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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Elnaz Lashgari, Uri Maoz
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0922b1f29d4546e69ed2872a2bb58cbe
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0922b1f29d4546e69ed2872a2bb58cbe
record_format	dspace
spelling	oai:doaj.org-article:0922b1f29d4546e69ed2872a2bb58cbe2021-12-02T20:18:02ZDimensionality reduction for classification of object weight from electromyography.1932-620310.1371/journal.pone.0255926https://doaj.org/article/0922b1f29d4546e69ed2872a2bb58cbe2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255926https://doaj.org/toc/1932-6203Electromyography (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.Elnaz LashgariUri MaozPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255926 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Elnaz Lashgari Uri Maoz Dimensionality reduction for classification of object weight from electromyography.
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.
format	article
author	Elnaz Lashgari Uri Maoz
author_facet	Elnaz Lashgari Uri Maoz
author_sort	Elnaz Lashgari
title	Dimensionality reduction for classification of object weight from electromyography.
title_short	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_sort	dimensionality reduction for classification of object weight from electromyography.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/0922b1f29d4546e69ed2872a2bb58cbe
work_keys_str_mv	AT elnazlashgari dimensionalityreductionforclassificationofobjectweightfromelectromyography AT urimaoz dimensionalityreductionforclassificationofobjectweightfromelectromyography
_version_	1718374327176396800

Dimensionality reduction for classification of object weight from electromyography.

Ejemplares similares