GAWNet: A Gated Attention Wavelet Network for Respiratory Monitoring via Millimeter-Wave Radar

Abstract

Millimeter-wave radar has attracted increasing attention for respiratory monitoring due to its non-contact operation and privacy-preserving characteristics. Nevertheless, extracting fine-grained respiratory waveforms from non-stationary radar signals remains highly challenging, as these signals are frequently contaminated by various interferences, most notably aperiodic body micromotion. The spectral components of such interference often overlap with the respiratory frequency band and typically exhibit power levels that significantly exceed the target signal. This letter introduces the Gated Attention Wavelet Network (GAWNet), an interpretable framework that integrates deep learning with physical priors by operating on radar phase information in the wavelet domain. GAWNet leverages a two-stage suppression strategy: first, a Temporal Gated Attention (TGA) encoder combines convolutional gating and self-attention to achieve initial interference reduction; second, a Frequency Gated Attention (FGA) decoder provides further refinement by transforming wavelet coefficients to the frequency domain for precise filtering. The clean respiratory waveform is then reconstructed using an Inverse Discrete Wavelet Transform (IDWT). Extensive experiments with data from 12 subjects demonstrate that GAWNet consistently outperforms state-of-the-art models and exhibits robust generalization capability.