Dual Unscented Kalman Filter and Its Applications to Respiratory System Modelling

Saatçı, Esra; Akan, Aydın

doi:10.5772/6807

Cited by 5 publications

(2 citation statements)

References 23 publications

(21 reference statements)

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“…More recent studies have used the UKF to assimilate in vivo or in vitro data in physiological models. In [4], patient data were used to estimate states and parameters in models of the human respiratory system. In [5], in vitro neuron membrane potential recordings were assimilated in a model of a small network of Hodgkin-Huxley-type neurons.…”

Section: Introductionmentioning

confidence: 99%

Optimization of an unscented Kalman filter for an embedded platform

Graybill

Gluckman

Kiani

2022

Computers in Biology and Medicine

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Section: Introductionmentioning

confidence: 99%

Optimization of an unscented Kalman filter for an embedded platform

Graybill

Gluckman

Kiani

2022

Computers in Biology and Medicine

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“…To overcome this problem, the Unscented Kalman Filter (UKF) is developed [32] , [33] . It is evaluated in many KF applications that UKF outperforms EKF [34] , [35] . In UKF, true mean and covariance values are captured by using the deterministic sigma points.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

2020

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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.

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Posterior Cramer–Rao Lower Bounds for dual Kalman estimation

Saatçı

Akan

2012

Digital Signal Processing

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Dual Unscented Kalman Filter and Its Applications to Respiratory System Modelling

Cited by 5 publications

References 23 publications

Optimization of an unscented Kalman filter for an embedded platform

Optimization of an unscented Kalman filter for an embedded platform

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

Posterior Cramer–Rao Lower Bounds for dual Kalman estimation

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