EXPLOITING EMG SIGNALS FOR THE RECOGNITION OF FINGER FLEXIONS USING WAVELET TRANSFORM AND MACHINE LEARNING
Journal
Academic Medical Journal
Date Issued
2024-12-11
Author(s)
Paunkoska, Klimentina
Nadzinski, Gorjan
Hristov, Blagoj
Gjorgjeski, Antonio
DOI
10.53582/AMJ2443096p
Abstract
Electromyography (EMG) is a technique that measures and records electrical activity
in response to a nerve’s stimulation of the muscle. EMG signals are biomedical signals that
represent electrical currents generated in muscles during their contraction. EMG signals
acquired from muscles require advanced methods for detection, decomposition, processing
and classification. Various mathematical techniques have received extensive attention and one
of the most popular is Wavelet transform. Wavelet transform is a mathematical tool for
analyzing data where the signal values vary at different scales, such as in EMG signals, so it
is widely used in EMG signal processing systems. This study explored the potential of
applying wavelet transform to EMG signals, which were collected using two sensors placed
on the forearms of eight subjects performing individual finger flexions. We experimented
with various mother wavelets and decomposition levels to determine the most effective
combination. After evaluating the results obtained from training models, we selected the
Daubechies wavelet (db1) with a second level of decomposition as the optimal solution. To
generate meaningful features from the wavelet coefficients, we extracted time-frequency
domain features, which were then used as inputs for training and testing machine learning
models. We employed five classification algorithms: K-nearest neighbors, Support Vector
Machine, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost). By
evaluating and comparing the performance of these algorithms, we demonstrated enhanced
accuracy and robustness achieved by the combination of wavelet transform and feature
extraction in EMG signal analysis.
in response to a nerve’s stimulation of the muscle. EMG signals are biomedical signals that
represent electrical currents generated in muscles during their contraction. EMG signals
acquired from muscles require advanced methods for detection, decomposition, processing
and classification. Various mathematical techniques have received extensive attention and one
of the most popular is Wavelet transform. Wavelet transform is a mathematical tool for
analyzing data where the signal values vary at different scales, such as in EMG signals, so it
is widely used in EMG signal processing systems. This study explored the potential of
applying wavelet transform to EMG signals, which were collected using two sensors placed
on the forearms of eight subjects performing individual finger flexions. We experimented
with various mother wavelets and decomposition levels to determine the most effective
combination. After evaluating the results obtained from training models, we selected the
Daubechies wavelet (db1) with a second level of decomposition as the optimal solution. To
generate meaningful features from the wavelet coefficients, we extracted time-frequency
domain features, which were then used as inputs for training and testing machine learning
models. We employed five classification algorithms: K-nearest neighbors, Support Vector
Machine, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost). By
evaluating and comparing the performance of these algorithms, we demonstrated enhanced
accuracy and robustness achieved by the combination of wavelet transform and feature
extraction in EMG signal analysis.
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