Scene-Adaptive Continual Learning for CSI-based Human Activity Recognition with Mixture of Experts

• URL: http://arxiv.org/abs/2605.06447v1
• Date: Thu, 07 May 2026 15:45:24 GMT
• Title: Scene-Adaptive Continual Learning for CSI-based Human Activity Recognition with Mixture of Experts
• Abstract summary: We propose Scene-Adaptive Mixture of Experts with Clustered Specialists (SAMoE-C)<n>SAMoE-C is a mixture-of-experts system that enables scene-specific adaptation via an attention-based semantic router.<n>We show that SAMoE-C approaches the state-of-the-art accuracy, while maintaining a significantly lower inference cost.
• Score: 11.152550233971573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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