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

TL;DR

PosePipe is an open-source, modular pipeline designed to simplify the application of state-of-the-art human pose estimation algorithms in clinical research, addressing technical barriers and data management challenges.

Contribution

This work introduces a flexible, integrated pipeline that manages complex dependencies and data workflows for human pose estimation in clinical settings, facilitating translational research.

Findings

Enables analysis of large clinical video datasets

Supports multiple pose estimation algorithms and workflows

Identifies limitations of algorithms in rehabilitation populations

Abstract

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.