Nonlinear Least Squares Estimation for Breathing Monitoring Using FMCW Radars

Beltrao, Gabriel; Alaee-Kerahroodi, Mohammad; Schroeder, Udo; Tatarinov, Dimitri; Shankar, M. R. Bhavani

doi:10.23919/eurad50154.2022.9784561

Cited by 1 publication

(6 citation statements)

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“…Hence, the displacement signal can be modeled as a sum of harmonically related complex sinusoids, having frequencies that are integer multiples of the fundamental breathing/heartbeat frequency. To exploit this harmonic structure, in this section, we will use the simple NLS approach recently proposed for breathing rate estimation in [27] and [28], and extend it for heart rate estimation. For doing this, we need to take into consideration the different frequency ranges of these signals and particularly pay attention to the interference of breathing harmonics in the heartbeat signal.…”

Section: Adaptive Nls Estimationmentioning

confidence: 99%

“…For instance, if the true breathing rate is 20 bpm, an additional peak will be generated at 10 bpm (20 bpm is a harmonic of 10 bpm), and it may have a higher amplitude than the true one at 20 bpm. To avoid detecting these additional peaks, the NLS estimator proposed in [27] and [28] uses an initial (coarse) estimation as a reference for the NLS search region, which is then limited around this value. Besides eliminating these eventual strong low-frequency components, this strategy also reduces the computational effort to perform the grid search.…”

Section: B Breathing Rate Estimationmentioning

confidence: 99%

“…In [27] and [28], this initial reference estimate is obtained using a peak detection algorithm applied directly over the time-domain displacement signal. In this work, we propose a simpler implementation for that.…”

Section: B Breathing Rate Estimationmentioning

confidence: 99%

“…The final heart rate estimate fh for the mth processing window is directly obtained from the updated state vector in (28), and the algorithm runs for each processing window with the updated state vector, its updated covariance, and a new set of NLS estimates z h as inputs.…”

Section: Kalman Filter Selectionmentioning

confidence: 99%

“…Subsequently, we present an adaptive nonlinear least squares (ANLS) framework that explores the harmonic structure existing in the recovered displacement signal. For estimating the breathing rate, we simplify the nonlinear least squares (NLS) algorithm recently proposed in [27] and [28] and adaptively adjust the search region over the NLS objective function according to previous estimates from adjacent processing windows. For the heart rate estimation, we extend the basic NLS approach, and to avoid harmonic interference from breathing, we explore the original idea in [29] by generating multiple heart rate estimates based on the heartbeat's own harmonics.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Adaptive Nonlinear Least Squares Framework for Contactless Vital Sign Monitoring

Beltrao

Martins

Shankar

et al. 2023

IEEE Trans. Microwave Theory Techn.

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The respiratory and heart rates are critical physiological parameters, and conventional contact-based monitoring techniques may cause discomfort and epidermal damage, being therefore inadequate for long-term monitoring. Despite recent advances, accurate contactless vital sign monitoring is still challenging in practical scenarios, especially in relation to heart rate estimation. In this work, we propose a comprehensive framework for vital sign processing in frequency-modulated continuouswave radar systems and evaluate its performance with real data imitating common working conditions in an office environment. First, to improve the signal-to-noise ratio before estimation, we propose a novel slow-time phase correlation processing, which allows early integration of the vital sign energy at nearby range bins. Subsequently, we present an adaptive nonlinear least squares framework that explores the harmonic structure existing in the recovered displacement signal. An additional Kalman filter stage is designed to select among multiple estimates from different search regions, thus conferring adaptivity and robustness against harmonic interference and noise. This approach largely provides estimates within the predefined error intervals, being capable of tracking the true breathing and heart rate values even during continuous small body movements. The final accuracy and root mean square error values have shown enhanced estimation, outperforming conventional spectral estimation and other recently proposed methods in almost all scenarios.

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Section: Adaptive Nls Estimationmentioning

confidence: 99%

Section: B Breathing Rate Estimationmentioning

confidence: 99%