Abstract:Channel state information (CSI)-based human activity recognition (HAR) is vulnerable to performance degradation under domain shifts across varying physical environments. Continual learning (CL) offers a principled way to learn new domains sequentially while preserving past knowledge, but existing CL solutions for CSI-based HAR scale poorly with accumulating domains, rely on a large replay buffer, or incur linearly growing inference cost. In this letter, we propose Scene-Adaptive Mixture of Experts with Clustered Specialists (SAMoE-C), which formulates cross-domain CSI-based HAR as a mixture-of-experts system that enables scene-specific adaptation, via an attention-based semantic router that activates only selected experts for each input. Moreover, we develop a novel training protocol, which requires only a tiny replay buffer for stabilizing domain discrimination of the router. Experimental results on a four-scene CSI dataset demonstrate that SAMoE-C approaches the state-of-the-art accuracy, while maintaining a significantly lower inference cost. By jointly combining modular experts, selective activation with router and a lightweight training protocol, SAMoE-C enables scalable cross-domain CSI-based HAR deployment with low training overhead and high computational efficiency in real-world settings.
| Comments: | 5 pages, 3 figures, 3 tables, this article was submitted to IEEE for possible publication |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.06447 [cs.LG] |
| (or arXiv:2605.06447v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.06447 arXiv-issued DOI via DataCite (pending registration) |
From: Wenhan Zheng [view email]
[v1]
Thu, 7 May 2026 15:45:24 UTC (1,134 KB)
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