Doppler Radar Vital Sign Detection Based on Complex Continuous Basis Pursuit Algorithm

This paper presents a Doppler radar sensor system with camera-aided random body movement cancellation (RBMC) techniques for noncontact vital sign detection. The camera measures the subject's random body motion that is provided for the radar system to perform RBMC and extract the uniform vital sign signals of respiration and heartbeat. Three RBMC strategies are proposed: 1) phase compensation at radar RF front-end, 2) phase compensation for baseband complex signals, and 3) movement cancellation for demodulated signals. Both theoretical analysis and radar simulation have been carried out to validate the proposed RBMC techniques. An experiment was carried out to measure a subject person who was breathing normally but randomly moving his body back and forth. The experimental result reveals that the proposed radar system is effective for RBMC.

Detecting respiration and heartbeat information from human chest movement is crucial for non‐contact vital sign detection. Due to the small frequency difference between respiration and heartbeat signals and the relatively low amplitude of the heartbeat signal, it is challenging to separate and extract them completely. This letter proposes a successive variational mode extraction method based on spectrum trend for the extraction of respiration and heartbeat signals. The proposed method adaptively determines the initial centre frequency and extraction order of the desired signal modes by analyzing the spectrum trend of the signal to be decomposed. Additionally, a recursive framework is introduced to sequentially extract the desired signal modes. Both simulation and practical experiment results show that the method is effective in extracting respiration and heartbeat signals, significantly improving the accuracy of estimating respiration and heartbeat frequencies.

Simulations were performed based on a model developed in ADS to demonstrate the effects of non-linear cosine transfer function in non-contact vital sign detection. Harmonics and intermodulation effects have been analyzed theoretically and demonstrated by simulation. It is shown that in contrast to the observation from simple linear approximation that detection sensitivity always increases with carrier frequency, there is an optimum carrier frequency for non-contact Doppler radar vital sign detection. For typical values of human chest wall movement, the simulation shows that the upper bound of the carrier frequency should be limited to the lower region of the Ka-band to improve detection accuracy

This paper presents a novel technique of cancelling random body movement (BM) in Doppler radar noncontact vital sign detection based on deep neural network (DNN). Respiration motions with different frequencies and different phase offsets are synthesized and mixed with simulated BMs to train the DNN model. Neither hardware modification nor extra hardware complexity is needed. The proposed DNN based method can separate the stable respiration motion from the signal mixture of respiration and BM without prior information from the BM. Experimental results show that the proposed DNN based method allows a single radar sensor to successfully reconstruct stable respiration motions without BM disturbance.

This paper presents a 60-GHz CMOS direct-conversion Doppler radar RF sensor with a quasi-circulator (QC) and a clutter canceller circuit for single-antenna noncontact human vital-signs detection. A high isolation QC is designed to provide better detection sensitivity for the tiny vital-signs detection for the single-antenna Doppler radar architecture. The clutter canceller performs cancellation for the transmitting leakage power from the circulator and the background reflection clutter to enhance the detection sensitivity of weak vital signals. The measurement shows that the total transmitting power is 3 dBm while the conversion gain of the sub-harmonic receiver is 10.5 dB. In the human vital-signs detection measurement, at a distance of 75 cm, the detected heartbeat (1–1.3 Hz) and respiratory (0.35–0.45 Hz) signals can be clearly observed. The RF sensor is fabricated in 90-nm technology with a chip size of 2 mm × 2 mm and a consuming power of 217 mW. The presented CMOS vital-signs Doppler radar RF sensor will be very useful for the wireless remote physiological monitoring healthcare system and the tiny vibrations detection applications.

The non-contact vital sign detection of an individual in indoor environments using a radar sensor has been widely researched by recent advances in the IoT industry. Most of these studies have been conducted by analyzing the spectrum of extracted phase variations in the echo signal of a motionless individual that has only micro-motion (that is, vital signs) caused by both the lung and the heart. However, in the case of a walking individual, the performance of the vital sign detection using conventional methods may be rapidly degraded owing to extreme phase fluctuations caused by the relatively large movement of the individual compared to the vital sign motions. To address this problem, we propose a new motion compensation method for the vital sign detection of a walking individual. First, we conduct a modeling of the echo signal for a walking individual using a radar and discuss problems in vital sign detection. Next, a new algorithm for real-time motion compensation is suggested based on a moving averaging filter and mode in statistics. In the experiments using an IR-UWB (Impulse-radio ultra-wideband) radar, we observed that the proposed method performed a successful real-time vital sign detection of a walking individual.

The respiratory and heartbeat signals can accurately reflect the health status of the tester, which is of great clinical significance. Compared with the traditional contact detection method, the non-contact radar detection method does not require the tester to wear any sensor equipment and will not cause any discomfort to the tester. The frequency modulated continuous wave (FMCW) radar has the characteristics of simple structure, high resolution, strong stability, and low transmission power and is used for the detection of respiratory and heartbeat signals. This paper designs a low-power, low-cost respiratory, and heartbeat signal detection system based on FMCW radar. In addition, the variational modal decomposition (VMD) method is used to separate respiration and heartbeat signals to obtain accurate respiration and heartbeat rates. The results show that the radar system for detecting respiratory and heartbeat signals has high detection accuracy.

Doppler radar for monitoring vital signals is an emerging tool, and how to remove the noise during the detection process and reconstruct the accurate respiration and heartbeat signals are hot issues in current research. In this paper, a novel radar vital signal separation and de-noising technique based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy (SampEn), and wavelet threshold is proposed. First, the noisy radar signal was decomposed into a series of intrinsic mode functions (IMFs) using ICEEMDAN. Then, each IMF was analyzed using SampEn to find out the first few IMFs containing noise, and these IMFs were de-noised using the wavelet threshold. Finally, in order to extract accurate vital signals, spectrum analysis and Kullback–Leible (KL) divergence calculations were performed on all IMFs, and appropriate IMFs were selected to reconstruct respiration and heartbeat signals. Moreover, as far as we know, there is almost no previous research on radar vital signal de-noising based on the proposed technique. The effectiveness of the algorithm was verified using simulated and measured experiments. The results show that the proposed algorithm could effectively reduce the noise and was superior to the existing de-noising technologies, which is beneficial for extracting more accurate vital signals.

This article presents a 24GHz doppler radar detection, high gain, receiver frontend for cancelling random heart rate sensing by deep neural network (DNN) in noncontact healthcare applications. The proposed heart rate sensing with algorithm, different frequencies and phase offsets are synthesized and integrated with simulation to train the DNN analysis. This article promoted integrated circuit in LNA, Mixer and bandpass filter with array antenna for contribution of doppler radar heart rate detection Experimental results demonstrate that the proposed idea can alleviate the noise cancellation and stable signal when body movement or normal respiration motion for 24GHz doppler radar vital sign detection.

In this paper, a concurrent dual-band hybrid down conversion architecture based on continuous-wave (CW) Doppler radar is demonstrated. The proposed system operates at 2.05-/1.64-GHz simultaneously. The dual-band can solve the null detection point problem generated at quarter-wavelength distance between the target and antenna. The detection results from different channels can be mutually verified to improve the system accuracy. The vital sign can be detected by the radar through the wooden board at higher transmit power. For satisfying the requirements of dual-band RF transmitters, and a two-stage dual-band power amplifier is designed. Experimental results have demonstrated the feasibility of the system.

Vital signs such as heart rate (HR) and respiration rate (RR) are essential physiological parameters that are routinely used to monitor human health and bodily functions. They can be continuously monitored through contact or contactless measurements performed in the home or a hospital. In this study, a contactless Doppler radar W‐band sensing system was used for short‐range, contactless vital sign estimation. Frequency‐modulated continuous wave (FMCW) measurements were performed to reduce the influence of a patient's micromotion. Sensing software was developed that can process the received chirps to filter and extract heartbeat and breathing rhythm signals. The proposed contactless sensing system eliminates the need for the contact electrodes, electric patches, photoelectric sensors, and conductive wires used in typical physiological sensing methods. The system operates at 76–81 GHz in FMCW mode and can detect objects on the basis of changes in frequency and phase. The obtained signals are used to precisely monitor a patient's HR and RR with minimal noise interference. In a laboratory setting, the heartbeats and breathing rhythm signals of healthy young participants were measured, and their HR and RR were estimated through frequency‐ and time‐domain analyses. The experimental results confirmed the feasibility of the proposed W‐band mm‐wave radar for contactless and short‐range continuous detection of human vital signs.

The non-contact monitoring of vital signs by radar has great prospects in clinical monitoring. However, the accuracy of separated respiratory and heartbeat signals has not satisfied the clinical limits of agreement. This paper presents a study for automated separation of respiratory and heartbeat signals based on empirical wavelet transform (EWT) for multiple people. The initial boundary of the EWT was set according to the limited prior information of vital signs. Using the initial boundary, empirical wavelets with a tight frame were constructed to adaptively separate the respiratory signal, the heartbeat signal and interference due to unconscious body movement. To verify the validity of the proposed method, the vital signs of three volunteers were simultaneously measured by a stepped-frequency continuous wave ultra-wideband (UWB) radar and contact physiological sensors. Compared with the vital signs from contact sensors, the proposed method can separate the respiratory and heartbeat signals among multiple people and obtain the precise rate that satisfies clinical monitoring requirements using a UWB radar. The detection errors of respiratory and heartbeat rates by the proposed method were within ±0.3 bpm and ±2 bpm, respectively, which are much smaller than those obtained by the bandpass filtering, empirical mode decomposition (EMD) and wavelet transform (WT) methods. The proposed method is unsupervised and does not require reference signals. Moreover, the proposed method can obtain accurate respiratory and heartbeat signal rates even when the persons unconsciously move their bodies.

The acquisition of ballistocardiogram (BCG) signals during sleeping always presents all kinds of high-frequency noise, which causes the problem to extract effective signals. This article proposes a new method to extract heartbeat and respiration signals by combining the whale optimization algorithm (WOA) and variational mode decomposition (VMD). Firstly, the sample entropy and energy difference ratio are used as the fitness function of WOA to optimize the penalty factor in the variational mode decomposition a and the number of decomposition layers K;Then the original signal was decomposed into multiple modal components (IMF) by optimized VMD; Finally, respiration and heartbeat signals were reconstructed, and their frequencies got calculated. Compared with the experimental results of empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD), the average respiratory rate error obtained by this algorithm is 0.097 and 0.013 lower than EMD and EEMD respectively, and the average heart rate error is 0.03 and 0.006 lower than EMD and EEMD respectively, it is proved that the algorithm can extract breath and heartbeat signals more effectively and accurately.

Radar sensors have been widely used in non-contact vital signs detection, especially for breathing and heartbeat monitoring. However, most of the achievements in the field are verified in small scenes. In this article, a unique TX-RX joint beamforming mechanism for multi-people breathing and heartbeat measurement is proposed, where a mm-wave FMCW radar with 9 TX channels and 16 RX channels is used. The transmitting beam pointing to the desired subject is formed by phase-shifting, which contributes to a longer detection range than the state-of-art system. Digital beamforming is designed to obtain optimal spatial filtering at the receiving end, which enhances the capability in multi-people measuring. Experimental results showed that, at a large scene with the detection range from 1.8 m to 12 m, the proposed mechanism can distinguish the breathing and heartbeat signal from different people accurately.

This paper presents a low power interference-robust radar transceiver architecture for noncontact vital sign detection and mobile healthcare applications. A duty-cycled transceiver design is proposed to significantly reduce power consumption of front-end circuits. Occupying 3-to-5 GHz band with four 500 MHz sub-channels, the radar mitigates the narrowband interference (NBI) problem with the frequency hopping scheme. In the time domain, the $\Delta \Sigma $ TDC is employed to achieve fine ranging resolution with noise shaped oversampling. The transceiver implemented in 65 nm CMOS consumes 17.2 mW from a 1.0-V supply when duty-cycled and achieves an RMS detection accuracy of 5.68 mm.