Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866911925268381696 |
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| author | Yuan, Hang Chan, Shing Creagh, Andrew P. Tong, Catherine Acquah, Aidan Clifton, David A. Doherty, Aiden |
| author_facet | Yuan, Hang Chan, Shing Creagh, Andrew P. Tong, Catherine Acquah, Aidan Clifton, David A. Doherty, Aiden |
| contents | 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 2.5%-100% (median 18.4%), the largest improvements occurring in the smaller datasets. In contrast to previous studies, our results generalise across external datasets, devices, and environments. Our open-source model will help researchers and developers to build customisable and generalisable activity classifiers with high performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_02909 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data Yuan, Hang Chan, Shing Creagh, Andrew P. Tong, Catherine Acquah, Aidan Clifton, David A. Doherty, Aiden Signal Processing Artificial Intelligence Machine Learning 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 2.5%-100% (median 18.4%), the largest improvements occurring in the smaller datasets. In contrast to previous studies, our results generalise across external datasets, devices, and environments. Our open-source model will help researchers and developers to build customisable and generalisable activity classifiers with high performance. |
| title | Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2206.02909 |