Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition

• URL: http://arxiv.org/abs/2409.16730v1
• Date: Wed, 25 Sep 2024 08:28:54 GMT
• Title: Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition
• Authors: Ning Sun, Yufei Wang, Yuwei Zhang, Jixiang Wan, Shenyue Wang, Ping Liu, Xudong Zhang,
• Abstract summary: Human Activity Recognition (HAR) has gained great attention from researchers due to the popularity of mobile devices. In this work, we collect a human activity recognition dataset called OPPOHAR consisting of phone IMU data. We propose a novel light-weight network called Non-stationary BERT with a two-stage training method to achieve user-specific activity recognition.
• Score: 12.175145985669642
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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