Classification of gesture with layered meanings

Abstract

Automatic sign language recognition research has largely not addressed an integral aspect of sign language communication - grammatical inflections which are conveyed through systematic temporal and spatial movement modifications. We propose to use low-level static and dynamic classifiers, together with Bayesian Networks, to classify gestures that include these inflections layered on top of the basic meaning. With a simulated vocabulary of 6 basic signs and 4 different layered meanings, test data for four test subjects was classified with 84.6% accuracy.