WiTTA-Bench: Benchmarking Test-Time Adaptation for WiFi Sensing

WiFi sensing offers passive and privacy-preserving perception that complements vision-based sensing, but its performance degrades sharply under domain shifts caused by changes in environment, users, or hardware. This challenge is exacerbated in real-world deployments where source data are unavailable, motivating test-time adaptation (TTA) as a practical solution for self-calibration using only unlabeled target samples. We introduce WiTTA-Bench, the first comprehensive benchmark for WiFi TTA, covering 20 representative methods, two adaptation protocols (OTTA and TTDA), and three major physics-induced shifts in WiFi: cross-environment, cross-subject, and cross-device. Furthermore, we contribute a new dataset featuring paired recordings from heterogeneous devices to bridge the cross-device gap. Extensive experiments reveal three key insights unique to WiFi sensing: (i) WiFi domain shifts exhibit a physics-induced hierarchy: environmental changes alter multipath statistics, subject variation perturbs temporal–spectral geometry, and hardware differences reshape the entire feature manifold; (ii) OTTA and TTDA are complementary: lightweight OTTA handles mild statistical drift, while TTDA is necessary to correct deep, hardware-induced structural distortions; and (iii) OTTA is hyperparameter-robust and scales linearly with source quality, whereas TTDA is more sensitive due to recursive self-training. WiTTA-Bench establishes the first systematic foundation for adaptive, robust, and deployable WiFi sensing under realistic wireless conditions.