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There has been significant progress in machine learning algorithms for humanpose estimation that may provide immense value in rehabilitation and movementsciences. However, there remain several challenges to routine use of thesetools for clinical practice and translational research, including: 1) hightechnical barrier to entry, 2) rapidly evolving space of algorithms, 3)challenging algorithmic interdependencies, and 4) complex data managementrequirements between these components. To mitigate these barriers, we developeda human pose estimation pipeline that facilitates running state-of-the-artalgorithms on data acquired in clinical context. Our system allows for runningdifferent implementations of several classes of algorithms and handles theirinterdependencies easily. These algorithm classes include subjectidentification and tracking, 2D keypoint detection, 3D joint locationestimation, and estimating the pose of body models. The system uses a databaseto manage videos, intermediate analyses, and data for computations at eachstage. It also provides tools for data visualization, including generatingvideo overlays that also obscure faces to enhance privacy. Our goal in thiswork is not to train new algorithms, but to advance the use of cutting-edgehuman pose estimation algorithms for clinical and translation research. We showthat this tool facilitates analyzing large numbers of videos of human movementranging from gait laboratories analyses, to clinic and therapy visits, topeople in the community. We also highlight limitations of these algorithms whenapplied to clinical populations in a rehabilitation setting.

PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research