scholarly journals Experimental Comparison of IR-UWB Radar and FMCW Radar for Vital Signs

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6695
Author(s):  
Dingyang Wang ◽  
Sungwon Yoo ◽  
Sung Ho Cho
Keyword(s):  
Continuous Wave ◽  
Vital Signs ◽  
Ultra Wideband ◽  
Experimental Comparison ◽  
Distance Information ◽  
Mimo Antenna ◽  
Fmcw Radar ◽  
Uwb Radar ◽  
Sign Detection ◽  
Detection Capabilities

In this paper, we compare the performances of impulse radio ultra-wideband (IR-UWB) and frequency modulation continuous wave (FMCW) radars in measuring noncontact vital signs such as respiration rate and heart rate. These two type radars have been widely used in various fields and have shown their applicability to extract vital signs in noncontact ways. IR-UWB radar can extract vital signs using distance information. On the other hand, FMCW radar requires phase information to estimate vital signs, and the result can be enhanced with Multi-input Multi-output (MIMO) antenna topologies. By using commercial radar chipsets, the operation of radars under different conditions and frequency bands will also affect the performance of vital sign detection capabilities. We compared the accuracy and signal-to-noise (SNR) ratios of IR-UWB and FMCW radars in various scenarios, such as distance, orientation, carotid pulse, harmonics, and obstacle penetration. In general, the IR-UWB radars offer a slightly better accuracy and higher SNR in comparison to FMCW radar. However, each radar system has its own unique advantages, with IR-UWB exhibiting fewer harmonics and a higher SNR, while FMCW can combine the results from each channel.

scholarly journals Adaptive Separation of Respiratory and Heartbeat Signals among Multiple People Based on Empirical Wavelet Transform Using UWB Radar

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4913 ◽  
Author(s):  
Mi He ◽  
Yongjian Nian ◽  
Luping Xu ◽  
Lihong Qiao ◽  
Wenwu Wang
Keyword(s):  
Wavelet Transform ◽  
Continuous Wave ◽  
Body Movement ◽  
Vital Signs ◽  
Initial Boundary ◽  
Tight Frame ◽  
Ultra Wideband ◽  
Clinical Monitoring ◽  
Uwb Radar ◽  
Empirical Wavelet Transform

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.

scholarly journals Applications of TVF-EMD in Vital Signal Detection for UWB Radar

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zhen Yang ◽  
Chi Ma ◽  
Qingjie Qi ◽  
Xin Li ◽  
Yan Li
Keyword(s):  
Vital Signs ◽  
Measured Data ◽  
Ultra Wideband ◽  
Continuous Wavelet ◽  
Uwb Radar ◽  
Detection Technology ◽  
Sign Detection ◽  
Wideband Radar ◽  
Echo Radar ◽  
Emd Method

When using pulsed ultra-wideband radar (UWB) noncontact detection technology to detect vital signs, weak vital signs echo signals are often covered by various noises, making human targets unable to identify and locate. To solve this problem, a new method for vital sign detection is proposed which is based on impulse ultra-wideband (UWB) radar. The range is determined based on the continuous wavelet transform (CWT) of the variance of the received signals. In addition, the TVF-EMD method is used to obtain the information of respiration and heartbeat frequency. Fifteen sets of experiments were carried out, and the echo radar signals of 5 volunteers at 3 different distances were collected. The analysis results of the measured data showed that the proposed algorithm can accurately and effectively extract the distance to the target human and its vital signs information, which shows vast prospects in research and application.

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

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2916 ◽  
Author(s):  
Artit Rittiplang ◽  
Pattarapong Phasukkit ◽  
Teerapong Orankitanun
Keyword(s):  
Vital Sign ◽  
Vital Signs ◽  
Ultra Wideband ◽  
Central Frequency ◽  
Additional Information ◽  
Advantages And Disadvantages ◽  
Uwb Radar ◽  
Sign Detection ◽  
Life Detection ◽  
New Formula

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.

scholarly journals A Novel Method for Breath Detection via Stepped-Frequency Continuous Wave Ultra-Wideband (SFCW UWB) Radars Based on Operational Bandwidth Segmentation

