  There has been significant progress in machine learning algorithms for human
pose estimation that may provide immense value in rehabilitation and movement
sciences. However, there remain several challenges to routine use of these
tools for clinical practice and translational research, including: 1) high
technical barrier to entry, 2) rapidly evolving space of algorithms, 3)
challenging algorithmic interdependencies, and 4) complex data management
requirements between these components. To mitigate these barriers, we developed
a human pose estimation pipeline that facilitates running state-of-the-art
algorithms on data acquired in clinical context. Our system allows for running
different implementations of several classes of algorithms and handles their
interdependencies easily. These algorithm classes include subject
identification and tracking, 2D keypoint detection, 3D joint location
estimation, and estimating the pose of body models. The system uses a database
to manage videos, intermediate analyses, and data for computations at each
stage. It also provides tools for data visualization, including generating
video overlays that also obscure faces to enhance privacy. Our goal in this
work is not to train new algorithms, but to advance the use of cutting-edge
human pose estimation algorithms for clinical and translation research. We show
that this tool facilitates analyzing large numbers of videos of human movement
ranging from gait laboratories analyses, to clinic and therapy visits, to
people in the community. We also highlight limitations of these algorithms when
applied to clinical populations in a rehabilitation setting.
