Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition
<|reference_start|>Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity 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.<|reference_end|>
@article{sun2024non-stationary,
    title={Non-stationary BERT: Exploring Augmented IMU Data For Robust Human
  Activity Recognition},
    author={Ning Sun, Yufei Wang, Yuwei Zhang, Jixiang Wan, Shenyue Wang, Ping
  Liu, Xudong Zhang},
    journal={arXiv preprint arXiv:2409.16730},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16730},
    primaryClass={cs.AI cs.CV}
}