Learning and manipulating human's fingertip bending data for sign language translation using PCA-BMU classifier

Abstract

Nowadays the classification of fingers movement could be used to classify or categorize many kinds of human finger motions including the classification of sign language for verbal communication.Principal Component Analysis (PCA) is one of classical method that capable to be verity the finger motions for various alphabets by reducing the dimensional dataset of finger movements.The objective of this paper is to analyze the human finger motions / movements between thumbs,index and middle fingers while bending the fingers using PCA-BMU based techniques. The used of low cost DataGlove “GloveMAP” which is based on fingers adapted postural movement (or EigenFingers) of the principal component was applied in order to translate the finger bending to the sign language alphabets. Preliminary experimental results have shown that the “GloveMAP” DataGlove capable to measure several human Degree of Freedom (DoF), by “translating” them into a virtual commands for the interaction in the virtual world