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Dimensionality reduction for classification of object weight from electromyography

Electromyography (EMG) is a simple, non-invasive, and cost-effective technology for measuring muscle activity. However, multi-muscle EMG is also a noisy, complex, and high-dimensional signal. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchai...

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Detalles Bibliográficos
Autores principales: Lashgari, Elnaz, Maoz, Uri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367006/
https://www.ncbi.nlm.nih.gov/pubmed/34398924
http://dx.doi.org/10.1371/journal.pone.0255926
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author Lashgari, Elnaz
Maoz, Uri
author_facet Lashgari, Elnaz
Maoz, Uri
author_sort Lashgari, Elnaz
collection PubMed
description Electromyography (EMG) is a simple, non-invasive, and cost-effective technology for measuring muscle activity. However, multi-muscle EMG is also a noisy, complex, and high-dimensional signal. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchairs, virtual computer mice, prosthesis, robotic fingers, etc.) and, in particular, to measure the reach-and-grasp motions of the human hand. Here, we developed an automated pipeline to predict object weight in a reach-grasp-lift task from an open dataset, relying only on EMG data. In doing so, we shifted the focus from manual feature-engineering to automated feature-extraction by using pre-processed EMG signals and thus letting the algorithms select the features. We further compared intrinsic EMG features, derived from several dimensionality-reduction methods, and then ran several classification algorithms on these low-dimensional representations. We found that the Laplacian Eigenmap algorithm generally outperformed other dimensionality-reduction methods. What is more, optimal classification accuracy was achieved using a combination of Laplacian Eigenmaps (simple-minded) and k-Nearest Neighbors (88% F1 score for 3-way classification). Our results, using EMG alone, are comparable to other researchers’, who used EMG and EEG together, in the literature. A running-window analysis further suggests that our method captures information in the EMG signal quickly and remains stable throughout the time that subjects grasp and move the object.
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spelling pubmed-83670062021-08-17 Dimensionality reduction for classification of object weight from electromyography Lashgari, Elnaz Maoz, Uri PLoS One Research Article Electromyography (EMG) is a simple, non-invasive, and cost-effective technology for measuring muscle activity. However, multi-muscle EMG is also a noisy, complex, and high-dimensional signal. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchairs, virtual computer mice, prosthesis, robotic fingers, etc.) and, in particular, to measure the reach-and-grasp motions of the human hand. Here, we developed an automated pipeline to predict object weight in a reach-grasp-lift task from an open dataset, relying only on EMG data. In doing so, we shifted the focus from manual feature-engineering to automated feature-extraction by using pre-processed EMG signals and thus letting the algorithms select the features. We further compared intrinsic EMG features, derived from several dimensionality-reduction methods, and then ran several classification algorithms on these low-dimensional representations. We found that the Laplacian Eigenmap algorithm generally outperformed other dimensionality-reduction methods. What is more, optimal classification accuracy was achieved using a combination of Laplacian Eigenmaps (simple-minded) and k-Nearest Neighbors (88% F1 score for 3-way classification). Our results, using EMG alone, are comparable to other researchers’, who used EMG and EEG together, in the literature. A running-window analysis further suggests that our method captures information in the EMG signal quickly and remains stable throughout the time that subjects grasp and move the object. Public Library of Science 2021-08-16 /pmc/articles/PMC8367006/ /pubmed/34398924 http://dx.doi.org/10.1371/journal.pone.0255926 Text en © 2021 Lashgari, Maoz https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lashgari, Elnaz
Maoz, Uri
Dimensionality reduction for classification of object weight from electromyography
title Dimensionality reduction for classification of object weight from electromyography
title_full Dimensionality reduction for classification of object weight from electromyography
title_fullStr Dimensionality reduction for classification of object weight from electromyography
title_full_unstemmed Dimensionality reduction for classification of object weight from electromyography
title_short Dimensionality reduction for classification of object weight from electromyography
title_sort dimensionality reduction for classification of object weight from electromyography
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367006/
https://www.ncbi.nlm.nih.gov/pubmed/34398924
http://dx.doi.org/10.1371/journal.pone.0255926
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