Exploring Sub-skeleton Trajectories for Interpretable Recognition of Sign Language

Joachim Gudmundsson, Martin P. Seybold, John Pfeifer

Abstract: Recent advances in tracking sensors and pose estimation software enable smart systems to use trajectories of skeleton joint locations for supervised learning. We study the problem of accurately recognizing sign language words, which is key to narrowing the communication gap between hard and non-hard of hearing people.