MovBeat: A Contrastive Learning Based WiFi CSI Sensing for Respiration Monitoring in Mobil...

Vital sign monitoring plays a key role in modern healthcare, supporting applications ranging from chronic disease management to more advanced elderly care. While traditional systems rely on contact-based devices, recent advances in WiFi sensing allow contactless monitoring using channel state information (CSI), offering a more convenient and unobtrusive approach. However, most existing WiFi-based methods mainly concentrate on monitoring in static conditions, rendering them unsuitable for real-world scenarios such as measuring respiration rate during walking. To address this gap, in this paper we propose MovBeat, a respiration prediction system designed to deliver high-accuracy respiratory monitoring when the subject is in motion. By integrating a contrastive learning framework with attention-based feature extraction, MovBeat effectively mitigates interference from the environment and body movements. Experimental results demonstrate that MovBeat achieves over 90% accuracy in monitoring respiration on the move - an improvment of approximately 20% compared to traditional methods. Comprehensive evaluations in diverse movement states, including both line-of-sight (LoS) and non-line-of-sight (NLoS) environments, demonstrate the robustness and generalizability of MovBeat in real-world scenarios.

MovBeat: A Contrastive Learning Based WiFi CSI Sensing for Respiration Monitoring in Mobility Scenarios

Yifan Feng, University of Sydney; Peng Cheng, La Trobe University; Shenghong Li, Data 61, CSIRO, Australia; Hongze Liu, Geng Wang, University of Sydney; Branka Vucetic, The University of Sydney; Yonghui Li, University of Sydney