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Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework

This contribution presents a novel methodology for myolectric-based control using surface electromyographic (sEMG) signals recorded during finger movements. A multivariate Bayesian mixture of experts (MoE) model is introduced which provides a powerful method for modeling force regression at the fing...

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Autores principales: Baldacchino, Tara, Jacobs, William R., Anderson, Sean R., Worden, Keith, Rowson, Jennifer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5834453/
https://www.ncbi.nlm.nih.gov/pubmed/29536005
http://dx.doi.org/10.3389/fbioe.2018.00013
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author Baldacchino, Tara
Jacobs, William R.
Anderson, Sean R.
Worden, Keith
Rowson, Jennifer
author_facet Baldacchino, Tara
Jacobs, William R.
Anderson, Sean R.
Worden, Keith
Rowson, Jennifer
author_sort Baldacchino, Tara
collection PubMed
description This contribution presents a novel methodology for myolectric-based control using surface electromyographic (sEMG) signals recorded during finger movements. A multivariate Bayesian mixture of experts (MoE) model is introduced which provides a powerful method for modeling force regression at the fingertips, while also performing finger movement classification as a by-product of the modeling algorithm. Bayesian inference of the model allows uncertainties to be naturally incorporated into the model structure. This method is tested using data from the publicly released NinaPro database which consists of sEMG recordings for 6 degree-of-freedom force activations for 40 intact subjects. The results demonstrate that the MoE model achieves similar performance compared to the benchmark set by the authors of NinaPro for finger force regression. Additionally, inherent to the Bayesian framework is the inclusion of uncertainty in the model parameters, naturally providing confidence bounds on the force regression predictions. Furthermore, the integrated clustering step allows a detailed investigation into classification of the finger movements, without incurring any extra computational effort. Subsequently, a systematic approach to assessing the importance of the number of electrodes needed for accurate control is performed via sensitivity analysis techniques. A slight degradation in regression performance is observed for a reduced number of electrodes, while classification performance is unaffected.
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spelling pubmed-58344532018-03-13 Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework Baldacchino, Tara Jacobs, William R. Anderson, Sean R. Worden, Keith Rowson, Jennifer Front Bioeng Biotechnol Bioengineering and Biotechnology This contribution presents a novel methodology for myolectric-based control using surface electromyographic (sEMG) signals recorded during finger movements. A multivariate Bayesian mixture of experts (MoE) model is introduced which provides a powerful method for modeling force regression at the fingertips, while also performing finger movement classification as a by-product of the modeling algorithm. Bayesian inference of the model allows uncertainties to be naturally incorporated into the model structure. This method is tested using data from the publicly released NinaPro database which consists of sEMG recordings for 6 degree-of-freedom force activations for 40 intact subjects. The results demonstrate that the MoE model achieves similar performance compared to the benchmark set by the authors of NinaPro for finger force regression. Additionally, inherent to the Bayesian framework is the inclusion of uncertainty in the model parameters, naturally providing confidence bounds on the force regression predictions. Furthermore, the integrated clustering step allows a detailed investigation into classification of the finger movements, without incurring any extra computational effort. Subsequently, a systematic approach to assessing the importance of the number of electrodes needed for accurate control is performed via sensitivity analysis techniques. A slight degradation in regression performance is observed for a reduced number of electrodes, while classification performance is unaffected. Frontiers Media S.A. 2018-02-26 /pmc/articles/PMC5834453/ /pubmed/29536005 http://dx.doi.org/10.3389/fbioe.2018.00013 Text en Copyright © 2018 Baldacchino, Jacobs, Anderson, Worden and Rowson. http://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 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 Bioengineering and Biotechnology
Baldacchino, Tara
Jacobs, William R.
Anderson, Sean R.
Worden, Keith
Rowson, Jennifer
Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework
title Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework
title_full Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework
title_fullStr Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework
title_full_unstemmed Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework
title_short Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework
title_sort simultaneous force regression and movement classification of fingers via surface emg within a unified bayesian framework
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5834453/
https://www.ncbi.nlm.nih.gov/pubmed/29536005
http://dx.doi.org/10.3389/fbioe.2018.00013
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