WiRM: Wireless Respiration Monitoring Using Conjugate Multiple Channel State Information and Fast Iterative Filtering in Wi-Fi Systems

TL;DR

WiRM introduces a novel two-stage Wi-Fi-based respiration monitoring system that significantly improves accuracy in estimating respiratory rate and waveform, while also demonstrating robustness in noisy environments.

Contribution

The paper presents WiRM, a new contactless respiration monitoring method using conjugate multiple channel state information and adaptive filtering, outperforming existing techniques in accuracy and noise resilience.

Findings

38% reduction in respiratory rate RMSE compared to state-of-the-art methods

178.3% improvement in correlation with ground truth respiratory waveform

Demonstrates robustness under various noise conditions

Abstract

Monitoring respiratory health with the use of channel state information (CSI) has shown promising results. Many existing methods focus on monitoring only the respiratory rate, while others focus on monitoring the motion of the chest as a patient breathes, which is referred to as the respiratory waveform. This paper presents WiRM, a two-staged approach to contactless respiration monitoring. In the first stage, WiRM improves upon existing respiratory rate estimation techniques by using conjugate multiplication for phase sanitisation and adaptive multi-trace carving (AMTC) for tracing how the respiratory rate changes over time. When compared against three state-of-the-art methods, WiRM has achieved an average reduction of $38\%$ in respiratory rate root mean squared error (RMSE). In the second stage, WiRM uses this improved respiratory rate estimate to inform the decomposition and selection of the respiratory waveform from the CSI data. Remarkably, WiRM delivers a $178.3\%$ improvement in average absolute correlation with the ground truth respiratory waveform. Within the literature, it is difficult to compare the robustness of existing algorithms in noisy environments. In this paper, we develop a purpose-built simulation toolkit to evaluate the robustness of respiration monitoring solutions under various noise conditions, including thermal, multiplicative, and phase noise. Our results show that WiRM demonstrates improved or comparable resilience to these common noise sources.