Tracking driver's heart rate by continuous-wave Doppler radar

Lee, Kwang Jin; Park, Chanki; Lee, Boreom

doi:10.1109/embc.2016.7591952

Cited by 30 publications

(15 citation statements)

References 14 publications

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“…In addition, they can be used as a home care service for the elderly through continuous monitoring system [10,11]. Furthermore, they can be used for a driver monitoring system for detecting and alerting drowsy driver [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Novel Vital-Sign Sensing Algorithm for Multiple Subjects Based on 24-GHz FMCW Doppler Radar

et al. 2019

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A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed. The approach uses a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method. Doppler processing and spectral estimation are concurrently implemented to detect vital signs from more than one subject, revealing excellent results. The parametric spectral estimation method is utilized to clearly identify multiple targets, making it possible to distinguish multiple targets located less than 40 cm apart, which is beyond the limit of the theoretical range resolution. Fourier transformation is used to extract phase information, and the result is combined with the spectral estimation result. To eliminate mutual interference, the range integration is performed when combining the range and phase information. By considering breathing and heartbeat periodicity, the proposed algorithm can accurately extract vital signs in real time by applying an auto-regressive algorithm. The capability of a contactless and unobtrusive vital sign measurement with a millimeter wave radar system has innumerable applications, such as remote patient monitoring, emergency surveillance, and personal health care.

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Section: Introductionmentioning

confidence: 99%

A Novel Vital-Sign Sensing Algorithm for Multiple Subjects Based on 24-GHz FMCW Doppler Radar

et al. 2019

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“…A similiar approach was proposed by Lee and colleagues in 2016 [ 178 ]. Similiar to Vinci’s second scenario, the authors proposed to place a 24 GHz CW sensor in the backrest of the car seat.…”

Section: Radar-based Methodsmentioning

confidence: 99%

“…This means that the interface is characterized by some sort of impedance measurement. In 2016, Lee et al demonstrated that motion artifacts in radar systems can be greatly reduced using multiple signal classification (MUSIC) [ 178 ]. The authors investigated three different driving conditions: halted vehicle, driving at 60 kmh and driving at 80 kmh.…”

Section: Challenges Of In-vehicle Measurementsmentioning

confidence: 99%

Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review

Leonhardt

Leicht

Teichmann

2018

Sensors

103

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This review provides an overview of unobtrusive monitoring techniques that could be used to monitor some of the human vital signs (i.e., heart activity, breathing activity, temperature and potentially oxygen saturation) in a car seat. It will be shown that many techniques actually measure mechanical displacement, either on the body surface and/or inside the body. However, there are also techniques like capacitive electrocardiogram or bioimpedance that reflect electrical activity or passive electrical properties or thermal properties (infrared thermography). In addition, photopleythysmographic methods depend on optical properties (like scattering and absorption) of biological tissues and—mainly—blood. As all unobtrusive sensing modalities are always fragile and at risk of being contaminated by disturbances (like motion, rapidly changing environmental conditions, triboelectricity), the scope of the paper includes a survey on redundant sensor arrangements. Finally, this review also provides an overview of automotive demonstrators for vital sign monitoring.

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“…Many researches have investigated the heartbeat detection via a Doppler sensor to achieve non-contact heartbeat detection without device attachment. Some conventional methods estimate the HR based on frequency analysis by (i) Fast Fourier Transform (FFT) [10][11], (ii) Wavelet Transform (WT) [12][13], (iii) Multiple Signal Classification (MUSIC) [14] [15], and DCT [16]. In contrast, by detecting a heartbeat signal over the signal obtained through the extended signal processing, the conventional methods [17]- [23] estimate the BBI.…”

Section: Related Workmentioning

confidence: 99%

ECG Signal Reconstruction via Doppler Sensor by Hybrid Deep Learning Model With CNN and LSTM

2020

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An Electrocardiogram (ECG) is a typical method used to detect heartbeat, and an ECG signal analysis enables the detection of some heart diseases. However, the ECG-based heartbeat detection requires device attachment, which is not preferred for daily use. A Doppler sensor could be a device used to enable the non-contact heartbeat detection. In this paper, we propose a Doppler sensor-based ECG signal reconstruction method by a hybrid deep learning model with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). An ECG signal can be reconstructed by relating features of a heartbeat signal obtained by a Doppler sensor to those of the ECG signal. Thus, we construct the deep learning model that extracts the spatial and temporal features from the heartbeat signal by CNN and LSTM. Based on the extracted features, the ECG signal is reconstructed. We conducted experiments to observe heartbeat against 9 healthy subjects without heart disease. The experimental results showed that our method performed ECG signal reconstruction with a correlation coefficient of 0.86 between the reconstructed and actual ECG signals, even without attaching devices. The results indicate that it is possible to remotely reconstruct an ECG signal from a heartbeat signal via a Doppler sensor.

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Tracking driver's heart rate by continuous-wave Doppler radar

Cited by 30 publications

References 14 publications

A Novel Vital-Sign Sensing Algorithm for Multiple Subjects Based on 24-GHz FMCW Doppler Radar

A Novel Vital-Sign Sensing Algorithm for Multiple Subjects Based on 24-GHz FMCW Doppler Radar

Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review

ECG Signal Reconstruction via Doppler Sensor by Hybrid Deep Learning Model With CNN and LSTM

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