DATTA: Domain-Adversarial Test-Time Adaptation for Cross-Domain WiFi-Based Human Activity Recognition

Abstract

WiFi-based human activity recognition (HAR) faces significant challenges in cross-domain generalization due to dynamic environmental variations, device heterogeneity, and subtle changes in human behavior. In this paper, we introduce DATTA - Domain-Adversarial Test-Time Adaptation - a novel framework that combines domain-adversarial training (DAT) with test-time adaptation (TTA) and a random weight-resetting mechanism. Unlike previous approaches that apply these techniques in isolation, DATTA is specifically tailored for WiFi-based HAR: it leverages DAT to learn robust, domain-invariant features while TTA continuously refines the model on streaming data. To mitigate catastrophic forgetting during adaptation, we incorporate a weight-resetting mechanism, ensuring sustained performance over prolonged domain shifts. Our extensive experiments on the Widar3.0-G6D dataset demonstrate that DATTA not only outperforms state-of-the-art methods by up to 8.1% in F1-Score but also achieves real-time inference with a lightweight architecture, making it a compelling solution for practical WiFi sensing applications. The PyTorch implementation of DATTA is publicly available at: https://github.com/StrohmayerJ/DATTA.