Rethinking Spatial Alignment in UDA for Wearable Multivariate Time Series: A Diagnostic Analysis and a Simple Remedy

Wearable sensing models often fail when deployed on new users, devices, or sensor placements, because the meaning of each sensor channel is inconsistent across these settings. Unsupervised domain adaptation (UDA) for multivariate time series (MTS) aims to close this gap, yet existing methods mainly address temporal distribution shift or align domains using spatial structure learned within each domain separately. Through a diagnostic analysis, we identify a distinct failure mode in wearable MTS, which we term sensor-wise domain shift. Current alignment strategies do not model this cross-domain channel mismatch explicitly and therefore struggle to correct it. We propose (Inter-Domain Sensor Alignment), a backbone-agnostic plug-in module that aligns sensors explicitly across domains. combines (i) inter-domain sensor transport, which learns a source–target sensor correspondence matrix from domain-specific sensor embeddings and maps target channels into the source sensor space, and (ii) channel decorrelation, a regularizer that suppresses redundant or noisy channel couplings and prevents representation collapse. The resulting spatial transportation loss can be interpreted as a sensor-level discrete optimal-transport objective. improves average activity-classification performance over recent baselines on HAR and sEMG benchmarks across most evaluated scenarios.