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EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee
Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and kne...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197024/ https://www.ncbi.nlm.nih.gov/pubmed/34067477 http://dx.doi.org/10.3390/s21113622 |
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author | Coker, Jordan Chen, Howard Schall, Mark C. Gallagher, Sean Zabala, Michael |
author_facet | Coker, Jordan Chen, Howard Schall, Mark C. Gallagher, Sean Zabala, Michael |
author_sort | Coker, Jordan |
collection | PubMed |
description | Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm’s prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy. |
format | Online Article Text |
id | pubmed-8197024 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81970242021-06-13 EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee Coker, Jordan Chen, Howard Schall, Mark C. Gallagher, Sean Zabala, Michael Sensors (Basel) Communication Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm’s prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy. MDPI 2021-05-22 /pmc/articles/PMC8197024/ /pubmed/34067477 http://dx.doi.org/10.3390/s21113622 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Coker, Jordan Chen, Howard Schall, Mark C. Gallagher, Sean Zabala, Michael EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee |
title | EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee |
title_full | EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee |
title_fullStr | EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee |
title_full_unstemmed | EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee |
title_short | EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee |
title_sort | emg and joint angle-based machine learning to predict future joint angles at the knee |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197024/ https://www.ncbi.nlm.nih.gov/pubmed/34067477 http://dx.doi.org/10.3390/s21113622 |
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