Details

Autor(en) / Beteiligte

• Félix Bigand
• Elise Prigent
• Bastien Berret
• Annelies Braffort

Titel

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

Ist Teil von

• PloS one, 2021-01, Vol.16 (10)

Ort / Verlag

Public Library of Science (PLoS)

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.