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The Newcastle University research output collection, currently available on ePrints, will shortly be moving to a new open repository platform, Figshare. To prepare for the data migration we have paused adding new content to ePrints, and will resume once the new repository is launched. During this time you will continue to have access to ePrints (but no new content will appear). We will share updates here when available.

Using unsupervised machine learning to quantify physical activity from accelerometry in a diverse and rapidly changing population

Lookup NU author(s): Dr Christopher ThorntonORCiD, Professor Niina KolehmainenORCiD, Professor Kianoush Nazarpour

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Licence

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Publication metadata

Author(s): Thornton C, Kolehmainen N, Nazarpour K

Publication type: Article

Publication status: Published

Journal: PLOS Digital Health

Year: 2023

Volume: 2

Issue: 4

Online publication date: 05/04/2023

Acceptance date: 23/02/2023

Date deposited: 20/04/2023

ISSN (electronic): 2767-3170

Publisher: Public Library of Science

URL: https://doi.org/10.1371/journal.pdig.0000220

DOI: 10.1371/journal.pdig.0000220

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Funding

Funder referenceFunder name
EP/R004242/2
ICA-SCL-2015-01-003National Institute for Health Research (NIHR)
ICA-SCL-2015-01-003National Institute for Health Research (NIHR)

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