“… FIGURE 3. The real measurements are collected using the experimental setups in laboratory [22] . FIGURE 4.…”
Section: Respiratory Rate Estimation Using Modjukfmentioning
confidence: 99%
“…It is shown that in a steady indoor environment, the averaged RR rate is successfully estimated using RF signals. These methods can be grouped as FFT-based methods (such as periodogram) [10] , [13] – [15] , [18] , [19] , and high resolution subspace techniques [12] , [20] – [22] . In the periodogram method, the frequency resolution and accuracy completely depend on the duration of the collected data window.…”
Section: Introductionmentioning
confidence: 99%
“…The RR is a time-varying parameter, assuming it is constant over a long period of time, increases the estimation latency and this can be a critical problem in emergencies. Although 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. In [23] , Kalman Filtering (KF) approach for RR estimation is investigated with a limited accuracy.…”
Section: Introductionmentioning
confidence: 99%
“…The real measurements are collected using the experimental setups in laboratory[22].VOLUME 4, 2016 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.…”
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 timevarying 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. INDEX TERMS Unscented Kalman filter, joint unscented Kalman filter, respiratory rate tracking, devicefree, vital signs, health monitoring, radio signals.
“… FIGURE 3. The real measurements are collected using the experimental setups in laboratory [22] . FIGURE 4.…”
Section: Respiratory Rate Estimation Using Modjukfmentioning
confidence: 99%
“…It is shown that in a steady indoor environment, the averaged RR rate is successfully estimated using RF signals. These methods can be grouped as FFT-based methods (such as periodogram) [10] , [13] – [15] , [18] , [19] , and high resolution subspace techniques [12] , [20] – [22] . In the periodogram method, the frequency resolution and accuracy completely depend on the duration of the collected data window.…”
Section: Introductionmentioning
confidence: 99%
“…The RR is a time-varying parameter, assuming it is constant over a long period of time, increases the estimation latency and this can be a critical problem in emergencies. Although 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. In [23] , Kalman Filtering (KF) approach for RR estimation is investigated with a limited accuracy.…”
Section: Introductionmentioning
confidence: 99%
“…The real measurements are collected using the experimental setups in laboratory[22].VOLUME 4, 2016 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.…”
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 timevarying 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. INDEX TERMS Unscented Kalman filter, joint unscented Kalman filter, respiratory rate tracking, devicefree, vital signs, health monitoring, radio signals.
“…Fang et al [314] use ensemble empirical mode decomposition (EEMD) algorithm to extract the intrinsic mode functions of RR and HR when two targets' distance is less than the range resolution of FMCW radar. Uysal et al [315] use unmodulated carrier wave signal because it does not require time synchronization system, their system consists of a motion detector based on adaptive thresholding and a computationally-efficient, high resolution search-free spectrum estimation method (ESPRIT) to estimate the respiration rate even with limited number of data. The radar self-motion (RSM) may affect the vital sign detection and occurs when the radar platform cannot be maintained stationary such as in the automotive and industrial environments.…”
First and foremost, I would like to thank my supervisor, Assoc. Prof. Ng Wee Keong, for his kindness, trust, and patience throughout the years of my PhD study. The research guidance given by Prof. Ng has important influence to the success of our new discovery. The research environment in NTU SCSE provides us the research freedom to carry out meaningful explorations and experimentations with new inventions or innovations. The regular seminars held by the school and university inspires us with new ideas and expand our core research to stay up-todate and relevant. I would like to thank Assoc. Prof. Adams Kong Wai Kin, Assoc.
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