SynHAR: Augmenting Human Activity Recognition With Synthetic Inertial Sensor Data Generated From Human Surface Models
We investigate combined, personalised biomechanical dynamics models and human surface models to synthesise IMU sensor time series data and to improve Human Activity Recognition (HAR) model performance for activities of daily living (ADLs). We analyse two model training scenarios: (1) data fusion of...
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| Автори: | , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
IEEE
2024-01-01
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| Серія: | IEEE Access |
| Предмети: | |
| Онлайн доступ: | https://ieeexplore.ieee.org/document/10786206/ |
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