Self-supervised learning for human activity recognition using 700,000 person-days of wearable data
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 a...
| Huvudupphovsmän: | , , , , , , |
|---|---|
| Materialtyp: | Journal article |
| Språk: | English |
| Publicerad: |
Springer Nature
2024
|