Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912045837844480 |
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| author | Sun, Ning Wang, Yufei Zhang, Yuwei Wan, Jixiang Wang, Shenyue Liu, Ping Zhang, Xudong |
| author_facet | Sun, Ning Wang, Yufei Zhang, Yuwei Wan, Jixiang Wang, Shenyue Liu, Ping Zhang, Xudong |
| contents | Human Activity Recognition (HAR) has gained great attention from researchers due to the popularity of mobile devices and the need to observe users' daily activity data for better human-computer interaction. In this work, we collect a human activity recognition dataset called OPPOHAR consisting of phone IMU data. To facilitate the employment of HAR system in mobile phone and to achieve user-specific activity recognition, we propose a novel light-weight network called Non-stationary BERT with a two-stage training method. We also propose a simple yet effective data augmentation method to explore the deeper relationship between the accelerator and gyroscope data from the IMU. The network achieves the state-of-the-art performance testing on various activity recognition datasets and the data augmentation method demonstrates its wide applicability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16730 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition Sun, Ning Wang, Yufei Zhang, Yuwei Wan, Jixiang Wang, Shenyue Liu, Ping Zhang, Xudong Artificial Intelligence Computer Vision and Pattern Recognition Human Activity Recognition (HAR) has gained great attention from researchers due to the popularity of mobile devices and the need to observe users' daily activity data for better human-computer interaction. In this work, we collect a human activity recognition dataset called OPPOHAR consisting of phone IMU data. To facilitate the employment of HAR system in mobile phone and to achieve user-specific activity recognition, we propose a novel light-weight network called Non-stationary BERT with a two-stage training method. We also propose a simple yet effective data augmentation method to explore the deeper relationship between the accelerator and gyroscope data from the IMU. The network achieves the state-of-the-art performance testing on various activity recognition datasets and the data augmentation method demonstrates its wide applicability. |
| title | Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.16730 |