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NeuroPose

3D human pose estimation pipeline for clinical movement research, built on MeTRAbs. Developed by the Shu Lab at Brown University.

!!! warning "Pre-alpha software" NeuroPose is under active development at version 0.1.0.dev0. APIs, schemas, and the command-line interface may change without notice between commits until the first tagged release. This is research software and must not be used for clinical decision-making.

What NeuroPose does

NeuroPose takes a video (or a directory of videos organised into "jobs"), runs the MeTRAbs 3D pose-estimation model on every frame, and produces a validated JSON output containing per-frame 3D and 2D joint positions and the original video's metadata (frame count, fps, resolution). The output schema is designed to be loaded back into Python, numpy, or any downstream analysis pipeline without ambiguity.

Three core components:

neuropose.estimator — the per-video inference worker. Streams frames from an input video, runs MeTRAbs on each one, and returns a validated VideoPredictions object. No filesystem or job-queue semantics.
neuropose.interfacer — a filesystem-polling daemon that watches an input directory for new job subdirectories, dispatches each to the estimator, and manages the status-file lifecycle.
neuropose.analyzer — a post-processing subpackage for motion analysis and classification (FastDTW, joint-angle features, sktime). (Pending the rewrite in commit 10.)

Where to go next

:material-rocket-launch: Getting Started — install, run your first job, understand the output.
:material-cube-outline: Architecture — how the pieces fit together and why.
:material-api: API Reference — auto-generated from the source docstrings.
:material-tools: Development — contributing, testing, and the release workflow.
:material-server: Deployment — running the daemon in production.

Intended use

NeuroPose is built for:

Clinical gait and movement-assessment research
Biomechanics work using standard RGB video
Research reproducibility — the output schema carries enough metadata (frame count, fps, resolution) to recover real time from frame indices without needing access to the original video.

It is not intended for:

Clinical diagnosis or treatment decisions.
General-purpose motion capture outside the research use cases actively supported by the Shu Lab.

Citing NeuroPose

If you use NeuroPose in academic work, please cite it using the metadata in CITATION.cff. A DOI and a manuscript citation will be added once the first paper is submitted.

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