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The Modular Actigraphy Platform: A Data Science Solution for Processing High-Resolution Time Series Sensor Data for

Pin-Wei Chen1, Dipriya A Pillai2, Michael S Campagna2

  • 1Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA.

Medrxiv : the Preprint Server for Health Sciences
|December 3, 2025
PubMed
Summary

The Modular Actigraphy Platform (MAP) efficiently processes raw wearable sensor data for sleep and physical activity research. This cloud-based system integrates open-source algorithms, enhancing data rigor and reproducibility in clinical studies.

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Area of Science:

  • Biomedical Engineering
  • Clinical Research Informatics
  • Wearable Technology

Background:

  • Traditional wearables use proprietary scoring, limiting transparency and reproducibility.
  • The need for robust data infrastructure to process raw sensor data in clinical research is growing.
  • Open-source scoring methods are emerging to improve rigor in sleep and physical activity assessment.

Purpose of the Study:

  • To develop a cloud-based computational platform, the Modular Actigraphy Platform (MAP), for processing raw wearable sensor data.
  • To integrate flexible, modular data processing capabilities for sleep and physical activity metrics.
  • To facilitate the incorporation of emerging open-source scoring algorithms.

Main Methods:

  • MAP was developed using a structured Software Development Life Cycle (SDLC) with multi-level testing.
  • Integrated open-source algorithms include GGIR and MIMS for sleep and physical activity scoring.
  • User acceptance testing involved alpha (17 files) and beta (686 files from 4 pediatric cohorts) phases.

Main Results:

  • MAP is a cloud-based platform processing high-resolution time series sensor data for sleep and activity metrics.
  • Beta testing demonstrated MAP's efficiency, leveraging up to 60 CPU cores and 500 GiB memory.
  • MAP processing was significantly faster than offline methods for GGIR (1.6-2.9x) and MIMS (2.4-14.0x) pre-processing.

Conclusions:

  • MAP provides an efficient computational solution for processing raw sensor data from wearables.
  • The platform enhances the estimation of sleep and physical activity in clinical research settings.
  • MAP supports the integration of open-source algorithms, promoting rigor and reproducibility.