Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach.

Sign Language (SL) is a continuous and complex stream of multiple body movement features. That raises the challenging issue of providing efficient computational models for the description and analysis of these movements. In the present paper, we used Principal Component Analysis (PCA) to decompose S...

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Autores principales:	Félix Bigand, Elise Prigent, Bastien Berret, Annelies Braffort
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7bd84600f735455bb76a80db69317c32
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spelling	oai:doaj.org-article:7bd84600f735455bb76a80db69317c322021-12-02T20:13:20ZDecomposing spontaneous sign language into elementary movements: A principal component analysis-based approach.1932-620310.1371/journal.pone.0259464https://doaj.org/article/7bd84600f735455bb76a80db69317c322021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259464https://doaj.org/toc/1932-6203Sign Language (SL) is a continuous and complex stream of multiple body movement features. That raises the challenging issue of providing efficient computational models for the description and analysis of these movements. In the present paper, we used Principal Component Analysis (PCA) to decompose SL motion into elementary movements called principal movements (PMs). PCA was applied to the upper-body motion capture data of six different signers freely producing discourses in French Sign Language. Common PMs were extracted from the whole dataset containing all signers, while individual PMs were extracted separately from the data of individual signers. This study provides three main findings: (1) although the data were not synchronized in time across signers and discourses, the first eight common PMs contained 94.6% of the variance of the movements; (2) the number of PMs that represented 94.6% of the variance was nearly the same for individual as for common PMs; (3) the PM subspaces were highly similar across signers. These results suggest that upper-body motion in unconstrained continuous SL discourses can be described through the dynamic combination of a reduced number of elementary movements. This opens up promising perspectives toward providing efficient automatic SL processing tools based on heavy mocap datasets, in particular for automatic recognition and generation.Félix BigandElise PrigentBastien BerretAnnelies BraffortPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0259464 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Félix Bigand Elise Prigent Bastien Berret Annelies Braffort Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach.
description	Sign Language (SL) is a continuous and complex stream of multiple body movement features. That raises the challenging issue of providing efficient computational models for the description and analysis of these movements. In the present paper, we used Principal Component Analysis (PCA) to decompose SL motion into elementary movements called principal movements (PMs). PCA was applied to the upper-body motion capture data of six different signers freely producing discourses in French Sign Language. Common PMs were extracted from the whole dataset containing all signers, while individual PMs were extracted separately from the data of individual signers. This study provides three main findings: (1) although the data were not synchronized in time across signers and discourses, the first eight common PMs contained 94.6% of the variance of the movements; (2) the number of PMs that represented 94.6% of the variance was nearly the same for individual as for common PMs; (3) the PM subspaces were highly similar across signers. These results suggest that upper-body motion in unconstrained continuous SL discourses can be described through the dynamic combination of a reduced number of elementary movements. This opens up promising perspectives toward providing efficient automatic SL processing tools based on heavy mocap datasets, in particular for automatic recognition and generation.
format	article
author	Félix Bigand Elise Prigent Bastien Berret Annelies Braffort
author_facet	Félix Bigand Elise Prigent Bastien Berret Annelies Braffort
author_sort	Félix Bigand
title	Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach.
title_short	Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach.
title_full	Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach.
title_fullStr	Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach.
title_full_unstemmed	Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach.
title_sort	decomposing spontaneous sign language into elementary movements: a principal component analysis-based approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7bd84600f735455bb76a80db69317c32
work_keys_str_mv	AT felixbigand decomposingspontaneoussignlanguageintoelementarymovementsaprincipalcomponentanalysisbasedapproach AT eliseprigent decomposingspontaneoussignlanguageintoelementarymovementsaprincipalcomponentanalysisbasedapproach AT bastienberret decomposingspontaneoussignlanguageintoelementarymovementsaprincipalcomponentanalysisbasedapproach AT anneliesbraffort decomposingspontaneoussignlanguageintoelementarymovementsaprincipalcomponentanalysisbasedapproach
_version_	1718374774201122816

Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach.

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