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3873 ◽  
Author(s):  
Hao Lv ◽  
Teng Jiao ◽  
Yang Zhang ◽  
Fulai Liang ◽  
Fugui Qi ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Doppler Radar ◽  
Ultra Wideband ◽  
Time Dimension ◽  
Processing Scheme ◽  
Practical Applications ◽  
Uwb Radar ◽  
Operational Frequency ◽  
Stepped Frequency ◽  
Breath Detection

Human being detection via ultra-wideband (UWB) radars has shown great prospects in many areas, such as biomedicine, military operation, public security, emergency rescue, and so on. When a person stays stationary, the main feature that separates him/her from surroundings is the movement of chest wall due to breath. There have been many algorithms developed for breath detection while using UWB radars. However, those algorithms were almost based on a basic scheme that focused on processing in the time dimension of UWB data. They did not utilize the benefits from the wide operational bandwidth of UWB radars to show potential superiority over those narrowband systems such as a continuous wave (CW) Doppler radar. In this paper, a breath detection method was proposed based on operational bandwidth segmentation. A basic theoretical model was firstly introduced, indicating that characteristics of breath signals contained in UWB echoes were consistent among the operational frequencies, while those of clutters were not. So, the method divided a set of UWB echo data into a number of subsets, each of which corresponded to a sub-band within the operational bandwidth of the UWB radar. Thus information about the operational frequency is provided for subsequent processing. With the aid of the information, a breath enhancement algorithm was developed mainly by averaging the segmented UWB data along the operational frequency. The algorithm’s performance was verified by data measured by a stepped-frequency CW (SFCW) UWB radar. The experimental results showed that the algorithm performed better than that without the segmentation. They also showed its feasibility for fast detection of breath based on a short duration of data. Moreover, the method’s potential for target identification and impulse-radio (IR) UWB radar was investigated. In summary, the method provides a new processing scheme for UWB radars when they are used for breath detection. With this scheme, the UWB radars have a benefit of greater flexibility in data processing over those narrowband radars, and thus will perform more effectively and efficiently in practical applications.

scholarly journals FPGA Implementation of an Efficient FFT Processor for FMCW Radar Signal Processing

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6443
Author(s):  
Jinmoo Heo ◽  
Yongchul Jung ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Signal Processing ◽  
Detection Rate ◽  
Continuous Wave ◽  
Vital Signs ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Fmcw Radar ◽  
Design And Implementation ◽  
Fft Processor ◽  
Point Operator

This paper presents the design and implementation results of an efficient fast Fourier transform (FFT) processor for frequency-modulated continuous wave (FMCW) radar signal processing. The proposed FFT processor is designed with a memory-based FFT architecture and supports variable lengths from 64 to 4096. Moreover, it is designed with a floating-point operator to prevent the performance degradation of fixed-point operators. FMCW radar signal processing requires windowing operations to increase the target detection rate by reducing clutter side lobes, magnitude calculation operations based on the FFT results to detect the target, and accumulation operations to improve the detection performance of the target. In addition, in some applications such as the measurement of vital signs, the phase of the FFT result has to be calculated. In general, only the FFT is implemented in the hardware, and the other FMCW radar signal processing is performed in the software. The proposed FFT processor implements not only the FFT, but also windowing, accumulation, and magnitude/phase calculations in the hardware. Therefore, compared with a processor implementing only the FFT, the proposed FFT processor uses 1.69 times the hardware resources but achieves an execution time 7.32 times shorter.

scholarly journals Matrix Pencil Method for Vital Sign Detection from Signals Acquired by Microwave Sensors

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5735
Author(s):  
Somayyeh Chamaani ◽  
Alireza Akbarpour ◽  
Marko Helbig ◽  
Jürgen Sachs
Keyword(s):  
Vital Sign ◽  
Continuous Wave ◽  
Vital Signs ◽  
Matrix Pencil ◽  
Ultra Wideband ◽  
Frequency Resolution ◽  
High Temporal Resolution ◽  
Matrix Pencil Method ◽  
Mode Decomposition ◽  
Microwave Sensors

