  Signed languages are visual languages produced by the movement of the hands,
face, and body. In this paper, we evaluate representations based on skeleton
poses, as these are explainable, person-independent, privacy-preserving,
low-dimensional representations. Basically, skeletal representations generalize
over an individual's appearance and background, allowing us to focus on the
recognition of motion. But how much information is lost by the skeletal
representation? We perform two independent studies using two state-of-the-art
pose estimation systems. We analyze the applicability of the pose estimation
systems to sign language recognition by evaluating the failure cases of the
recognition models. Importantly, this allows us to characterize the current
limitations of skeletal pose estimation approaches in sign language
recognition.
