Multivariate Functional Principal Component Analysis in Sign Language

Abstract

The aim of this paper is to establish a novel statistical methods for characterizing the sign language movements at multiple body parts simultaneously. The method we applied is the multivariate functional principal components analysis (MFPCA), which is capable of capturing the individual variation of sign language movements using not only palm movements but also multiple movements such as fingers, elbows, and shoulders. This method successfully captures the characteristic that sign language is composed of a combination of multiple consecutive actions. We apply MFPCA to quantify the differences in variation of the performance among ten beginner and one master of the sing languages measured at nineteen body parts. The results of MFPCA quantify the individual qualities for the sign languages by making use of multivariate function principal component scores. At the same time, MFPCA revealed which part the characteristic movement of the individual sign language appears strongly. Finally, we distinguished some words that tend to be difficult or easy to learn.