Microwave sensors have recently been introduced as high-temporal resolution sensors, which could be used in the contactless monitoring of artery pulsation and breathing. However, accurate and efficient signal processing methods are still required. In this paper, the matrix pencil method (MPM), as an efficient method with good frequency resolution, is applied to back-reflected microwave signals to extract vital signs. It is shown that decomposing of the signal to its damping exponentials fulfilled by MPM gives the opportunity to separate signals, e.g., breathing and heartbeat, with high precision. A publicly online dataset (GUARDIAN), obtained by a continuous wave microwave sensor, is applied to evaluate the performance of MPM. Two methods of bandpass filtering (BPF) and variational mode decomposition (VMD) are also implemented. In addition to the GUARDIAN dataset, these methods are also applied to signals acquired by an ultra-wideband (UWB) sensor. It is concluded that when the vital sign is sufficiently strong and pure, all methods, e.g., MPM, VMD, and BPF, are appropriate for vital sign monitoring. However, in noisy cases, MPM has better performance. Therefore, for non-contact microwave vital sign monitoring, which is usually subject to noisy situations, MPM is a powerful method.

scholarly journals Contactless Simultaneous Breathing and Heart Rate Detections in Physical Activity Using IR-UWB Radars

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5503
Author(s):  
Xinyue Zhang ◽  
Xiuzhu Yang ◽  
Yi Ding ◽  
Yili Wang ◽  
Jialin Zhou ◽  
...  
Keyword(s):  
Physical Activity ◽  
Vital Signs ◽  
Motion Artifacts ◽  
Body Motion ◽  
Ultra Wideband ◽  
Body Movements ◽  
Range Resolution ◽  
Vital Signs Monitoring ◽  
Uwb Radar ◽  
Empirical Wavelet Transform

Vital signs monitoring in physical activity (PA) is of great significance in daily healthcare. Impulse Radio Ultra-WideBand (IR-UWB) radar provides a contactless vital signs detection approach with advantages in range resolution and penetration. Several researches have verified the feasibility of IR-UWB radar monitoring when the target keeps still. However, various body movements are induced by PA, which lead to severe signal distortion and interfere vital signs extraction. To address this challenge, a novel joint chest–abdomen cardiopulmonary signal estimation approach is proposed to detect breath and heartbeat simultaneously using IR-UWB radars. The movements of target chest and abdomen are detected by two IR-UWB radars, respectively. Considering the signal overlapping of vital signs and body motion artifacts, Empirical Wavelet Transform (EWT) is applied on received radar signals to remove clutter and mitigate movement interference. Moreover, improved EWT with frequency segmentation refinement is applied on each radar to decompose vital signals of target chest and abdomen to vital sign-related sub-signals, respectively. After that, based on the thoracoabdominal movement correlation, cross-correlation functions are calculated among chest and abdomen sub-signals to estimate breath and heartbeat. The experiments are conducted under three kinds of PA situations and two general body movements, the results of which indicate the effectiveness and superiority of the proposed approach.

scholarly journals Vital Sign Monitoring Using FMCW Radar in Various Sleeping Scenarios

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6505
Author(s):  
Emmi Turppa ◽  
Juha M. Kortelainen ◽  
Oleg Antropov ◽  
Tero Kiuru
Keyword(s):  
Heart Rate ◽  
Remote Monitoring ◽  
Vital Sign ◽  
Continuous Wave ◽  
Real Life ◽  
Vital Signs ◽  
Absolute Error ◽  
Physical Contact ◽  
Radar Signal ◽  
Fmcw Radar

