  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.
  Our method explores a geometric feature space that we call `sub-skeleton'
aspects of movement. We assess similarity of feature space trajectories using
natural, speed invariant distance measures, which enables clear and insightful
nearest neighbor classification. The simplicity and scalability of our basic
method allows for immediate application in different data domains with little
to no parameter tuning.
  We demonstrate the effectiveness of our basic method, and a boosted
variation, with experiments on data from different application domains and
tracking technologies. Surprisingly, our simple methods improve sign
recognition over recent, state-of-the-art approaches.
