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CARE-PD: A Comprehensive 3D Gait Dataset for Parkinson's Disease

Overview

CARE-PD is the largest publicly available archive of 3D mesh gait data for Parkinson's Disease (PD) and the first to include data collected across multiple sites. The dataset aggregates 9 cohorts from 8 clinical sites, including 363 participants spanning a range of disease severity. All recordings—whether from RGB video or motion capture—are unified into anonymized SMPL body gait meshes through a curated harmonization pipeline.

This dataset enables two main benchmarks:

  1. Supervised clinical score prediction: Estimating UPDRS gait scores from 3D meshes
  2. Unsupervised motion pretext tasks: Parkinsonian gait representation learning

Dataset Contents

CARE-PD consists of 9 harmonized datasets:

  1. 3DGait - Clinical gait recordings with UPDRS scores
  2. BMCLab - Gait recordings with medication status and UPDRS scores
  3. DNE - Contains healthy, Parkinson's, and other neurological conditions
  4. E-LC - Medication status (on/off) with freezing events
  5. KUL-DT-T - Freezer/non-freezer classification data
  6. PD-GaM - Clinical gait recordings with UPDRS scores
  7. T-SDU - General walking recordings
  8. T-SDU-PD - PD patient walking with UPDRS scores
  9. T-LTC - Additional walking recordings

Data Structure

Each dataset is provided in a standardized format:

{
    "anonymized_subject_id": {
        "anonymized_walk_id": {
            "pose": array,      # SMPL pose parameters (shape varies by dataset)
            "trans": array,     # Translation data
            "beta": array,      # Body shape parameters (zeros for privacy)
            "fps": int,         # Frames per second (standardized)
            "UPDRS_GAIT": int,  # Clinical score (0-3) or None if unavailable
            "medication": str,  # Medication status or None if unavailable
            "other": str        # Additional labels or None if unavailable
        }
    }
}

Getting Started

Loading Data

We provide utility functions for loading the 3D skeletal data in the repository.

Please refer to the dataset setup detail in README.md.

Data Visualization

We provide utility functions for visualizing the 3D skeletal data in the repository.

python utility/viz_seqs.py -n assets/datasets/h36m/BMCLab/h36m_3d_world2cam2img_sideright_floorXZZplus_30f_or_longer.npz  -f h36m -p 2d

python utility/viz_seqs.py -n assets/datasets/h36m/BMCLab/h36m_3d_world_floorXZZplus_30f_or_longer.npz  -f h36m

You can refer to the motion-latent-diffusion (MLD) repository (link) for code for rendering SMPL.

Benchmarks

CARE-PD includes data splits to test generalization:

  1. 6-Fold (split per subject)
  2. Leave-one-subject-out
  3. Fixed train-test splits (split per subject)

The former two are only provided for the supervised clinical score prediction task.

Terms of Use

By accessing and using this database (the "Database"), users ("Users") acknowledge and agree to comply with the following conditions:

1. License and Attribution

  • The Database is publicly released under a Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license.
  • Users must provide appropriate attribution by citing the Datbase and the original publications associated with each dataset accessed from the Database.

2. Data Privacy and Ethics

  • Users must not attempt to identify, contact, or otherwise compromise the anonymity of any individuals whose data is included in the Database.
  • All use of the data must comply with applicable ethical guidelines and legal regulations, including privacy laws (e.g., GDPR, HIPAA, PIPEDA).

3. Data Handling and Security

  • Users must maintain appropriate data security measures to prevent unauthorized access, sharing, or use of the data.
  • Users are encouraged, but not required, to direct third parties to the original Database URL rather than re-hosting the data.

4. Intellectual Property Notice

  • Copyright and other rights remain with the original data providers.

5. Disclaimer of Warranty

  • Use of the Database is subject to Section 5 (Disclaimer of Warranties and Limitation of Liability) of the CC BY-NC 4.0 licence.

By using the Database, Users expressly acknowledge and agree to abide by these Terms of Use.