Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Jan 24, 2022
Date Accepted: Mar 29, 2022
Date Submitted to PubMed: Apr 22, 2022
Scikit Digital Health: A Python package for Streamlined Wearable Inertial Sensor Data Processing
ABSTRACT
Background:
Wearable inertial sensors are providing enhanced insight into patient mobility and health. Significant research efforts have focused on wearable algorithm design and deployment in both research and clinical settings, however, open-source, general-purpose software tools for processing various activities of daily living are relatively scarce. Furthermore, few studies include code for replication or off-the-shelf software packages.
Objective:
In this work, we introduce SciKit Digital Health (SKDH), a Python software package containing various algorithms for deriving clinical features of gait, sit-to-stand, physical activity, and sleep, wrapped in an easily extensible framework.
Methods:
Results:
SKDH combines data ingestion, pre-processing, and data analysis methods geared towards modern data science workflows and streamlines the generation of digital endpoints in ``good practice" (GxP) environments by combining all the necessary data processing steps in a single pipeline. Our package simplifies construction of new data processing pipelines and promotes reproducibility by following a convention over configuration approach, standardizing most settings on physiologically reasonable defaults in healthy or mildly impaired adult populations. SKDH is open source, free to use, and extend under a permissive MIT license and available from GitHub (PfizerRD/scikit-digital-health), as well as the Python Package Index and Anaconda (conda-forge channel).
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Copyright
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