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
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Publicado: Public Library of Science (PLoS) 2021
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spelling oai:doaj.org-article:9363052d3c174eb89625ae2cc551cfed2021-11-04T07:42:06ZDecomposing spontaneous sign language into elementary movements: A principal component analysis-based approach1932-6203https://doaj.org/article/9363052d3c174eb89625ae2cc551cfed2021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555838/?tool=EBIhttps://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 (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/9363052d3c174eb89625ae2cc551cfed
work_keys_str_mv AT felixbigand decomposingspontaneoussignlanguageintoelementarymovementsaprincipalcomponentanalysisbasedapproach
AT eliseprigent decomposingspontaneoussignlanguageintoelementarymovementsaprincipalcomponentanalysisbasedapproach
AT bastienberret decomposingspontaneoussignlanguageintoelementarymovementsaprincipalcomponentanalysisbasedapproach
AT anneliesbraffort decomposingspontaneoussignlanguageintoelementarymovementsaprincipalcomponentanalysisbasedapproach
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