ArticleJMIR mHealth and uHealth2022
SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing.
Article in JMIR mHealth and uHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Individual-Level Modeling of Depressive Symptom Severity Using Smartphone and Wearable Data: Time-Aware 1-Year Study With Feature-Group Contributions.JMIR formative research · 2026Article
- A Head-to-Head Comparison of Three Literature Algorithms for Physical Activity Endpoints from a Wrist Accelerometer in Free-Living Conditions.Sensors (Basel, Switzerland) · 2026Article
- Mobgap: A State-of-the-Art Python Framework for Reproducible Estimation and Algorithm Validation of Digital Mobility Outcomes from a Single Wearable Device.Sensors (Basel, Switzerland) · 2026Article
- Lumbar Acceleration Gait Estimation: "Step-by-Step" Algorithm Updates and Improvements.Journal of medical Internet research · 2025Article
- MoveLabSensors (Basel, Switzerland) · 2025Article
- Every Step Counts-How Can We Accurately Count Steps with Wearable Sensors During Activities of Daily Living in Individuals with Neurological Conditions?Sensors (Basel, Switzerland) · 2025Article
- Deep Learning Predicts Postoperative Mobility, Activities of Daily Living, and Discharge Destination in Older Adults from Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- Alzheimer's disease digital biomarkers multidimensional landscape and AI model scoping review.NPJ digital medicine · 2025Article
- Improved functional assessment in cancer patients using home-based digital technologies.Scientific reports · 2025Article
- Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait.Journal of medical Internet research · 2025Article
- NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour.BMC digital health · 2024Article
- In-Clinic and Natural Gait Observations master protocol (I-CAN-GO) to validate gait using a lumbar accelerometer.Scientific reports · 2024Article
- Monitoring Activity and Gait in Children (MAGIC) using digital health technologies.Pediatric research · 2024Article
- Measuring gait parameters from a single chest-worn accelerometer in healthy individuals: a validation study.Scientific reports · 2024Article
- DISPEL: A Python Framework for Developing Measures From Digital Health Technologies.IEEE open journal of engineering in medicine and biology · 2024Article
- Article
- Gaitmap-An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking.IEEE open journal of engineering in medicine and biology · 2024Article
- Monitoring Gait and Physical Activity of Elderly Frail Individuals in Free-Living Environment: A Feasibility Study.Gerontology · 2024Article
- Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?Sensors (Basel, Switzerland) · 2023Article
- SciKit digital health package for accelerometry-measured physical activity: comparisons to existing solutions and investigations of age effects in healthy adults.Frontiers in digital health · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. In this work, we introduce SciKit Digital Health (SKDH), a Python software package (Python Software Foundation) containing various algorithms for deriving clinical features of gait, sit to stand, physical activity, and sleep, wrapped in an easily extensible framework. SKDH combines data ingestion, preprocessing, and data analysis methods geared toward modern data science workflows and streamlines the generation of digital endpoints in "good practice" environments by combining all the necessary data processing steps in a single pipeline. Our package simplifies the 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 adult populations or those with mild impairment. SKDH is open source, as well as free to use and extend under a permissive Massachusetts Institute of Technology license, and is available from GitHub (PfizerRD/scikit-digital-health), the Python Package Index, and the conda-forge channel of Anaconda.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.