Feature-Augmentation Based Cross Domain Human Activity Recognition Using Wi-Fi Signals

Human activity recognition based on Wi-Fi signals has promising applications in smart homes, health monitoring, and human-computer interaction. However, existing methods typically treat channel state information (CSI) as a time sequence, overlooking variations across channel dimensions among different subcarriers. Moreover, their recognition performance significantly degrades under distribution shifts between source and target domains. To address these challenges, this paper proposes a cross-domain activity recognition framework based on a paired attention mechanism and adversarial domain adaptation. The framework comprises three core modules: a timefrequency domain feature encoder, a label prediction module, and a domain classification module. The encoder adaptively enhances discriminative features along both the temporal and channel dimensions of CSI data via temporal and channel attention mechanisms. The label prediction module performs activity classification in the aligned feature space. Meanwhile, the domain classification module forms an adversarial relationship with the encoder through a gradient reversal layer, encouraging the encoder to learn domain-invariant representations. This joint optimization strategy effectively balances discriminative power and domain invariance, addressing the limitations of prior methods that overemphasize domain alignment at the expense of classification accuracy. Experimental results demonstrate that the proposed method achieves recognition accuracies of $\mathbf{9 2. 0 0 \%}$ and $\mathbf{9 2. 7 9 \%}$ in two typical cross-domain scenarios, outperforming existing approaches.