Aviles, M.; Rodríguez-Reséndiz, J.; Ibrahimi, D. Optimizing EMG Classification through Metaheuristic Algorithms. Technologies2023, 11, 87.
Aviles, M.; Rodríguez-Reséndiz, J.; Ibrahimi, D. Optimizing EMG Classification through Metaheuristic Algorithms. Technologies 2023, 11, 87.
Aviles, M.; Rodríguez-Reséndiz, J.; Ibrahimi, D. Optimizing EMG Classification through Metaheuristic Algorithms. Technologies2023, 11, 87.
Aviles, M.; Rodríguez-Reséndiz, J.; Ibrahimi, D. Optimizing EMG Classification through Metaheuristic Algorithms. Technologies 2023, 11, 87.
Abstract
This work proposes a metaheuristic-based approach for hyperparameter selection in a multilayer perceptron to classify electromyographic signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, the epochs, and the training batches. The approach proposed in this work shows that hyperparameter optimization using particle swarm optimization and gray wolf optimizer significantly improves the performance of a multilayer perceptron for classifying EMG motion signals. The final model achieved an average classification rate of 93% for the validation phases. The results obtained are promising and suggest that the proposed approach may be helpful for the optimization of deep learning models in other signal processing applications.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
