There is a newer version of the record available.

Published February 5, 2026 | Version v1.1.0
Software Open

ParaDigMa: A toolbox for deriving Parkinson's disease Digital Markers from real-life wrist sensor data

  • 1. Radboud University, Nijmegen, Netherlands; Radboud University Medical Center, Nijmegen, Netherlands
  • 2. Radboud University Medical Center, Nijmegen, Netherlands
  • 3. Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Brazil
  • 4. Netherlands eScience Center
  • 5. Imperial College London
  • 6. University of Birmingham
  • 7. University of Nottingham
  • 8. Radboud University, Nijmegen, Netherlands

Description

The Parkinson's disease Digital Markers (ParaDigMa) toolbox is a Python software package designed for processing and analyzing real-life wrist sensor data to extract digital measures of motor and non-motor signs of Parkinson's disease (PD). The toolbox processes accelerometer, gyroscope and photoplethysmography signals collected during passive monitoring in daily life. It contains three scientifically validated data processing pipelines: (1) arm swing during gait, (2) tremor, and (3) pulse rate analysis. An orchestrator function enables end-to-end processing from raw data loading to aggregated measures, with automatic data preparation including flexible column mapping and sensor orientation adjustment. The toolbox also provides general functionalities for signal processing and feature extraction, such as filtering, peak detection, and spectral analysis. ParaDigMa is accompanied by comprehensive documentation including tutorials, installation guides, and API reference. The modular architecture enables researchers to easily extend the toolbox with custom algorithms and functionalities while maintaining compatibility with standardized data formats.

Notes

If you use this software, please cite it using the metadata from this file.

Files

biomarkersParkinson/paradigma-v1.1.0.zip

Files (21.7 MB)

Name Size Download all
md5:db6aa3758fc837ad0a5879c56c59337e
21.7 MB Preview Download

Additional details

Related works