Adaptive Separation of Respiratory and Heartbeat Signals among Multiple People Based on Empirical Wavelet Transform Using UWB Radar

He, Mi; Nian, Yongjian; Xu, Luping; Qiao, Lihong; Wang, Wenwu

doi:10.3390/s20174913

Cited by 30 publications

(24 citation statements)

References 25 publications

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“…The mean bias was only 0.114 bpm with LOAs of 3.14 and −2.91 bpm. These results show very good agreement between radar estimates and the reference device, with comparable or smaller LOAs than recent studies [61], [62], [63], for both breathing and heart rate estimation.…”

Section: Resultssupporting

confidence: 83%

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: Resultssupporting

confidence: 83%

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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“…EWT decomposes non-stationary signal into several sub-signals using a series of wavelet filters based on Fourier spectrum segments [ 39 ], which are applied to remove noise and extract interested signals in several researches [ 40 , 41 , 42 ]. After DC removal, there still exist clutters reflected from background.…”

Section: Methodsmentioning

confidence: 99%

Contactless Simultaneous Breathing and Heart Rate Detections in Physical Activity Using IR-UWB Radars

Zhang

Yang

Ding

et al. 2021

Sensors

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Vital signs monitoring in physical activity (PA) is of great significance in daily healthcare. Impulse Radio Ultra-WideBand (IR-UWB) radar provides a contactless vital signs detection approach with advantages in range resolution and penetration. Several researches have verified the feasibility of IR-UWB radar monitoring when the target keeps still. However, various body movements are induced by PA, which lead to severe signal distortion and interfere vital signs extraction. To address this challenge, a novel joint chest–abdomen cardiopulmonary signal estimation approach is proposed to detect breath and heartbeat simultaneously using IR-UWB radars. The movements of target chest and abdomen are detected by two IR-UWB radars, respectively. Considering the signal overlapping of vital signs and body motion artifacts, Empirical Wavelet Transform (EWT) is applied on received radar signals to remove clutter and mitigate movement interference. Moreover, improved EWT with frequency segmentation refinement is applied on each radar to decompose vital signals of target chest and abdomen to vital sign-related sub-signals, respectively. After that, based on the thoracoabdominal movement correlation, cross-correlation functions are calculated among chest and abdomen sub-signals to estimate breath and heartbeat. The experiments are conducted under three kinds of PA situations and two general body movements, the results of which indicate the effectiveness and superiority of the proposed approach.

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“…EWT has shown excellent performance in decomposing non-stationary signals, which effectively remove noise and extract interesting signal components. It has been used in image processing, disease diagnosis, and other fields [40][41][42].…”

Section: B Heartbeat Signal Extraction Algorithm Based On Anf and Ewtmentioning

confidence: 99%

Non-Contact Heart Rate Monitoring Based on Millimeter Wave Radar

et al. 2022

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Millimeter Wave(mmWave) radar has been widely used in vital sign monitoring with noncontact and privacy protection. To reduce the effect of random body motion, respiration, and its harmonics on heart rate estimation, the heart rate monitoring based on frequency-modulated continuous-wave (FMCW) radar is researched in this paper. First, a vital sign signal extraction algorithm is designed for the Range Bin variation caused by random body motion and heavy breathing. Second, a heartbeat signal extraction algorithm is designed based on Adaptive Notch Filter (ANF) and Empirical Wavelet Transform (EWT). The harmonic of respiration will be suppressed, and the heartbeat signal will be separated. Finally, the weighted estimation is performed according to the relationship of harmonics of the heartbeat signal to obtain the heart rate. Twenty subjects are invited to the experiment. The experimental results show that the proposed method can improve the signal-to-noise ratio (SNR), reduce the harmonic interference, and estimate the heart rate with a mean absolute error less than 4BPM.INDEX TERMS FMCW radar, heart rate estimation, ANF, EWT, the weighted estimation, the relationship of harmonics

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Adaptive Separation of Respiratory and Heartbeat Signals among Multiple People Based on Empirical Wavelet Transform Using UWB Radar

Cited by 30 publications

References 25 publications

Adaptive Nonlinear Least Squares Framework for Contactless Vital Sign Monitoring

Adaptive Nonlinear Least Squares Framework for Contactless Vital Sign Monitoring

Contactless Simultaneous Breathing and Heart Rate Detections in Physical Activity Using IR-UWB Radars

Non-Contact Heart Rate Monitoring Based on Millimeter Wave Radar

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