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Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition

Choosing the right features is important to optimize lower limb pattern recognition, such as in prosthetic control. EMG signals are noisy in nature, which makes it more challenging to extract useful information. Many features are used in the literature, which raises the question which features are m...

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Autores principales: Schulte, Robert V., Prinsen, Erik C., Hermens, Hermie J., Buurke, Jaap H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573095/
https://www.ncbi.nlm.nih.gov/pubmed/34760930
http://dx.doi.org/10.3389/frobt.2021.710806
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author Schulte, Robert V.
Prinsen, Erik C.
Hermens, Hermie J.
Buurke, Jaap H.
author_facet Schulte, Robert V.
Prinsen, Erik C.
Hermens, Hermie J.
Buurke, Jaap H.
author_sort Schulte, Robert V.
collection PubMed
description Choosing the right features is important to optimize lower limb pattern recognition, such as in prosthetic control. EMG signals are noisy in nature, which makes it more challenging to extract useful information. Many features are used in the literature, which raises the question which features are most suited for use in lower limb myoelectric control. Therefore, it is important to find combinations of best performing features. One way to achieve this is by using a genetic algorithm, a meta-heuristic capable of searching vast feature spaces. The goal of this research is to demonstrate the capabilities of a genetic algorithm and come up with a feature set that has a better performance than the state-of-the-art feature set. In this study, we collected a dataset containing ten able-bodied subjects who performed various gait-related activities while measuring EMG and kinematics. The genetic algorithm selected features based on the performance on the training partition of this dataset. The selected feature sets were evaluated on the remaining test set and on the online benchmark dataset ENABL3S, against a state-of-the-art feature set. The results show that a feature set based on the selected features of a genetic algorithm outperforms the state-of-the-art set. The overall error decreased up to 0.54% and the transitional error by 2.44%, which represent a relative decrease in overall errors up to 11.6% and transitional errors up to 14.1%, although these results were not significant. This study showed that a genetic algorithm is capable of searching a large feature space and that systematic feature selection shows promising results for lower limb myoelectric control.
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spelling pubmed-85730952021-11-09 Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition Schulte, Robert V. Prinsen, Erik C. Hermens, Hermie J. Buurke, Jaap H. Front Robot AI Robotics and AI Choosing the right features is important to optimize lower limb pattern recognition, such as in prosthetic control. EMG signals are noisy in nature, which makes it more challenging to extract useful information. Many features are used in the literature, which raises the question which features are most suited for use in lower limb myoelectric control. Therefore, it is important to find combinations of best performing features. One way to achieve this is by using a genetic algorithm, a meta-heuristic capable of searching vast feature spaces. The goal of this research is to demonstrate the capabilities of a genetic algorithm and come up with a feature set that has a better performance than the state-of-the-art feature set. In this study, we collected a dataset containing ten able-bodied subjects who performed various gait-related activities while measuring EMG and kinematics. The genetic algorithm selected features based on the performance on the training partition of this dataset. The selected feature sets were evaluated on the remaining test set and on the online benchmark dataset ENABL3S, against a state-of-the-art feature set. The results show that a feature set based on the selected features of a genetic algorithm outperforms the state-of-the-art set. The overall error decreased up to 0.54% and the transitional error by 2.44%, which represent a relative decrease in overall errors up to 11.6% and transitional errors up to 14.1%, although these results were not significant. This study showed that a genetic algorithm is capable of searching a large feature space and that systematic feature selection shows promising results for lower limb myoelectric control. Frontiers Media S.A. 2021-10-25 /pmc/articles/PMC8573095/ /pubmed/34760930 http://dx.doi.org/10.3389/frobt.2021.710806 Text en Copyright © 2021 Schulte, Prinsen, Hermens and Buurke. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Schulte, Robert V.
Prinsen, Erik C.
Hermens, Hermie J.
Buurke, Jaap H.
Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition
title Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition
title_full Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition
title_fullStr Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition
title_full_unstemmed Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition
title_short Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition
title_sort genetic algorithm for feature selection in lower limb pattern recognition
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573095/
https://www.ncbi.nlm.nih.gov/pubmed/34760930
http://dx.doi.org/10.3389/frobt.2021.710806
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