AI RESEARCH
Temporal Structure Matters for Efficient Test-Time Adaptation in Wearable Human Activity Recognition
arXiv CS.LG
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ArXi:2605.04617v1 Announce Type: cross Wearable human activity recognition (WHAR) models often suffer from performance degradation under real-world cross-user distribution shifts. Test-time adaptation (TTA) mitigates this degradation by adapting models online using unlabeled test streams, yet existing methods largely inherit assumptions from vision tasks and underexploit the inherent inter-window temporal structure in WHAR streams. In this paper, we revisit such temporal structure as a feature-conditioned inference signal rather than merely an output-space smoothing prior.