Measurement of instantaneous heart rate using radar echoes from the human head

Sakamoto, Takuya; Muragaki, Masashi; Tamura, Koji; Okumura, Shun; Sato, Toru; Mizutani, K.; Inoue, Kohei; Fukuda, Takahiro; Sakai, Hiroyuki

doi:10.1049/el.2018.0811

Cited by 15 publications

(12 citation statements)

References 15 publications

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“…Nagae and Mase [13] measured the leg of a seated person. The present authors measured the soles [14] and the top of the head [15]. Vinci et al [16] measured the front and back sides of the torso for comparison.…”

Section: Introductionmentioning

confidence: 87%

Noncontact Monitoring of Heartbeat and Movements during Sleep Using a Pair of Millimeter-Wave Ultra-Wideband Radar Systems

Sakamoto

Mitani

Sato

2021

IEICE Trans. Commun.

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We experimentally evaluate the performance of a noncontact system that measures the heartbeat of a sleeping person. The proposed system comprises a pair of radar systems installed at two different positions. We use millimeter-wave ultra-wideband multiple-input multiple-output array radar systems and evaluate the performance attained in measuring the heart inter-beat interval and body movement. The importance of using two radar systems instead of one is demonstrated in this paper. We conduct three types of experiments; the first and second experiments are radar measurements of three participants lying on a bed with and without body movement, while the third experiment is the radar measurement of a participant actually sleeping overnight. The experiments demonstrate that the performance of the radar-based vital measurement strongly depends on the orientation of the person under test. They also show that the proposed system detects 70% of rolling-over movements made overnight.

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Section: Introductionmentioning

confidence: 87%

Noncontact Monitoring of Heartbeat and Movements during Sleep Using a Pair of Millimeter-Wave Ultra-Wideband Radar Systems

Sakamoto

Mitani

Sato

2021

IEICE Trans. Commun.

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“…Considering all these merits, we chose 79-GHz radar in this study. The pitch between any two adjacent antennas is 4.6 mm, which corresponds to 0.92 wavelength [37], [38]. We measured the radar echo reflected from the shoulder of each participant.…”

Section: A System Model and Experimental Settingsmentioning

confidence: 99%

“…We synthesized the data from 16 channels and applied a beamforming technique to maximize the SNR. The beamforming uses the eigenvector that corresponds to the maximum eigenvalue of the correlation matrix of the signal vector as the weighting vector [38]. The experimental setup is shown in Fig.…”

Section: A System Model and Experimental Settingsmentioning

confidence: 99%

“…One is that the performance is always degraded by the interference between the heartbeat and respiration components. To address this issue, some researchers have adopted filtering based on Fourier analysis, wavelet analysis, eigen features, and other methods [21], [22], [38]. Although there has been good progress on this front, it is still challenging to separate the respiration and heartbeat components [18], [23].…”

Section: Introductionmentioning

confidence: 99%

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Person-Specific Heart Rate Estimation With Ultra-Wideband Radar Using Convolutional Neural Networks

Sakamoto

Oishi

et al. 2019

IEEE Access

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Vital-sign estimation using ultra-wideband (UWB) radar is preferable because it is contactless and less privacy-invasive. Recently, many approaches have been proposed for estimating heart rate from UWB radar data. However, their performance is still not reliable enough for practical applications. To improve the accuracy, this study employs convolutional neural networks to learn the special patterns of the heartbeats. In the proposed system, skin displacements of the target person are measured using UWB radar, and the radar signal is converted to a two-dimensional matrix, which is used as the input of the designed neural networks. Meanwhile, two triangular waves corresponding to the peaks and valleys in an electrocardiogram are adopted as the output of the networks. The proposed system then identifies each individual and estimates the heart rate automatically based on the already trained neural networks. The estimation error of the interbeat interval computed using our approach was reduced to 4.5 ms in the best case; and 48.5 ms in the worst case. Experiment results show that the proposed approach significantly outperforms a conventional method. The proposed machine learning approach achieves both personal identification and heart rate estimation simultaneously using UWB radar data for the first time. Moreover, this study found that using the respiration and heartbeat components together may enhance the accuracy of heart rate estimation, which is counter-intuitive, because the respiration is usually believed to interfere with the heartbeat. INDEX TERMS Ultra-wideband radar, heart rate, vital signs, convolutional neural networks.

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“…Further more, some complex algorithms for respiratory heartbeat detection based on UWB radar were proposed. T. Sakamoto used an algorithm based on continuous wavelet filtering and ensemble empirical mode decomposition (EEMD) to extract the heart and lung signals [32]. Then ZhenZhen Duan used the variational mode decomposition (VMD) algorithm to extract heartbeat signals and respiratory signals [23].…”

Section: Introductionmentioning

confidence: 99%

Harmonic Multiple Loop Detection (HMLD) Algorithm for Not-Contact Vital Sign Monitoring Based on Ultra-Wideband (UWB) Radar

Zhang

et al. 2020

IEEE Access

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In this paper, the harmonic multiple loop detection (HMLD) algorithm for heart rate (HR) and respiration rate (RR) estimation with impulse radio ultrawideband (IR-UWB) radar is introduced. The algorithm includes two parts: one is the harmonic multiple discriminant principle and the other is the cyclic spectrum updating process. The harmonic multiple discriminant principle is used to detect whether the peak point in the fundamental frequency band is the true vital sign signal. The cyclic spectrum updating process can remove the error peak point according to the results obtained from the part one. In this algorithm, threshold setting is not needed and the estimation error caused by the erroneous threshold can be reduced. Only the fundamental and second harmonic frequencies are needed for HR and RR estimation, so the algorithm has strong environmental robustness and low requirement of radar hardwares. Compared with other algorithms, HMLD algorithm provides reasonable average error rates (4.95% and 5.06%) compared to real data from the oximeter in RR and HR detection. The real-time vital signs detection experiment shows that HMLD algorithm can not only detect the vital signs rate and record the historical curve in real time, but also detect the location information, which proves the validity of HMLD algorithm in real-time vital signs detection. INDEX TERMS Harmonic multiple loop detection, biomedical signal processing, impulse radio ultrawideband (IR-UWB) radar, heart rate (HR), respiration rate (RR).

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Measurement of instantaneous heart rate using radar echoes from the human head

Cited by 15 publications

References 15 publications

Noncontact Monitoring of Heartbeat and Movements during Sleep Using a Pair of Millimeter-Wave Ultra-Wideband Radar Systems

Noncontact Monitoring of Heartbeat and Movements during Sleep Using a Pair of Millimeter-Wave Ultra-Wideband Radar Systems

Person-Specific Heart Rate Estimation With Ultra-Wideband Radar Using Convolutional Neural Networks

Harmonic Multiple Loop Detection (HMLD) Algorithm for Not-Contact Vital Sign Monitoring Based on Ultra-Wideband (UWB) Radar

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