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Bibliographic Details
Main Author: Surya Teja
Format: Recurso digital
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Published: Zenodo 2026
Online Access:https://doi.org/10.5281/zenodo.19106797
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Table of Contents:
  • <p>Initial release of the PGSI pipeline accompanying the paper:</p> <p>"PGSI: A Novel Pose-Derived Composite Index for Quantitative Parkinsonian Gait Severity Assessment Using Markerless Video Analysis"</p> <h2>Contents</h2> <ul> <li>Full PGSI pipeline (feature extraction, scoring, classification)</li> <li>Demo recorder script</li> <li>Results extractor for KOA-PD-NM dataset</li> <li>Configuration file (config.py)</li> </ul> <h2>Dataset</h2> <p>Evaluated on KOA-PD-NM (DOI: 10.17632/44pfnysy89.1)</p> <h2>Requirements</h2> <p>Python 3.10+, MediaPipe, OpenCV, NumPy, SciPy, scikit-learn</p>