Validation of CW Doppler Radar Embedded Within Driving Seat For Heartbeat Detection Using NHA

Konishi, Takuro; Kawamura, Hirohide; Furuta, So; Yoshida, Kazuo

doi:10.1109/lifetech52111.2021.9391786

Cited by 5 publications

(6 citation statements)

References 5 publications

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“…Locating the radar close to the heart usually leads to higher signal quality. In some systems, such as the PhysioChair [ 40 ], the stress assessment method [ 41 ], and the method for monitoring a driver’s heartbeat information [ 42 ], the radar is installed in the position of the seat back directly opposite to the chest, and obvious heartbeat signals can be seen in the chest movement waveform demodulated from radar echoes. A simple processing method like a bandpass filter is sufficient to extract heartbeat signals with high quality.…”

Section: Discussionmentioning

confidence: 99%

A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring

Han

Zhai

Zhang

et al. 2023

Sensors

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Noncontact vital sign monitoring based on radar has attracted great interest in many fields. Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. This paper proposes a new algorithm for HRV monitoring in which frequency-modulated continuous-wave (FMCW) radar is used to separate echo signals from different distances, and the beamforming technique is adopted to improve signal quality. After the phase reflecting the chest wall motion is demodulated, the acceleration is calculated to enhance the heartbeat and suppress the impact of respiration. The time interval of each heartbeat is estimated based on the smoothed acceleration waveform. Finally, a joint optimization algorithm was developed and is used to precisely segment the acceleration signal for analyzing HRV. Experimental results from 10 participants show the potential of the proposed algorithm for obtaining a noncontact HRV estimation with high accuracy. The proposed algorithm can measure the interbeat interval (IBI) with a root mean square error (RMSE) of 14.9 ms and accurately estimate HRV parameters with an RMSE of 3.24 ms for MEAN (the average value of the IBI), 4.91 ms for the standard deviation of normal to normal (SDNN), and 9.10 ms for the root mean square of successive differences (RMSSD). These results demonstrate the effectiveness and feasibility of the proposed method in emotion recognition, sleep monitoring, and heart disease diagnosis.

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Section: Discussionmentioning

confidence: 99%

A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring

Han

Zhai

Zhang

et al. 2023

Sensors

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“…We have previously demonstrated that NHA can effectively suppress sidelobes and detect subtle heart rate signals hidden within respiratory signals [15], [38].…”

Section: ) Nhamentioning

confidence: 99%

“…The experimental setup included a 24-GHz 70mm-wide and 50-mm-long CW Doppler radar (Carea Corporation CRA-24 GHz). To measure the non-contact heart rates of automobile drivers, the Doppler radar has been installed either on the back of the seat [15], [38], [52] or directly on the seatbelt [18], and studies have been conducted to measure heart rate displacement mainly from chest circumference variation. Accordingly, we chose to install the radar behind the seat rather than in front to mitigate noise generated by the driver's movements.…”

Section: Experimental Conditions a Experimental Environment And Setupmentioning

confidence: 99%

“…In the context of heartbeat component detection using NHA, Konishi et al recorded the heartbeat of a stationary subject by placing a Doppler sensor in the driver's seat and confirmed the potential for heartbeat detection. They focused not only on the fundamental frequency but also on higher harmonics [38]. The results of the study indicated that NHA can discern high-frequency resolution and small signals, such as heart rate signals and heart rate harmonics, owing to its sidelobe-suppressing capabilities.…”

Section: Introductionmentioning

confidence: 98%

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Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis

Goto,

Horimoto,

Koyama

et al. 2024

IEEE Access

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The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an increased focus on the routine analysis of vital signs such as breathing and pulse rates. Radar technology has proven effective for non-contact, long-term monitoring of these vital signs, with frequency analysis being the default method for processing signals from Doppler radar owing to their inherent noise. However, conventional analysis approaches often struggle to detect weak signals buried within the sidelobes of other signals. Some data analysis techniques for Doppler radar rely on machine learning, but they struggle to generate clear time-frequency diagrams, complicating heartbeat detection. In this study, we employed non-harmonic analysis (NHA) as a frequency analysis method to mitigate sidelobe interference and implemented semantic segmentation for precise heartbeat detection. To validate the proposed approach, we conducted heartbeat detection tests both in stationary, low-noise conditions and in a noisy driving simulation environment. The results indicated that the NHA method successfully analyzed heartbeat harmonics, suggesting its potential for detecting heartbeat components through machine learning. To validate these findings, we determined the detection accuracy by comparing true and false positive rates, allowing us to quantify the detectability of heartbeats under both resting and driving simulation conditions.INDEX TERMS Continuous-wave Doppler radar (CW Doppler radar), driving simulation, harmonic, heartbeat, non-harmonic analysis (NHA).

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“…As well, beamforming imaging radar is applied to cancer diagnoses [ 18 , 19 , 20 ]. In the automotive industry, the radar function of monitoring respiration and heartbeat applies to occupant detection [ 21 , 22 , 23 , 24 , 25 ] and driver monitoring system [ 26 , 27 , 28 ]. Some companies already sell cars with occupancy detection radar.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact VITAL Signs Monitoring of a Patient Lying on Surgical Bed Using Beamforming FMCW Radar

Lim¹,

Jang²,

Song³

et al. 2022

Sensors

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Respiration and heartrates are important information for surgery. When the vital signs of the patient lying prone are monitored using radar installed on the back of the surgical bed, the surgeon’s movements reduce the accuracy of these monitored vital signs. This study proposes a method for enhancing the monitored vital sign accuracies of a patient lying on a surgical bed using a 60 GHz frequency modulated continuous wave (FMCW) radar system with beamforming. The vital sign accuracies were enhanced by applying a fast Fourier transform (FFT) for range and beamforming which suppress the noise generated at different ranges and angles from the patient’s position. The experiment was performed for a patient lying on a surgical bed with or without surgeon. Comparing a continuous-wave (CW) Doppler radar, the FMCW radar with beamforming improved almost 22 dB of signal-to-interference and noise ratio (SINR) for vital signals. More than 90% accuracy of monitoring respiration and heartrates was achieved even though the surgeon was located next to the patient as an interferer. It was analyzed using a proposed vital signal model included in the radar IF equation.

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Validation of CW Doppler Radar Embedded Within Driving Seat For Heartbeat Detection Using NHA

Cited by 5 publications

References 5 publications

A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring

A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring

Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis

Non-Contact VITAL Signs Monitoring of a Patient Lying on Surgical Bed Using Beamforming FMCW Radar

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