Self-supervised Learning for Human Activity Recognition Using 700, 000 Person-days of Wearable Data

@article{Yuan2022SelfsupervisedLF,
  title={Self-supervised Learning for Human Activity Recognition Using 700, 000 Person-days of Wearable Data},
  author={Han Yuan and S. Chan and Andrew P. Creagh and C. Tong and David A. Clifton and Aiden Doherty},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.02909}
}
Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets. In this study, we leverage self-supervised learning techniques on the UK-Biobank activity tracker dataset–the largest of its kind to date–containing more than 700,000 person-days of unlabelled wearable sensor data. Our resulting activity recognition model consistently outperformed strong baselines across seven benchmark datasets, with an F1 relative improvement of 2… 

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