Abstract

Further applications of impulse radio ultra-wideband radar in mobile health are hindered by the difficulty in extracting such vital signals as heartbeats from moving targets. Although the empirical mode decomposition based method is applied in recovering waveforms of heartbeats and estimating heart rates, the instantaneous heart rate is not achievable. This paper proposes a Heartbeat Estimation And Recovery (HEAR) approach to expand the application to mobile scenarios and extract instantaneous heartbeats. Firstly, the HEAR approach acquires vital signals by mapping maximum echo amplitudes to the fast time delay and compensating large body movements. Secondly, HEAR adopts the variational nonlinear chirp mode decomposition in extracting instantaneous frequencies of heartbeats. Thirdly, HEAR extends the clutter removal method based on the wavelet decomposition with a two-parameter exponential threshold. Compared to heart rates simultaneously collected by electrocardiograms (ECG), HEAR achieves a minimum error rate 4.6% in moving state and 2.25% in resting state. The Bland–Altman analysis verifies the consistency of beat-to-beat intervals in ECG and extracted heartbeat signals with the mean deviation smaller than 0.1 s. It indicates that HEAR is practical in offering clinical diagnoses such as the heart rate variability analysis in mobile monitoring.

Highlights

  • Ultra-wideband (UWB) radar has been widely developed for heart rate monitoring, since it provides a contactless measurement

  • The variable HRecg is the reference heart rate detected in ECG recordings

  • Variables HRvncmd and HRst f t are heart rates respectively extracted by Heartbeat Estimation And Recovery (HEAR) and the scheme of STFT

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Summary

Introduction

Ultra-wideband (UWB) radar has been widely developed for heart rate monitoring, since it provides a contactless measurement. In contrast to the dynamic Electrocardiogram (ECG) monitoring, UWB radar acquires heart rates or respiratory rates without bringing inconveniences in users’ activities in scenarios such as the sleep apnea monitoring. It is difficult to sense vital signs of targets with large body movements. A solution [1] for keeping measurements steady chooses to quit detecting when the existence of motion artifacts is recognized. It restricts targets in static states to avoid interferences in extracting vital signs. In order to promote applications of UWB radars in mobile health monitoring, it is necessary to release constraints on targets’ statuses

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