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 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 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 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 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 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.

scholarly journals Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar

2016 ◽  
Author(s):  
Xikun Hu ◽  
Tian Jin
Keyword(s):  
Doppler Radar ◽  
Signal To Noise Ratio ◽  
Vital Signs ◽  
Ensemble Empirical Mode Decomposition ◽  
The Body ◽  
Ultra Wideband ◽  
Detection Range ◽  
Wall Movement ◽  
Uwb Radar ◽  
Mode Decomposition

The radar sensor described realizes healthcare monitoring capable of detecting subject chest-wall movement caused by cardiopulmonary activities, and wirelessly estimating the respiration and heartbeat rates of the subject without attaching any devices to the body. Conventional single-tone Doppler radar can only capture Doppler signatures because of a lack of bandwidth information with noncontact sensors. In contrast, we take full advantage of impulse radio ultra-wideband (IR-UWB) radar to achieve low power consumption and convenient portability, with a flexible detection range and desirable accuracy. A noise reduction method based on improved ensemble empirical mode decomposition (EEMD) and a vital sign separation method based on the continuous-wavelet transform (CWT) are proposed jointly to improve the signal-to-noise ratio (SNR) in order to acquire accurate respiration and heartbeat rates. Experimental results illustrate that respiration and heartbeat signals can be extracted accurately under different conditions. This noncontact healthcare sensor system proves the commercial feasibility and considerable accessibility of using compact IR-UWB radar for emerging biomedical applications.

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

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 83
Author(s):  
Hongqiang Xu ◽  
Malikeh P. Ebrahim ◽  
Kareeb Hasan ◽  
Fatemeh Heydari ◽  
Paul Howley ◽  
...  
Keyword(s):  
Heart Rate ◽  
Signal Processing ◽  
Respiration Rate ◽  
Body Position ◽  
Random Noise ◽  
Vital Signs ◽  
Ultra Wideband ◽  
Spectrum Estimation ◽  
Respiration Activity ◽  
Uwb Radar

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.

scholarly journals A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yangyang Ma ◽  
Pengfei Wang ◽  
Wenzhe Huang ◽  
Fugui Qi ◽  
Fulai Liang ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
Vital Signs ◽  
Ultra Wideband ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Signal Source ◽  
Vital Signs Monitoring ◽  
Uwb Radar ◽  
Svm Model ◽  
Feature Based

AbstractPets have been indispensable members for many families in modern life, especially significant for the elderly and the blind. However, they may cause false alarm when misused as signal source in non-contact monitoring of the vital signs using ultra-wideband (UWB) radar. Distinguishing between humans and pets can help ensure the correct signal source. Nevertheless, existing solutions are few or only utilize a single feature, which can hinder robustness and accuracy because of individual differences. In this study, we proposed a robust multi-feature based method to solve the problem. First, 19 discriminative features were extracted to reflect differences in aspects of energy, frequency, wavelet entropy, and correlation coefficient. Second, the features were ranked by recursive feature elimination algorithm and the top eight were then selected to build an optimal support vector machine (SVM) model. The area under the receiver operating characteristic curve (AUC) of the optimal SVM model reached 0.9620. The false and missing alarms for identifying humans were 0.0962 and 0.0600, respectively. Finally, comparison with the state-of-the-art method that only employed one feature validated the advance and accuracy of the proposed method. The method is envisioned to facilitate the UWB radar applications in non-contact and continuous vital signs monitoring.

scholarly journals Through-Wall Multi-Subject Localization and Vital Signs Monitoring Using UWB MIMO Imaging Radar

2021 ◽  
Vol 13 (15) ◽  
pp. 2905
Author(s):  
Zhi Li ◽  
Tian Jin ◽  
Yongpeng Dai ◽  
Yongkun Song
Keyword(s):  
Continuous Wave ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Vital Signs ◽  
Low Frequency ◽  
Mimo Radar ◽  
Ultra Wideband ◽  
Radar Images ◽  
Imaging Radar ◽  
Vital Signs Monitoring

Radar-based non-contact vital signs monitoring has great value in through-wall detection applications. This paper presents the theoretical and experimental study of through-wall respiration and heartbeat pattern extraction from multiple subjects. To detect the vital signs of multiple subjects, we employ a low-frequency ultra-wideband (UWB) multiple-input multiple-output (MIMO) imaging radar and derive the relationship between radar images and vibrations caused by human cardiopulmonary movements. The derivation indicates that MIMO radar imaging with the stepped-frequency continuous-wave (SFCW) improves the signal-to-noise ratio (SNR) critically by the factor of radar channel number times frequency number compared with continuous-wave (CW) Doppler radars. We also apply the three-dimensional (3-D) higher-order cumulant (HOC) to locate multiple subjects and extract the phase sequence of the radar images as the vital signs signal. To monitor the cardiopulmonary activities, we further exploit the VMD algorithm with a proposed grouping criterion to adaptively separate the respiration and heartbeat patterns. A series of experiments have validated the localization and detection of multiple subjects behind a wall. The VMD algorithm is suitable for separating the weaker heartbeat pattern from the stronger respiration pattern by the grouping criterion. Moreover, the continuous monitoring of heart rate (HR) by the MIMO radar in real scenarios shows a strong consistency with the reference electrocardiogram (ECG).

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