MoVi-Fi: motion-robust vital signs waveform recovery via deep interpreted RF sensing

Abstract

Vital signs are crucial indicators for human health, and researchers are studying contact-free alternatives to existing wearable vital signs sensors. Unfortunately, most of these designs demand a subject human body to be relatively static, rendering them very inconvenient to adopt in practice where body movements occur frequently. In particular, radio-frequency (RF) based contact-free sensing can be severely affected by body movements that overwhelm vital signs. To this end, we introduce MoVi-Fi as a motion-robust vital signs monitoring system, capable of recovering fine-grained vital signs waveform in a contact-free manner. Being a pure software system, MoVi-Fi can be built on top of virtually any commercial-grade radars. What inspires our design is that RF reflections caused by vital signs, albeit weak, do not totally disappear but are composited with other motion-incurred reflections in a nonlinear manner. As nonlinear blind source separation is inherently hard, MoVi-Fi innovatively employs deep contrastive learning to tackle the problem; this self-supervised method requires no ground truth in training, and it exploits contrastive signal features to distinguish vital signs from body movements. Our experiments with 12 subjects and 80hour data demonstrate that MoVi-Fi accurately recovers vital signs waveform under severe body movements.