Related Experiment Video
Updated: Jan 9, 2026
Multi-Modal Home Sleep Monitoring in Older Adults
Published on: January 26, 2019
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.
Introduction:
Wearables with proprietary scoring protocols are typically used to assess sleep and physical activity, but the field is shifting to wearables with raw sensor data accessible and open-source scoring methods to enhance rigor and reproducibility. The data infrastructure to process raw sensor data in clinical research is underdeveloped; we therefore developed the Modular Actigraphy Platform (MAP).
Methods:
MAP is a cloud-based computational platform that processes high-resolution time series sensor data to derive sleep and physical activity metrics. It was engineered to be modular, providing flexibility in data processing and enabling the seamless integration of open-source sleep and physical activity scoring methodologies as they become available (currently, GGIR and MIMS processing algorithms have been integrated). A structured Software Development Life Cycle (SDLC) approach was used to guide the development of MAP, with a multi-level testing framework consisting of unit testing (verification of modules), integration testing (interaction among modules), and system testing (validating specifications). Following these foundational tests, we then completed user acceptance testing in two phases - alpha (17 test files) and beta (686 files from 4 pediatric cohorts) to assess processing performance.
Results:
For beta testing, MAP leveraged up to 60 CPU cores and 500 GiB of memory. The pre-processing module was the most computationally demanding and was more efficient in MAP compared to offline processing (up to 8 CPU cores and 23.2 GiB of memory). For example, the preprocessing GGIR part 1 container was completed at a speed of 0.29-0.49 minutes per file (1.6-2.9 times faster than offline processing) and the pre-processing MIMS container was completed at a speed of 0.49-4.66 minutes per file (2.4 to 14.0 times faster than offline processing).
Conclusion:
MAP is an efficient computational platform that integrates open-source scoring algorithms to efficiently process raw sensor data for wearable sleep and physical activity estimation in clinical research and is available through the Children's Hospital of Philadelphia's Research Institute.
