Journal article
Self-supervised learning for human activity recognition using 700,000 person-days of wearable data
- Abstract:
- Accurate physical activity monitoring is essential to understand the impact of physical activity on one's physical health and overall well-being. However, advances in human activity recognition algorithms have been constrained by the limited availability of large labelled datasets. This study aims to leverage recent advances in self-supervised learning to exploit the large-scale UK Biobank accelerometer dataset-a 700,000 person-days unlabelled dataset-in order to build models with vastly improved generalisability and accuracy. Our resulting models consistently outperform strong baselines across eight benchmark datasets, with an F1 relative improvement of 2.5-130.9% (median 24.4%). More importantly, in contrast to previous reports, our results generalise across external datasets, cohorts, living environments, and sensor devices. Our open-sourced pre-trained models will be valuable in domains with limited labelled data or where good sampling coverage (across devices, populations, and activities) is hard to achieve.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.5MB, Terms of use)
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- Publisher copy:
- 10.1038/s41746-024-01062-3
Authors
- Publisher:
- Springer Nature
- Journal:
- npj Digital Medicine More from this journal
- Volume:
- 7
- Issue:
- 1
- Article number:
- 91
- Place of publication:
- England
- Publication date:
- 2024-04-12
- Acceptance date:
- 2024-02-22
- DOI:
- EISSN:
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2398-6352
- ISSN:
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2398-6352
- Pmid:
-
38609437
- Language:
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English
- Keywords:
- Pubs id:
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1989639
- Local pid:
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pubs:1989639
- Deposit date:
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2024-05-02
Terms of use
- Copyright holder:
- Yuah et al.
- Copyright date:
- 2024
- Rights statement:
- © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Notes:
- For the purpose of open access, the author has applied a CC-BY public copyright licence to any author-accepted manuscript version arising from this submission.
- Licence:
- CC Attribution (CC BY)
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