Device-free crowd-counting using WiFi Channel State Information (CSI) is a key enabling technology for a new generation of privacy-preserving Internet of Things (IoT) applications. However, practical deployment is severely hampered by the domain shift problem, where models trained in one environment fail to generalise to another. To overcome this, we propose a novel two-stage framework centred on a CSI-ResNet-A architecture. This model is pre-trained via self-supervised contrastive learning to learn domain-invariant representations and leverages lightweight Adapter modules for highly efficient fine-tuning. The resulting event sequence is then processed by a stateful counting machine to produce a final, stable occupancy estimate. We validate our framework extensively. On our WiFlow dataset, our unsupervised approach excels in a 10-shot learning scenario, achieving a final Mean Absolute Error (MAE) of just 0.44--a task where supervised baselines fail. To formally quantify robustness, we introduce the Generalisation Index (GI), on which our model scores near-perfectly, confirming its ability to generalise. Furthermore, our framework sets a new state-of-the-art public WiAR benchmark with 98.8\% accuracy. Our ablation studies reveal the core strength of our design: adapter-based fine-tuning achieves performance within 1\% of a full fine-tune (98.84\% vs. 99.67\%) while training 97.2\% fewer parameters. Our work provides a practical and scalable solution for developing robust sensing systems ready for real-world IoT deployments.

翻译：利用WiFi信道状态信息（CSI）进行无设备人群计数是新一代隐私保护物联网（IoT）应用的关键使能技术。然而，实际部署受到域偏移问题的严重阻碍，即在某一环境中训练的模型无法泛化至其他环境。为解决此问题，我们提出了一种以CSI-ResNet-A架构为核心的新型两阶段框架。该模型通过自监督对比学习进行预训练，以学习域不变表征，并利用轻量级适配器模块实现高效微调。随后，生成的事件序列由状态计数机处理，以产生最终稳定的占用估计。我们对该框架进行了广泛验证。在我们的WiFlow数据集上，我们的无监督方法在10样本学习场景中表现优异，实现了仅0.44的最终平均绝对误差（MAE）——而监督基线方法在此任务中完全失效。为形式化量化鲁棒性，我们引入了泛化指数（GI），我们的模型在该指标上接近满分，证实了其泛化能力。此外，我们的框架在公开WiAR基准测试中取得了98.8%准确率的新最优性能。消融研究揭示了我们设计的核心优势：基于适配器的微调性能达到全参数微调的99%以内（98.84% vs. 99.67%），同时训练参数减少97.2%。我们的工作为开发适用于真实世界物联网部署的鲁棒感知系统提供了实用且可扩展的解决方案。