Optimizing EMG Classification through Metaheuristic Algorithms
This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG 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,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Technologies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7080/11/4/87 |
| _version_ | 1797583125138636800 |
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| author | Marcos Aviles Juvenal Rodríguez-Reséndiz Danjela Ibrahimi |
| author_facet | Marcos Aviles Juvenal Rodríguez-Reséndiz Danjela Ibrahimi |
| author_sort | Marcos Aviles |
| collection | DOAJ |
| description | This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG 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 the gray wolf optimizer significantly improves the performance of a multilayer perceptron in classifying EMG motion signals. The final model achieves an average classification rate of 93% for the validation phase. 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. |
| first_indexed | 2024-03-10T23:32:23Z |
| format | Article |
| id | doaj.art-284617bc6f3842c58a25cc1df9e6a84d |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-10T23:32:23Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-284617bc6f3842c58a25cc1df9e6a84d2023-11-19T03:13:14ZengMDPI AGTechnologies2227-70802023-07-011148710.3390/technologies11040087Optimizing EMG Classification through Metaheuristic AlgorithmsMarcos Aviles0Juvenal Rodríguez-Reséndiz1Danjela Ibrahimi2Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MexicoFacultad de Medicina, Universidad Autónoma de Querétaro, Querétaro 76176, MexicoThis work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG 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 the gray wolf optimizer significantly improves the performance of a multilayer perceptron in classifying EMG motion signals. The final model achieves an average classification rate of 93% for the validation phase. 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.https://www.mdpi.com/2227-7080/11/4/87PSOGWOmetaheuristicmultilayer perceptronhyperparametersEMG signals |
| spellingShingle | Marcos Aviles Juvenal Rodríguez-Reséndiz Danjela Ibrahimi Optimizing EMG Classification through Metaheuristic Algorithms Technologies PSO GWO metaheuristic multilayer perceptron hyperparameters EMG signals |
| title | Optimizing EMG Classification through Metaheuristic Algorithms |
| title_full | Optimizing EMG Classification through Metaheuristic Algorithms |
| title_fullStr | Optimizing EMG Classification through Metaheuristic Algorithms |
| title_full_unstemmed | Optimizing EMG Classification through Metaheuristic Algorithms |
| title_short | Optimizing EMG Classification through Metaheuristic Algorithms |
| title_sort | optimizing emg classification through metaheuristic algorithms |
| topic | PSO GWO metaheuristic multilayer perceptron hyperparameters EMG signals |
| url | https://www.mdpi.com/2227-7080/11/4/87 |
| work_keys_str_mv | AT marcosaviles optimizingemgclassificationthroughmetaheuristicalgorithms AT juvenalrodriguezresendiz optimizingemgclassificationthroughmetaheuristicalgorithms AT danjelaibrahimi optimizingemgclassificationthroughmetaheuristicalgorithms |