  Crucial for healthcare and biomedical applications, respiration monitoring
often employs wearable sensors in practice, causing inconvenience due to their
direct contact with human bodies. Therefore, researchers have been constantly
searching for contact-free alternatives. Nonetheless, existing contact-free
designs mostly require human subjects to remain static, largely confining their
adoptions in everyday environments where body movements are inevitable.
Fortunately, radio-frequency (RF) enabled contact-free sensing, though
suffering motion interference inseparable by conventional filtering, may offer
a potential to distill respiratory waveform with the help of deep learning. To
realize this potential, we introduce MoRe-Fi to conduct fine-grained
respiration monitoring under body movements. MoRe-Fi leverages an IR-UWB radar
to achieve contact-free sensing, and it fully exploits the complex radar signal
for data augmentation. The core of MoRe-Fi is a novel variational
encoder-decoder network; it aims to single out the respiratory waveforms that
are modulated by body movements in a non-linear manner. Our experiments with 12
subjects and 66-hour data demonstrate that MoRe-Fi accurately recovers
respiratory waveform despite the interference caused by body movements. We also
discuss potential applications of MoRe-Fi for pulmonary disease diagnoses.
