Journal article
M. Reyes-Fernandez, R. Posada-Gómez, A. Martínez-Sibaja, A. Aguilar-Lasserre, J. J. A. Flores Cuautle
Journal of Sensors, 2024
Journal of Sensors, 2024
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DOI
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APA
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Reyes-Fernandez, M., Posada-Gómez, R., Martínez-Sibaja, A., Aguilar-Lasserre, A., & Cuautle, J. J. A. F. (2024). Wrist EMG Monitoring Using Neural Networks Techniques. Journal of Sensors.
Chicago/Turabian
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Reyes-Fernandez, M., R. Posada-Gómez, A. Martínez-Sibaja, A. Aguilar-Lasserre, and J. J. A. Flores Cuautle. “Wrist EMG Monitoring Using Neural Networks Techniques.” Journal of Sensors (2024).
MLA
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Reyes-Fernandez, M., et al. “Wrist EMG Monitoring Using Neural Networks Techniques.” Journal of Sensors, 2024.
BibTeX Click to copy
@article{m2024a,
title = {Wrist EMG Monitoring Using Neural Networks Techniques},
year = {2024},
journal = {Journal of Sensors},
author = {Reyes-Fernandez, M. and Posada-Gómez, R. and Martínez-Sibaja, A. and Aguilar-Lasserre, A. and Cuautle, J. J. A. Flores}
}
Abstract
In rehabilitation, the correct performance of the exercises the specialist prescribes wrist movement is crucial. However, this may have the disadvantage of the patient’s subjectivity. Moreover, recent studies show that feedback through electrostimulation devices is beneficial during the process that leads to neuromotor rehabilitation. Besides, the electromyographic (EMG) signals give information about the actual degree of rehabilitation. This work examines whether temporal features can be used to classify wrist movements using back-propagation artificial neural networks and superficial EMG (sEMG) signals. The data for the evaluation were based on the information acquired from sEMG signals of two forearm muscles: the flexor carpi ulnaris (FCU) and the brachioradialis (B). These sEMG signals were analyzed to find the most critical parameters for classifying the wrist’s movement and to configure a multilayer perceptron (MLP) capable of classifying such movements.