解决WiFi人体活动识别中的跨域偏差问题，实时适配未知环境。
DATTA: Domain-Adversarial Test-Time Adaptation for Cross-Domain WiFi-Based Human Activity Recognition
- 结合对抗训练与测试时自适应，动态调整模型应对域偏移。
- 在未见场景下提升F1分数8.1%，优于现有最优方法。
- 轻量高效，适合实时人体活动识别应用。
由于环境、设备和个体差异导致信道状态信息出现域偏移，基于WiFi的传感跨域泛化仍是开放难题。为此，我们提出领域对抗测试时自适应（DATTA）框架，融合领域对抗训练（DAT）、测试时自适应（TTA）与权重重置机制，实现对未见目标域的适应并防止灾难性遗忘。DATTA集成于一个轻量灵活的架构中，专为速度优化。我们在公开数据集上进行全面评估，包括关键组件的消融实验，并验证其适用于实时应用如人体活动识别。相较于结合SotA视频类TTA与WiFi-DAT的方法，我们的方法在F1分数上提升8.1%。DATTA的PyTorch实现已开源：https://github.com/StrohmayerJ/DATTA。
原文摘要 · Abstract (English)
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.
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