ParaDigMa logo

Badges

Packages and Releases

Latest release PyPI Static Badge

DOI

DOI

Build Status

Build and test pages-build-deployment

License

GitHub license

Overview

ParaDigMa (Parkinson’s disease Digital Markers) is a Python toolbox for extracting validated digital biomarkers from wrist sensor data in Parkinson’s disease. It processes accelerometer, gyroscope, and PPG signals collected during passive monitoring in daily life.

Key Features:

  • Arm swing during gait analysis

  • Tremor analysis

  • Pulse rate analysis

  • Scientifically validated in peer-reviewed publications

  • Modular, extensible architecture for custom analyses

Quick Start

Installation

For regular use:

pip install paradigma

Requires Python 3.11+.

For development or running tutorials:

Example data requires git-lfs. See the installation guide for setup instructions.

Basic Usage

from paradigma.orchestrator import run_paradigma

# Example 1: Single DataFrame with default output directory
results = run_paradigma(
    dfs=df,
    pipelines=['gait', 'tremor'],
    watch_side='left',  # Required for gait pipeline
    save_intermediate=['quantification', 'aggregation']  # Saves to ./output by default
)

# Example 2: Multiple DataFrames as list (assigned to 'df_1', 'df_2', etc.)
results = run_paradigma(
    dfs=[df1, df2, df3],
    pipelines=['gait', 'tremor'],
    output_dir="./results",  # Custom output directory
    watch_side='left',
    save_intermediate=['quantification', 'aggregation']
)

# Example 3: Dictionary of DataFrames (custom segment/file names)
results = run_paradigma(
    dfs={'morning_session': df1, 'evening_session': df2},
    pipelines=['gait', 'tremor'],
    watch_side='right',
    save_intermediate=[]  # No files saved - results only in memory
)

# Example 4: Load from data directory
results = run_paradigma(
    data_path='./my_data',
    pipelines=['gait', 'tremor'],
    watch_side='left',
    file_pattern='*.parquet',
    save_intermediate=['quantification', 'aggregation']
)

# Access results (nested by pipeline)
gait_measures = results['quantifications']['gait']
tremor_measures = results['quantifications']['tremor']
gait_aggregates = results['aggregations']['gait']
tremor_aggregates = results['aggregations']['tremor']

# Check for errors
if results['errors']:
    print(f"Warning: {len(results['errors'])} error(s) occurred")

See our tutorials for complete examples.

Pipelines

Pipeline architeecture

Validated Processing Pipelines

Pipeline

Input sensors

Output week-level aggregation

Publications

Tutorial

Arm swing during gait

Accelerometer + Gyroscope

Typical, maximum & variability of arm swing range of motion

Post 2025, Post 2026*

Guide

Tremor

Gyroscope

% tremor time, typical & maximum tremor power

Timmermans 2025a, Timmermans 2025b*

Guide

Pulse rate

PPG (+ Accelerometer)

Resting & maximum pulse rate

Veldkamp 2025*

Guide

* Indicates pre-print

Pipeline Architecture

ParaDigMa can best be understood by categorizing the sequential processes:

Process

Description

Preprocessing

Preparing raw sensor signals for further processing

Feature extraction

Extracting features based on windowed sensor signals

Classification

Detecting segments of interest using validated classifiers (e.g., gait segments)

Quantification

Extracting specific measures from the detected segments (e.g., arm swing measures)

Aggregation

Aggregating the measures over a specific time period (e.g., week-level aggregates)

Usage

Documentation

Sensor Requirements & Supported Devices

ParaDigMa is designed for wrist sensor data collected during passive monitoring in persons with Parkinson’s disease. While designed to work with any compliant device, it has been empirically validated on:

  • Verily Study Watch (gait, tremor, pulse rate)

  • Axivity AX6 (gait, tremor)

  • Gait-up Physilog 4 (gait, tremor)

  • Empatica EmbracePlus (data loading)

Please check before running the pipelines whether your sensor data complies with the requirements for the sensor configuration and context of use. See the sensor requirements guide for data specifications and the supported devices guide for device-specific setup instructions.

Data Formats

ParaDigMa supports the following data formats:

  • In-memory (recommended): Pandas DataFrames (see examples above)

  • Data loading file extensions: TSDF, Parquet, CSV, Pickle and several device-specific formats (AVRO (Empatica), CWA (Axivity))

Troubleshooting

For installation issues, see the installation guide troubleshooting section.

For other issues, check our issue tracker or contact paradigma@radboudumc.nl.

Scientific Validation

ParaDigMa pipelines are validated in peer-reviewed publications:

Pipeline

Publication

Arm swing during gait

Post et al. (2025, 2026)

Tremor

Timmermans et al. (2025a, 2025b)

Pulse rate

Veldkamp et al. (2025)

See the validation guide for full publication details.

Contributing

We welcome contributions! Please see:

Citation

If you use ParaDigMa in your research, please cite:

@software{paradigma2024,
  author = {Post, Erik and Veldkamp, Kars and Timmermans, Nienke and
            Soriano, Diogo Coutinho and Kasalica, Vedran and
            Kok, Peter and Evers, Luc},
  title = {ParaDigMa: Parkinson's disease Digital Markers},
  year = {2024},
  doi = {10.5281/zenodo.13838392},
  url = {https://github.com/biomarkersParkinson/paradigma}
}

License

Licensed under the Apache License 2.0. See LICENSE for details.

Acknowledgements

Core Team: Erik Post, Kars Veldkamp, Nienke Timmermans, Diogo Coutinho Soriano, Vedran Kasalica, Peter Kok, Twan van Laarhoven, Luc Evers

Advisors: Max Little, Jordan Raykov, Hayriye Cagnan, Bas Bloem

Funding: the initial release was funded by the Michael J Fox Foundation (grant #020425) and the Dutch Research Council (grants #ASDI.2020.060, #2023.010)

Contact

User guides

Developer docs

API