Cross-domain generalization is an open problem in WiFi-based sensing due to variations in environments, devices, and subjects, causing domain shifts in channel state information. To address this, we propose Domain-Adversarial Test-Time Adaptation (DATTA), a novel framework combining domain-adversarial training (DAT), test-time adaptation (TTA), and weight resetting to facilitate adaptation to unseen target domains and to prevent catastrophic forgetting. DATTA is integrated into a lightweight, flexible architecture optimized for speed. We conduct a comprehensive evaluation of DATTA, including an ablation study on all key components using publicly available data, and verify its suitability for real-time applications such as human activity recognition. When combining a SotA video-based variant of TTA with WiFi-based DAT and comparing it to DATTA, our method achieves an 8.1% higher F1-Score. The PyTorch implementation of DATTA is publicly available at: https://github.com/StrohmayerJ/DATTA.

翻译：基于WiFi的传感技术因其在环境、设备和受试者方面的差异而导致信道状态信息发生域偏移，跨域泛化是该领域一个悬而未决的问题。为解决此问题，我们提出了一种新颖的框架——域对抗测试时适应（DATTA），它结合了域对抗训练（DAT）、测试时适应（TTA）和权重重置，旨在促进对未见目标域的适应并防止灾难性遗忘。DATTA被集成到一个为速度优化的轻量级、灵活架构中。我们对DATTA进行了全面评估，包括使用公开数据对所有关键组件进行消融研究，并验证了其适用于人体活动识别等实时应用。当将一种基于视频的SotA TTA变体与基于WiFi的DAT结合，并与DATTA进行比较时，我们的方法实现了高出8.1%的F1分数。DATTA的PyTorch实现已在以下网址公开：https://github.com/StrohmayerJ/DATTA。