Remote monitoring of vital signs for studying sleep is a user-friendly alternative to monitoring with sensors attached to the skin. For instance, remote monitoring can allow unconstrained movement during sleep, whereas detectors requiring a physical contact may detach and interrupt the measurement and affect sleep itself. This study evaluates the performance of a cost-effective frequency modulated continuous wave (FMCW) radar in remote monitoring of heart rate and respiration in scenarios resembling a set of normal and abnormal physiological conditions during sleep. We evaluate the vital signs of ten subjects in different lying positions during various tasks. Specifically, we aim for a broad range of both heart and respiration rates to replicate various real-life scenarios and to test the robustness of the selected vital sign extraction methods consisting of fast Fourier transform based cepstral and autocorrelation analyses. As compared to the reference signals obtained using Embla titanium, a certified medical device, we achieved an overall relative mean absolute error of 3.6% (86% correlation) and 9.1% (91% correlation) for the heart rate and respiration rate, respectively. Our results promote radar-based clinical monitoring by showing that the proposed radar technology and signal processing methods accurately capture even such alarming vital signs as minimal respiration. Furthermore, we show that common parameters for heart rate variability can also be accurately extracted from the radar signal, enabling further sleep analyses.

scholarly journals Developing of Algorithms Monitoring Heartbeat and Respiration Rate of a Seated Person with an FMCW Radar

2021 ◽  
Vol 19 ◽  
pp. 195-206
Author(s):  
Lorenz J. Dirksmeyer ◽  
Aly Marnach ◽  
Daniel Schmiech ◽  
Andreas R. Diewald
Keyword(s):  
Respiration Rate ◽  
Continuous Wave ◽  
Frequency Estimation ◽  
Wavelet Packet ◽  
Vital Signs ◽  
Wave Mode ◽  
Fine Tuning ◽  
Fmcw Radar ◽  
Real Time Visualization ◽  
Spectral Frequency

Abstract. With a radar working in the 24 GHz ISM-band in a frequency modulated continuous wave mode the major vital signs heartbeat and respiration rate are monitored. The observation is hereby contactless with the patient sitting straight up in a distance of 1–2 m to the radar. Radar and sampling platform are components developed internally in the university institution. The communication with the radar is handled with MATLAB via TCP/IP. The signal processing and real-time visualization is developed in MATLAB, too. Cornerstone of this publication are the wavelet packet transformation and a spectral frequency estimation for vital sign calculation. The wavelet transformation allows a fine tuning of frequency subspaces, separating the heartbeat signal from the respiration and more important from noise and other movement. Heartbeat and respiration are monitored independently and compared to parallel recorded ECG-data.

scholarly journals Indoor Activity and Vital Sign Monitoring for Moving People with Multiple Radar Data Fusion

2021 ◽  
Vol 13 (18) ◽  
pp. 3791
Author(s):  
Xiuzhu Yang ◽  
Xinyue Zhang ◽  
Yi Ding ◽  
Lin Zhang
Keyword(s):  
Data Fusion ◽  
Vital Sign ◽  
Continuous Wave ◽  
Short Term Memory ◽  
Wavelet Packet ◽  
Vital Signs ◽  
Radar Data ◽  
Activity Monitoring ◽  
Ultra Wideband ◽  
Additional Information

The monitoring of human activity and vital signs plays a significant role in remote health-care. Radar provides a non-contact monitoring approach without privacy and illumination concerns. However, multiple people in a narrow indoor environment bring dense multipaths for activity monitoring, and the received vital sign signals are heavily distorted with body movements. This paper proposes a framework based on Frequency Modulated Continuous Wave (FMCW) and Impulse Radio Ultra-Wideband (IR-UWB) radars to address these challenges, designing intelligent spatial-temporal information fusion for activity and vital sign monitoring. First, a local binary pattern (LBP) and energy features are extracted from FMCW radar, combined with the wavelet packet transform (WPT) features on IR-UWB radar for activity monitoring. Then the additional information guided fusing network (A-FuseNet) is proposed with a modified generative and adversarial structure for vital sign monitoring. A Cascaded Convolutional Neural Network (CCNN) module and a Long Short Term Memory (LSTM) module are designed as the fusion sub-network for vital sign information extraction and multisensory data fusion, while a discrimination sub-network is constructed to optimize the fused heartbeat signal. In addition, the activity and movement characteristics are introduced as additional information to guide the fusion and optimization. A multi-radar dataset with an FMCW and two IR-UWB radars in a cotton tent, a small room and a wide lobby is constructed, and the accuracies of activity and vital sign monitoring achieve 99.9% and 92.3% respectively. Experimental results demonstrate the superiority and robustness of the proposed framework.

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