Modular Retrieval-Augmented Generalization for Human Action Recognition
Inertial Measurement Unit (IMU)-based Human Activity Recognition (HAR) aims to interpret and classify user behaviors from temporal motion signals. Recently, deep learning frameworks have advanced this task by learning and extracting discriminative spatiotemporal representations, significantly improving recognition performance. However, IMU-based HAR still faces several critical challenges, particularly limited training samples and static knowledge utilization, both of which severely hinder its large-scale deployment. In this paper, we introduce MoRA, the first Retrieval-Augmented Module specifically designed for motion series. It can be flexibly integrated into any existing HAR model, enh...
| Authors | Peng Liao, Shangsong Liang, Lin Chen et al. |
|---|---|
| Primary category | eess.SP |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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