High-Fidelity and Location-Robust Respiratory Waveform Monitoring with Single-Antenna WiFi

TL;DR

RespirFi is a WiFi sensing system that accurately monitors respiratory waveforms and biomarkers in a contactless, location-robust manner, overcoming hardware and accuracy limitations of prior methods.

Contribution

The paper introduces RespirFi, a novel WiFi-based respiratory monitoring system with a theoretical reflection model and adaptive waveform construction for high fidelity and robustness.

Findings

Outperforms existing methods in clinical respiratory metrics

Achieves high-fidelity waveform recovery across user locations

Demonstrates robustness with commodity WiFi devices

Abstract

In recent years, WiFi sensing has been recognized as a promising technology to bring respiratory monitoring into everyday homes, thanks to its contactless nature and ubiquitous availability. However, existing WiFi-based respiratory monitoring systems still fall short of deployment-oriented performance: they suffer from restrained hardware scalability, limited accuracy, and are highly sensitive to user location. To overcome these limitations and push WiFi sensing towards clinically meaningful precision, we propose RespirFi, a novel system that robustly delivers high-fidelity respiratory waveforms with WiFi Channel State Information (CSI), thereby enabling accurate estimation of key physiological biomarkers. At the core of RespirFi is a theoretical human reflection model, through which we perform an in-depth characterization of how CSI variations are shaped by both subcarrier frequency and spatial user location. Guided by these insights, we develop a location-robust waveform construction method that adaptively selects high quality subcarriers and aligns their waveform trends, ensuring accurate waveform recovery. Furthermore, we propose a breathing phase identification method that leverages inter-subcarrier CSI differences to reliably distinguish inhalation from exhalation. We implement RespirFi over commodity WiFi devices, and extensive experiments demonstrate that it outperforms state-of-the-art approaches across a wide range of clinically relevant respiratory metrics.