| PyPI | About | Repo | Cite |
|---|---|---|---|
accelerometer |
A Python Toolkit for Extracting Physical Activity and Behavior Metrics from Wearable Sensor Data | OxWearables/biobankAccelerometerAnalysis | Citation |
actipy |
A Python toolkit to process wearable sensor data | OxWearables/actipy | Citation |
stepcount |
Improved Step Counting via Foundation Models for Wrist-Worn Accelerometers | OxWearables/stepcount | Citation |
asleep |
A sleep classifier for wearable sensor data using machine learning | OxWearables/asleep | Citation |
actinet |
An activity classification model based on self-supervised learning for wrist-worn accelerometer data. | OxWearables/actinet | Citation |
| -- | Self-supervised learning model for wearables using the UK-Biobank (>700,000 person-days) | OxWearables/ssl-wearables | Citation |
Please note that a license is required for non-academic users. For further details, kindly contact: https://innovation.ox.ac.uk/about/people/karl-dickinson/
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Fulda ES, Portas L, Harper C, Preiss D, Bennett D, Doherty A. Association of Daily Steps with Incident Non-Alcoholic Fatty Liver Disease: Evidence from the UK Biobank Cohort. Med Sci Sports Exerc. 2025 Apr 24:10.1249/MSS.0000000000003738. doi: 10.1249/MSS.0000000000003738. Epub ahead of print. PMID: 40279651; PMCID: PMC7617666. https://doi.org/10.1249/MSS.0000000000003738
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Shreves AH, Small SR, Walmsley R, et alAmount and intensity of daily total physical activity, step count and risk of incident cancer in the UK BiobankBritish Journal of Sports Medicine Published Online First: 26 March 2025. doi: 10.1136/bjsports-2024-109360 https://doi.org/10.1136/bjsports-2024-109360
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Chan, S., Hang, Y., Tong, C. et al. CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition. Sci Data 11, 1135 (2024). https://doi.org/10.1038/s41597-024-03960-3
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Yuan, H., Hill, E. A., Kyle, S. D., & Doherty, A. (2024). A systematic review of the performance of actigraphy in measuring sleep stages. Journal of Sleep Research, 33(5), e14143. https://doi.org/10.1111/jsr.14143
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Yuan, H., Plekhanova, T., Walmsley, R. et al. Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality. npj Digit. Med. 7, 86 (2024). https://doi.org/10.1038/s41746-024-01065-0
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Small SR, Chan S, Walmsley R, VON Fritsch L, Acquah A, Mertes G, Feakins BG, Creagh A, Strange A, Matthews CE, Clifton DA, Price AJ, Khalid S, Bennett D, Doherty A. Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank. Med Sci Sports Exerc. 2024 Oct 1;56(10):1945-1953. doi: 10.1249/MSS.0000000000003478. Epub 2024 May 15. PMID: 38768076; PMCID: PMC11402590. https://doi.org/10.1249/MSS.0000000000003478
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Chen, Y., Chan, S., Bennett, D. et al. Device-measured movement behaviours in over 20,000 China Kadoorie Biobank participants. Int J Behav Nutr Phys Act 20, 138 (2023). https://doi.org/10.1186/s12966-023-01537-8
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Yuan, H., Chan, S., Creagh, A.P. et al. Self-supervised learning for human activity recognition using 700,000 person-days of wearable data. npj Digit. Med. 7, 91 (2024). https://doi.org/10.1038/s41746-024-01062-3
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Creagh, A.P., Hamy, V., Yuan, H. et al. Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis. npj Digit. Med. 7, 33 (2024). https://doi.org/10.1038/s41746-024-01013-y