition using EMG Signals

İlker Ali ÖZKAN
Pages
96–99
Publication Date
2019-12-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result of 10-fold cross-validation, with the proposed method, 96.6% overall classification accuracy was achieved.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Electromyography (EMG) signals that obtained by electrodes connected to the forearm are the monitoring of the muscles by the electrical method.
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Key Findings & Data:EMG signals that belong to 10 different finger movements obtained from eight subjects were used.
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Conclusion & Impact:As a result of 10-fold cross-validation, with the proposed method, 96.6% overall classification accuracy was achieved.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Ensemble Classifier · Bagged Tree · Electromyography

Abstract

Electromyography (EMG) signals that obtained by electrodes connected to the forearm are the monitoring of the muscles by the electrical method. These signals are quite useful during the use of prosthesis as a source signal to the moving prosthesis. Therefore, it is essential that classifying the EMG signals with high accuracy by analyzing. This study aims that classifying the individual and combined finger movements using surface EMG signals taken from the surface of the human forearm. EMG signals that belong to 10 different finger movements obtained from eight subjects were used. Firstly, EMG signals have been split into segments by the windowing process, and temporal feature vectors are formed by applying various feature extraction methods to these segments. Feature vectors have been classified with the ensemble bagged tree algorithm, which is a combination of classifiers, to obtain the correct classification decision. As a result of 10-fold cross-validation, with the proposed method, 96.6% overall classification accuracy was achieved. The results obtained show that the ensemble classifier can be used successfully in determining finger movements when compared with similar studies.

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Cite This Article

ÖZKAN, İ. A. (2019). An Ensemble Classifier for Finger Movement Recognition using EMG Signals. International Journal of Applied Mathematics Electronics and Computers, Cilt: 7 - Sayı: 4, 96–99. https://ajindex.com/articles/258047

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