--- license: cc-by-nc-nd-4.0 ---

Overview

Please read carefully the terms and conditions and any accompanying documentation at neurips2025.care-pd.ca/terms-of-use before you download and/or use the CARE-PD dataset.

Project page: https://neurips2025.care-pd.ca/

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 362 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 for 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 (original license: CC BY 4.0)
  3. DNE – Contains healthy, Parkinson's, and other neurological conditions (original license: CC BY 4.0)
  4. E-LC – Medication status (on/off) and PD subtypes
  5. KUL-DT-T – Freezer/non-freezer subtypes
  6. PD-GaM – Clinical gait recordings with UPDRS scores
  7. T-SDU – Ambient walking recordings
  8. T-SDU-PD – PD patient walking with UPDRS scores
  9. T-LTC – Ambient walking recordings

Canonicalized SMPL files

*_canonical.pkl files in the Canonicalized_SMPL_pickles folder keep the same nested dataset format as the original pickles. The canonical versions change only the motion coordinates:

pose/trans are rotated so the motion uses a shared coordinate system. 

x = lateral
y = up
z = forward

They also preprocess translation so:

Note: KUL-DT-T and E-LC are the only datasets that are not purely straight walking sequences, so for these two datasets the subject is canonicalized to start facing z+ in the first frame.

Data Structure

The main SMPL datasets are 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
        }
    }
}

Additionally, we provide h36m, HumanML3D, and SMPL_6D formats.

Getting Started

Please refer to https://github.com/TaatiTeam/CARE-PD for getting started with the dataset.

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
  2. Data Privacy and Ethics
  3. Data Handling and Security
  4. Intellectual Property Notice
  5. Disclaimer of Warranty

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

Citation

If you use CARE-PD in your research, please cite:

Adeli V, Klabučar I, Rajabi J, Filtjens B, Mehraban S, Wang D, Seo H, Hoang T-H, Do MN, Muller C, Neves de Oliveira C, Boari Coelho D, Ginis P, Gilat M, Nieuwboer A, Spildooren J, McKay JL, Kwon H, Clifford G, Esper CD, Factor SA, Genias I, Dadashzadeh A, Shum L, Whone A, Mirmehdi M, Iaboni A, Taati B. CARE-PD: A Multi-Site Anonymized Clinical Dataset for Parkinson’s Disease Gait Assessment. In: Advances in Neural Information Processing Systems (NeurIPS); 2025.

Additionally, please cite the relevant datasets:

  1. 3DGait
  2. BMCLab
  3. DNE
  4. E-LC
  5. KUL-DT-T
  6. PD-GaM
  7. T-SDU
  8. T-SDU-PD
  9. T-LTC

Acknowledgments

We thank all participating research institutions and subjects who made this dataset possible.