Figure 6 from Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning | Semantic Scholar (2024)

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HomeOSD, an advanced appliance status-detection system that uses mmWave radar, is introduced and a Vibration-Intensity Metric based on periodic signal characteristics is introduced to reduce interference from other moving objects, like people.

A neural network model for the automatic detection and classification of objects in front of a vehicle, including pedestrians and other vehicles, using radar technology is developed, demonstrating that this model can be used to inform drivers of potential hazards or to initiate autonomous braking and steering maneuvers to prevent collisions.

The proposed novel noncontact heart-beat signal modeling and estimation algorithm using a compact 2.4-GHz Doppler radar is accurate, robust, and simple, and demonstrates an average heart- Beat detection accuracy of more than 90% at a distance of 1.5 m away from the subjects.

HeRe for low-power radar, a deep learning (DL) approach for heartbeat signal reconstruction, using a neural network to identify patterns in the signal, leading to a significant improvement in the SNR of the heartbeat signal.

A deep learning framework that estimates the respiration rate (RR) and heart rate (HR) in real time from long-term data measured during sleep using a contactless impulse radio ultrawide-band (IR-UWB) radar was developed and evaluated.

By using a more complete model of return signal, a new routine is introduced for the robust detection of breathing and HR that extremely alleviates the problems associated with harmonic interferences.

The merit of this approach lies in that, the quasi-ideal matching impulse approximating the actual heart- beat signal can be readily retrieved by subtracting the polynomial waveform from a received signal, which provides high adaptability for individual subjects.

By quantifying the rate-of-change of ECG spectral components over time, it is shown that heart rate estimates can be reliably obtained even in extremely noisy signals, thus bypassing the need for ECG enhancement.

A customized bandpass filter obtained through adaptive filtering is used, applied to the electrocardiography signal, to extract a cardiac radar signal model, free of issues that arise from random body motion (RBM) or from an eventual decrease in signals quality, which are inherent issues of long-term acquisitions.

It was theoretically and experimentally demonstrated that arctangent demodulation with wavelet-based signal processing enhanced with template matching is effective to estimate HRV parameters with accuracy on the order of 10 ms with 2.4 GHz DR.

An investigation of heart rate variability (HRV) and respiratory sinus arrhythmia (RSA) indices using data obtained from Doppler radar cardiopulmonary remote sensing achieved high accuracy using a direct-conversion quadrature radar system with linear demodulation method.

The high-precision displacement detection algorithm of radar is applied to measure cardiac motion, and a more accurate cardiac motion waveform is extracted by eliminating the fundamental frequency component of the heartbeat waveform.

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