用视觉辅助自动修复WiFi感知的环境漂移，让边缘设备持续精准识别人体动作。
maxVSTAR: Maximally Adaptive Vision-Guided CSI Sensing with Closed-Loop Edge Model Adaptation for Robust Human Activity Recognition
- 引入视觉模型实时生成动作标签，驱动边缘WiFi感知模型在线自适应调整。
- 在未校准硬件下，识别准确率从49.14%提升至81.51%，接近原始93.52%水平。
- 适合部署在隐私敏感、长期运行的智能边缘场景，如居家养老、无人值守监控。
基于WiFi信道状态信息（CSI）的人体活动识别（HAR）为智能环境提供了一种无设备、保护隐私的传感方案。然而，其在边缘设备上的部署严重受限于域偏移问题——环境与硬件条件变化导致识别性能下降。本文提出maxVSTAR（最大自适应视觉引导感知技术），一种闭环、视觉引导的模型自适应框架，可自主缓解边缘部署的CSI感知系统中的域偏移。系统采用跨模态师生架构，以高精度YOLO视觉模型作为动态监督信号，实时为CSI数据流提供动作标签。这些标签使轻量级的CSI-HAR模型STAR实现边缘端自主在线微调，形成闭环重训练机制，无需人工干预即可持续适应环境变化。大量实验表明，maxVSTAR效果显著：在未校准硬件上，基准模型准确率由93.52%降至49.14%；经一次视觉引导适应后，准确率恢复至81.51%。结果证实该系统在隐私敏感的物联网环境中具备动态自监督模型适应能力，建立了可扩展、实用的边缘长期自主HAR新范式。
原文摘要 · Abstract (English)
WiFi Channel State Information (CSI)-based human activity recognition (HAR) provides a privacy-preserving, device-free sensing solution for smart environments. However, its deployment on edge devices is severely constrained by domain shift, where recognition performance deteriorates under varying environmental and hardware conditions. This study presents maxVSTAR (maximally adaptive Vision-guided Sensing Technology for Activity Recognition), a closed-loop, vision-guided model adaptation framework that autonomously mitigates domain shift for edge-deployed CSI sensing systems. The proposed system integrates a cross-modal teacher-student architecture, where a high-accuracy YOLO-based vision model serves as a dynamic supervisory signal, delivering real-time activity labels for the CSI data stream. These labels enable autonomous, online fine-tuning of a lightweight CSI-based HAR model, termed Sensing Technology for Activity Recognition (STAR), directly at the edge. This closed-loop retraining mechanism allows STAR to continuously adapt to environmental changes without manual intervention. Extensive experiments demonstrate the effectiveness of maxVSTAR. When deployed on uncalibrated hardware, the baseline STAR model's recognition accuracy declined from 93.52% to 49.14%. Following a single vision-guided adaptation cycle, maxVSTAR restored the accuracy to 81.51%. These results confirm the system's capacity for dynamic, self-supervised model adaptation in privacy-conscious IoT environments, establishing a scalable and practical paradigm for long-term autonomous HAR using CSI sensing at the network edge.
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