Coverage-Aware Virtual IMU Augmentation for Low-Resource Human Activity Recognition
IMU-based human activity recognition (HAR) enables continuous, privacy-friendly monitoring of daily activities using wearable sensors. However, building reliable HAR models that generalize across diverse users and real-world conditions requires large amounts of labeled IMU data, which are expensive and difficult to collect. Existing approaches mainly rely on augmentation or synthesis to expand available data, but indiscriminately adding virtual samples may provide little new coverage and introduce unreliable supervision. To overcome these challenges, we propose a novel coverage-aware virtual IMU augmentation framework that decides where to supplement real data, how to generate and select ...
| Authors | Jiayuan Gao, Yingwei Zhang, Ziyao Tang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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