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, l

SUBMITTER: Yuan H 

PROVIDER: S-EPMC11015005 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature