Noncontact Vital Sign Detection Research Articles

Vital signs detection of moving targets using FMCW radar

Abstract Respiratory and heartbeat rates are crucial indicators for human health assessment. Compared to contact-based measurements, millimeter-wave radar detection of these vital signs avoids the discomfort caused by physical contact and better protects personal privacy, making it highly valuable for home health monitoring. The use of millimeter-wave radar for vital sign detection of the human body is mostly focused on targets in a stationary state at present. However, the human body may sway or even move during actual detection. This article proposes a non-contact vital sign detection method for moving targets. Compared with methods for detecting vital signs of stationary targets, detecting vital signs of moving targets requires determining the range bin where the targets are located continuously and extracting target phase information. The noise components such as movement and sway contained in the phase signal need to be removed. In this paper, moving target indication is used to remove static components, an adaptive range bin selection method is proposed to determine the range bin where the targets are located, and range bin selection fluctuation is smoothed using a moving average filter. The wavelet transform is used to decompose the phase signal, remove swaying noise based on autocorrelation function, and reconstruct the life signal for different scale factors. A bandpass filter is used to separate the respiratory and heartbeat signals, and a notch filter is designed to suppress respiratory harmonic signals. The experimental results show that the proposed method can separate vital signs signals from the phase signals of moving targets, achieving detection of respiration and heartbeat rate. The average accuracy of respiration and heartbeat rate detection is 94.7% and 95.5%, respectively.

Cutting-Edge Microwave Sensors for Vital Signs Detection and Precise Human Lung Water Level Measurement

In this article, a comprehensive review is presented of recent technological advancements utilizing electromagnetic sensors in the microwave range for detecting human vital signs and lung water levels. With the main objective of improving detection accuracy and system robustness, numerous advancements in front-end architecture, detection techniques, and system-level integration have been reported. The benefits of non-contact vital sign detection have garnered significant interest across a range of applications, including healthcare monitoring and search and rescue operations. Moreover, some integrated circuits and portable systems have lately been shown off. A comparative examination of various system architectures, baseband signal processing methods, system-level integration strategies, and possible applications are included in this article. Going forward, researchers will continue to focus on integrating radar chips to achieve compact form factors and employ advanced signal processing methods to further enhance detection accuracy.

Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System.

An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field.

Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.

Non-Contact Vital Sign Detection With High Noise and Clutter Immunity Based on Coherent Low-IF CW Radar

Non-Contact Vital Sign Detection With High Noise and Clutter Immunity Based on Coherent Low-IF CW Radar

Accurate multi-target vital signs detection method for FMCW radar

The frequency modulated continuous wave (FMCW) radar has gained increasing attention in the field of noncontact vital signs detection. However, the detection of the target chest’s range bin is easily influenced by various clutters and interferences, posing numerous challenges to vital signs detection, particularly in scenarios involving multiple targets This paper proposes an anti-clutter and interference method that combinates moving target indication (MTI), constant false alarm rate (CFAR) detection, K-means clustering, and local search with moving average filtering to accurately detect multi-target vital signs. Moreover, it employs the extended differentiate and cross multiply algorithm for phase demodulation to overcome phase discontinuity. The proposed method is evaluated using a 77 GHz FMCW radar in an office environment. Experimental results demonstrate that the proposed method can effectively detect multiple targets amidst various clutters and small random body movement, yielding mean accuracies of 94.2 % and 95.1 % for breathing rate and heartbeat rate, respectively.

A new approach to contactless probing of vital signs

Vital sign detection is used across ubiquitous scenarios in medical and health settings and contact and wearable sensors have been widely deployed. Contactless detection can be achieved using camera imaging, but it is susceptible to ambient light conditions along with privacy concerns. This brief report appraises a photonic radar for non-contact vital sign detection. The high resolution of the radar system enables accurate respiratory detection from breathing simulators and a cane toad as a human proxy. This could cater for contactless and high-resolution vital sign detection to meet the increasing demands of future medical and healthcare applications.

Contactless methods to acquire heart and respiratory signals—A review

<p align="justify">The vital sign is the most important parameter for the internal health status of any subject in time. Every person is witnessed of COVID-19 global pandemic viruses. The world population has faced this problem globally. Collecting the infected person’s sample data in a contact-based approach may lead to the spreading of the disease. On the other hand, if we use a non-contact-based approach for the collection, it is somehow far better and breaks the chain of virus spreading. This radar-based technique is preferred in non-contact vital sign detection so that any person gets to their health status prior and according to that doctor can diagnose the proper treatment. The radar-based signal is targeted to the subject’s chest. Due to the chest wall displacement main vital sign parameters of the heart and respiration of the individual’s health are being captured. These captured signals are called vital signs, with this it is very helpful that the pre-diagnosis and treatment can be recommended by doctors or health service providers. Some patients due to their movement may be older or children for a long-time use skin irritation or allergy type of problems may face. On the other hand, some patients may be COVID-19 infected disease and burn patients. Hence, it is not possible to connect as both cases are unexpected for the required purpose. For constant and continuous measurement, existing contact-based methods are not fruitful hence non-contact-based approach is adopted. Non-contact-based vital sign detection is preferably due to several problems occurring. This paper presents a state-of-the-art review of recent monitoring methods and techniques for health monitoring in medical fields of operations. These methods and techniques are used as a tool to acquire, visualize and analyze the sampled data collected in any environment either indoor or outdoor.</p>

Photonic radar for contactless vital sign detection

Vital sign detection is used across ubiquitous scenarios in medical and health settings, and contact and wearable sensors have been widely deployed. However, they are unsuitable for patients with burn wounds or infants with insufficient areas for attachment. Contactless detection can be achieved using camera imaging, but it is susceptible to ambient light conditions and has privacy concerns. Here we report a photonic radar for non-contact vital sign detection to overcome these challenges. This photonic radar can achieve millimetre-level range resolution based on synthesized radar signals with a bandwidth of up to 30 GHz. The high resolution of the radar system enables accurate respiratory detection from breathing simulators and a cane toad as a human proxy. Moreover, we demonstrate that the optical signals generated from the proposed system can enable vital sign detection based on light detection and ranging (LiDAR). This demonstration reveals the potential of a sensor-fusion architecture that can combine the complementary features of radar and LiDAR to achieve improved sensing accuracy and system resilience. The work provides a technical basis for contactless and high-resolution vital sign detection to meet the increasing demands of future medical and healthcare applications.

A Low-Complexity Non-Contact Vital Sign Detection System and Heartbeat Extraction Algorithm

A low-complexity vital sign detection system is proposed. The system uses a phase discriminator as the demodulation module and a phase shifter to solve the null problem in CW radar. This design significantly reduces the complexity. In the subsequent processing of this system, variational pattern decomposition (VMD) is used to analyze vital sign signals. Experiments using this system show that VMD is efficient and accurate in extracting human target respiration and heartbeat frequencies.

Vital Signs Detection With Difference Beamforming and Orthogonal Projection Filter Based on SIMO-FMCW Radar

Noncontact vital signs detection based on radar has attracted a lot of interest in recent years. Many efforts have been made to make this technology more complete. However, there still exist some challenges, such as vital signs detection of two closely positioned subjects and heart rate estimation with the interference of the high-order respiration harmonics. This article designs a single-input multiple-output (SIMO) frequency-modulated continuous-wave (FMCW) radar. Based on this system, difference beamforming and orthogonal projection filter (OPF) have been proposed to solve the two challenges. Difference beamforming is able to separate the vital signs of the two subjects even if the angular difference between the two subjects is smaller than the angular resolution of the array antenna. OPF can remove the high-order respiration harmonics without complex parameter adjustment operations and tedious calculations. The experimental results show that the proposed algorithm can successfully separate the vital signs of two closely positioned subjects and estimate the heart rate accurately with the interference of high-order respiration harmonics. The results also verify that the designed SIMO FMCW radar can separate the vital signs of subjects at different distances.

Non-Contact Detection of Vital Signs Based on Improved Adaptive EEMD Algorithm (July 2022).

Non-contact vital sign detection technology has brought a more comfortable experience to the detection process of human respiratory and heartbeat signals. Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method which can be used to decompose the echo data of frequency modulated continuous wave (FMCW) radar and extract the heartbeat and respiratory signals. The key of EEMD is to add Gaussian white noise into the signal to overcome the mode aliasing problem caused by original empirical mode decomposition (EMD). Based on the characteristics of clutter and noise distribution in public places, this paper proposed a static clutter filtering method for eliminating ambient clutter and an improved EEMD method based on stable alpha noise distribution. The symmetrical alpha stable distribution is used to replace Gaussian distribution, and the improved EEMD is used for the separation of respiratory and heartbeat signals. The experimental results show that the static clutter filtering technology can effectively filter the surrounding static clutter and highlight the periodic moving targets. Within the detection range of 0.5 m~2.5 m, the improved EEMD method can better distinguish the heartbeat, respiration, and their harmonics, and accurately estimate the heart rate.

A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar.

With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics, such as non-invasive, high penetration, accurate ranging, low power, and low cost, it makes the technology more suitable for non-contact vital signs detection. Therefore, a non-contact multi-human vital signs detection method based on IR-UWB radar is proposed in this paper. By using this technique, the realm of multi-target detection is opened up to even more targets for subjects than the more conventional single target. We used an optimized algorithm CIR-SS based on the channel impulse response (CIR) smoothing spline method to solve the problem that existing algorithms cannot effectively separate and extract respiratory and heartbeat signals. Also in our study, the effectiveness of the algorithm was analyzed using the Bland–Altman consistency analysis statistical method with the algorithm’s respiratory and heart rate estimation errors of 5.14% and 4.87%, respectively, indicating a high accuracy and precision. The experimental results showed that our proposed method provides a highly accurate, easy-to-implement, and highly robust solution in the field of non-contact multi-person vital signs detection.

Accurate Heart Rate and Respiration Rate Detection Based on a Higher-Order Harmonics Peak Selection Method Using Radar Non-Contact Sensors.

Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm.

걸어 다니는 사람의 생체신호탐지를 위한 움직임 보상기법 연구

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.

Radar detection of multi-target vital signs based on blind source separation

Radar non-contact monitoring of vital signs has a broad application prospect in clinical monitoring. Aiming at the problem of strong interference in non-contact vital signs detection (Such as multi-target, random body motion), a blind source separation (BSS) signal detection method based on Fast-ICA is proposed to reduce the interference of multi-target. In this algorithm, entropy is used to evaluate the non Gaussian property, and the appropriate transformation matrix is selected, according to the statistical independence of the signals, the source signals are separated from the observed mixed signals. On this basis, the traditional blind source separation process is improved, and the wavelet transform preprocessing algorithm based on translation invariant is added to suppress the interference of static clutter. The feasibility of this method is verified by simulation experiments.

A Non-Contact Vital Signs Detection in a Multi-Channel 77GHz LFMCW Radar System

The technology of vital signs detection has been proven of great use, whereas it is still limited by several challenges. One of the major challenges is the random body movements (RBMs), which significantly degrade the accuracy of the measurement. In this paper, a multi-channel 77GHz linear frequency modulated continuous-wave (LFMCW) radar system is investigated to perform vital signs monitoring on multiple targets with the mitigation of RBMs and a novel vital signs detection scheme is provided for the accurate estimates of the respiration rate (RR) and heart rate (HR). In the proposed scheme, a multi-channel Kalman smoother is firstly proposed to address the outliers in the extracted phase histories from the echoes in the multiple receivers, so that enhanced outlier-robust phase histories are acquired for the subsequent estimates of the RR and HR. Furthermore, a novel regional hidden Markov model is then proposed to carry out accurate estimates of RR and HR by exploiting the underlying slowly-varying characteristics of these vital signs for further mitigation of the effects of RBMs. Experimental results demonstrate that the estimated errors in the proposed scheme are less than 2 beats per minute (bpm) for both RR and HR under normal scenarios with young men in the RBMs environment.

A W-Band Integrated Tapered Array Antenna With Series Feed for Noncontact Vital Sign Detection

A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula> -band integrated tapered array antenna with series feed is presented. To reduce the fabrication cost, the antenna is designed on a single layer with the standard printed circuit board (PCB) process. In the array design, a novel coplanar waveguide (CPW) feeding structure is proposed to simplify the feeding network and fabrication by avoiding three-way power divider and via process. Considering the interconnection reliability and cost, the wire-bonding technique is used to connect the integrated circuit (IC) and antenna. To compensate for the parasitic effect of the bond wire and enhance the bandwidth of the antenna with interconnection, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> compensation structure is introduced and codesigned with the array antenna, achieving wideband-impedance feature. To demonstrate the validity of the design, several prototypes of the proposed antennas are implemented and measured. The measured results show that the array antenna with wire-bonding interconnection achieves a fractional impedance bandwidth ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\vert S_{11}\vert &lt; -10$ </tex-math></inline-formula> dB) of 13.2%, sidelobe level less than −15 dB at 100 GHz, and ~15 dBi gain. The simulated radiation efficiency is larger than 80% from 98 to 104 GHz. With our designed 100 GHz CMOS radar transceiver, the proposed antenna is used to detect the human vital sign.

Optimal Central Frequency for Non-Contact Vital Sign Detection Using Monocycle UWB Radar.

Ultra-wideband (UWB) radar has become a critical remote-sensing tool for non-contact vital sign detection such as emergency rescues, securities, and biomedicines. Theoretically, the magnitude of the received reflected signal is dependent on the central frequency of mono-pulse waveform used as the transmitted signal. The research is based on the hypothesis that the stronger the received reflected signals, the greater the detectability of life signals. In this paper, we derive a new formula to compute the optimal central frequency to obtain as maximum received reflect signal as possible over the frequency up to the lower range of Ka-band. The proposed formula can be applicable in the optimization of hardware for UWB life detection and non-contact monitoring of vital signs. Furthermore, the vital sign detection results obtained by the UWB radar over a range of central frequency have been compared to those of the former continuous (CW) radar to provide additional information regarding the advantages and disadvantages of each radar.

Differential Enhancement Method for Robust and Accurate Heart Rate Monitoring via Microwave Vital Sign Sensing

Noncontact vital sign detection based on microwave sensing has attracted much interest for its important application potential in many fields, especially in the field of healthcare. However, robust and accurate heart rate (HR) tracking remains a challenge due to the tricky and universal respiration harmonic interference in real scenarios. In this article, a novel method, called differential enhancement (DE), was proposed, which can effectively eliminate the effects of the respiration harmonic interference on HR estimation, including the likely adjacent harmonic interference. The basic idea underlying our proposed method is that the differential operation can significantly enhance the heartbeat components, especially high-order heartbeat harmonics, and it is very useful to locate the true value of HR by combining with the autocorrelation-based periodicity extraction technique. The detailed procedures for performing the DE method were described, which can be simply implemented and have low computational complexity. In addition, a rigorous mathematical analysis was provided for demonstrating the robustness and accuracy of the algorithm. Experimental results with various scenarios validated the performance of the proposed method, and we investigated the HR variability (HRV) monitoring capability of the DE method with a short sliding window length.