WiResP: A Robust Wi-Fi-Based Respiration Monitoring via Spectrum Enhancement

Respiration monitoring has been attracting substantial attention because of its potential for assessing sleep stages and quality. Traditional approaches for respiratory rate (RR) tracking require dedicated wearable devices, which can intrude upon and create an unwelcoming experience for users. To address this, researchers have proposed Wi-Fi-based respiration monitoring systems that capitalize on Wi-Fi's ubiquity, cost-effectiveness, and noncontact nature, effectively converting existing infrastructure into ubiquitous sensors. However, existing systems often struggle with the subtle signal-to-noise ratio of embedded breathing signals, leading to limited coverage and inflexible deployment in noisy environments. This article presents Wi-Fi respiration perception (WiResP), an ingenious and pragmatic Wi-Fi-based system for tracking respiration, employing the spectrum enhancement approach to boost respiration detection. By treating the spectrum of the breathing signal obtained from channel state information as an image, the system can leverage image processing techniques to greatly enhance the respiration signal trace, improving detectability and increasing sensing coverage for RR estimation. Moreover, an image-based continuity checker module is proposed to verify the signal trace continuity to reduce false alarms (FAs). We conducted extensive experiments and assessed WiResP under different settings. The experiments demonstrate that WiResP can reliably capture RR during sleep and achieve a detection rate of 92% and an FA rate <5%, leading to significantly improved sleep stage recognition under flexible device placements. The promising performance positions WiResP as a candidate for a real-world in-home respiration tracking system.