| Title: | Self-supervised learning for human activity recognition using 700,000 person-days of wearable data |
| Journal: | npj Digital Medicine |
| Published: | 12 Apr 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38609437/ |
| DOI: | https://doi.org/10.1038/s41746-024-01062-3 |
| URL: | https://www.nature.com/articles/s41746-024-01062-3.pdf |
Publication 9912
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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.</p>
7 Authors
- Hang Yuan
- Shing Chan
- Andrew P. Creagh
- Catherine Tong
- Aidan Acquah
- David A. Clifton
- Aiden Doherty
1 Application
| Application ID | Title |
|---|---|
| 59070 | Statistical machine learning of wearable sensor data to predict disease outcomes |
Enabling scientific discoveries that improve human health