Fine-Grained Contactless Human Respiratory Measurement Using Millimeter-Wave Radar

Respiration is a critical vital sign, and its continuous, fine-grained monitoring provides essential insights for clinical assessment and daily health management. Millimeter-wave radar offers a promising contactless and privacy-preserving solution for respiratory monitoring. However, accurately reconstructing respiratory waveforms from radar echoes remains challenging due to the distributed scattering of the human thorax resulting in the respiratory signals across multiple range bins, especially in a high range-resolution radar system, as well as interferences from the body micro-motions such as random body movements (RBMs). In this paper, we propose mmBreath, a fine-grained respiration monitoring system based on a 60 GHz frequency-modulated continuous wave (FMCW) radar. mmBreath adaptively integrates phase signals from multiple range bins to capture comprehensive respiratory dynamics, and employs an attention-enhanced variational autoencoder (VAE) to reconstruct high-fidelity respiratory waveforms. Furthermore, we introduce a respiratory spectrogram reconstruction task as an auxiliary objective to incorporate time-frequency features, which effectively suppresses interference from RBMs, thus further improving reconstruction accuracy. Extensive experiments on 17 hours of data from 12 human subjects demonstrate that mmBreath achieves superior accuracy and robustness, highlighting its potential for practical, contactless respiratory monitoring in real-world scenarios.