Abstract
It can be life-saving to monitor the respiratory rate (RR) even for healthy people in real-time. It is reported that the infected people with coronavirus disease 2019 (COVID-19), generally develop mild respiratory symptoms in the early stage. It will be more important to continuously monitor the RR of people in nursing homes and houses with a non-contact method. Conventional, contact-based, methods are not suitable for long-term health monitoring especially in-home care services. The potentials of wireless radio signals for health care applications, such as fall detection, etc., are examined in literature. In this paper, we focus on a device-free real-time RR monitoring system using wireless signals. In our recent study, we proposed a non-contact RR monitoring system with a batch processing (delayed) estimation method. In this paper, for real-time monitoring, we modify the standard joint unscented Kalman filter (JUKF) method for this new and time-critical problem. Due to the nonlinear structure of the RR estimation problem with respect to the measurements, a novel modification is proposed to transform measurement errors into parameter errors by using the hyperbolic tangent function. It is shown in the experiments conducted with the real measurements taken using healthy volunteers that the proposed modified joint unscented Kalman filter (ModJUKF) method achieves the highest accuracy according to the windowing-based methods in the time-varying RR scenario. It is also shown that the ModJUKF not only reduces the computational complexity approximately 8.54% but also improves the accuracy 36.7% with respect to the standard JUKF method.
Highlights
Monitoring systems are becoming widespread and continuously monitoring of respiratory rate (RR), especially for those living alone and suffering from respiratory diseases is vital [1]
We showed in our recent studies that the subspace techniques, estimation of signal parameters by rotational invariance technique (ESPRIT) and multiple signal classification (MUSIC), for the RR estimation with a reduced latency and improved accuracy according to the periodogram methods [21], [22], a real-time RR tracking is still an important requirement
Nowadays, the potentials of ambient wireless radio signals are investigated for fall detection, elderly health monitoring etc. for home-care applications
Summary
Monitoring systems are becoming widespread and continuously monitoring of respiratory rate (RR), especially for those living alone and suffering from respiratory diseases is vital [1]. These methods can be divided into two groups depending on whether they use the received signal strength (RSS) [10], [11] or the fine-grained channel state information (CSI) parameters [12]–[15] of the ambient WiFi signals for monitoring In addition to these wireless sensing systems, other non-contact methods which are using different technologies such as audio [16] and visual [17] are investigated in literature. The nonlinear model-based KF approach which is capable of estimating and tracking the RR in real-time with a low steady-state error is proposed. The proposed new real-time non-contact RR estimation and tracking ModJUKF method reduces the computational complexity and improves the accuracy according to the standard JUKF method. ModJUKF achieves the highest accuracy among the windowing-based common methods in the time-varying RR scenario
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