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

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

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. No conventional Doppler only can capture Doppler signatures because of a lack of bandwidth information with noncontact sensors. In contrast, we take full advantages 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 continuous-wavelet transform (CWT) are proposed jointly to improve the signal-to-noise ratio (SNR) in order to acquire accurate respiration and heartbeat rates. This noncontact healthcare sensor system proves the commercial feasibility and considerable accessibility of using compact IR-UWB radar for emerging biomedical applications. Compared with traditional contact measurement devices, experimental results utilizing a 2.3 GHz bandwidth transceiver, demonstrate 100% similar results.

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):  
Short Range ◽  
Doppler Radar ◽  
Vital Signs ◽  
Ensemble Empirical Mode Decomposition ◽  
The Body ◽  
Ultra Wideband ◽  
Detection Range ◽  
Wall Movement ◽  
Mode Decomposition ◽  
The Subject

The designed radar sensor realizes the healthcare monitoring capable of short-range to detect the chest-wall movement of the subject caused by cardiopulmonary activities, and wirelessly estimating the distance from the sensor to the subject without any devices being attached to the body. Ensemble empirical mode decomposition (EEMD) based denoise method and 1-D continuous-wavelet transform (CWT) are applied for improving on the detection SNR so that accurate respiration rate and heartbeat rate can be acquired in time domain or frequency domain with further distance. No choosing the conventional Doppler radar only able to capture the Doppler signatures due to the lack of bandwidth information as noncontact sensor, we take full advantages of ultra-wideband (UWB) impulse radar to make it low power consumed and portable conveniently, with flexible detection range and preferable accuracy. This noncontact healthcare sensor system addressed proves the commercial feasibility and vast availability of using compact impulse radar for emerging biomedical applications. Compared with traditional contact measurement devices, experimental results utilizing the 2.3 GHz bandwidth transceiver, demonstrate 100% similar results.

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 A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 597 ◽  
Author(s):  
Guohui Li ◽  
Zhichao Yang ◽  
Hong Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Measurement Technology ◽  
Signal Denoising ◽  
Frequency Noise ◽  
Denoising Method ◽  
Radiated Noise ◽  
Mode Decomposition ◽  
Adaptive Noise

Due to the non-linear and non-stationary characteristics of ship radiated noise (SR-N) signal, the traditional linear and frequency-domain denoising methods cannot be used for such signals. In this paper, an SR-N signal denoising method based on modified complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN), dispersion entropy (DE), and interval thresholding is proposed. The proposed denoising method has the following advantages: (1) as an improved version of CEEMDAN, modified CEEMDAN (MCEEMDAN) combines the advantages of EMD and CEEMDAN, and it is more reliable than CEEMDAN and has less consuming time; (2) as a fast complexity measurement technology, DE can effectively identify the type of intrinsic mode function (IMF); and (3) interval thresholding is used for SR-N signal denoising, which avoids loss of amplitude information compared with traditional denoising methods. Firstly, the original signal is decomposed into a series of IMFs using MCEEMDAN. According to the DE value of IMF, the modes are divided into three types: noise IMF, noise-dominated IMF and pure IMF. After noise IMFs are removed, the noise-dominated IMFs are denoised using interval thresholding. Finally, the pure IMF and the processed noise-dominated IMFs are reconstructed to obtain the final denoised signal. The denoising experiments with the Chen’s chaotic system show that the proposed method has a higher signal-to-noise ratio (SNR) than the other three methods. Applying the proposed method to denoise the real SR-N signal, the topological structure of chaotic attractor can be recovered clearly. It is proved that the proposed method can effectively suppress the high-frequency noise of SR-N signal.

scholarly journals A Novel Signal Separation and De-Noising Technique for Doppler Radar Vital Signal Detection

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4751 ◽  
Author(s):  
Xiaoling Li ◽  
Bin Liu ◽  
Yang Liu ◽  
Jiawei Li ◽  
Jiarui Lai ◽  
...  
Keyword(s):  
Signal Detection ◽  
Spectrum Analysis ◽  
Doppler Radar ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Signal Separation ◽  
Radar Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Adaptive Noise

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.

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 Comprehensive Improvement of the Sensitivity and Detectability of a Large-Aperture Electromagnetic Wear Particle Detector

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3162 ◽  
Author(s):  
Ran Jia ◽  
Biao Ma ◽  
Changsong Zheng ◽  
Xin Ba ◽  
Liyong Wang ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Wear Particle ◽  
Impedance Change ◽  
Particle Detector ◽  
Particle Detection ◽  
Detection Range ◽  
Micro Particles ◽  
Mode Decomposition ◽  
Flux Change ◽  
Lock In

The electromagnetic wear particle detector has been widely studied due to its prospective applications in various fields. In order to meet the requirements of the high-precision wear particle detector, a comprehensive method of improving the sensitivity and detectability of the sensor is proposed. Based on the nature of the sensor, parallel resonant exciting coils are used to increase the impedance change of the exciting circuit caused by particles, and the serial resonant topology structure and an amorphous core are applied to the inductive coil, which improves the magnetic flux change of the inductive coil and enlarges the induced electromotive force of the sensor. Moreover, the influences of the resonance frequency on the sensitivity and effective particle detection range of the sensor are studied, which forms the basis for optimizing the frequency of the magnetic field within the sensor. For further improving the detectability of micro-particles and the real-time monitoring ability of the sensor, a simple and quick extraction method for the particle signal, based on a modified lock-in amplifier and empirical mode decomposition and reverse reconstruction (EMD-RRC), is proposed, which can effectively extract the particle signal from the raw signal with low signal-to-noise ratio (SNR). The simulation and experimental results show that the proposed methods improve the sensitivity of the sensor by more than six times.

BREATHING PATTERN RECOGNITION OF ABDOMINAL WALL MOVEMENT BY USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION

2014 ◽  
Vol 06 (01) ◽  
pp. 1450002 ◽  
Author(s):  
YA-CHEN CHEN ◽  
TZU-CHIEN HSIAO ◽  
JU-HSIN HSU ◽  
JIN-LONG CHEN
Keyword(s):  
Empirical Mode Decomposition ◽  
Breathing Pattern ◽  
Recognition Rate ◽  
Ensemble Empirical Mode Decomposition ◽  
Feature Extraction Method ◽  
Statistical Representation ◽  
Wall Movement ◽  
Mode Decomposition ◽  
Abdominal Breathing ◽  
Physiological Benefits

Thoracic breathing (TB), abdominal breathing (AB), and mixing breathing are common respiratory functions. Individuals usually breathe thoracically, whereas the breathing pattern of AB is vague. Despite the statistical representation of the physiological benefits of AB, coping with a time-variant environment still remains challenging. Therefore, based on ensemble empirical mode decomposition (EEMD), this study compares the identification types of using R value, power proportion, and modified significant test (MST). Respiratory maneuver of 26 subjects results that MST varied with a paced breathing frequency is the highest accurate recognition rate of TB (80.8% in 0.2 Hz and 88.5% in 0.1 Hz) and of AB (73.1% in 0.2 and 0.1 Hz). Results of this study demonstrate that EEMD is an adaptive algorithm to decompose respiratory movement. Furthermore, MST is a highly promising feature extraction method for breathing type recognition.

scholarly journals Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3133
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Ryan Wen Liu ◽  
Pandian Vasant
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Ensemble Empirical Mode Decomposition ◽  
Wavelet Packet Transform ◽  
Smart Meters ◽  
Model Decomposition ◽  
Mode Decomposition ◽  
Data Heterogeneity ◽  
Electricity Load ◽  
Load Disaggregation

Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD–WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD–WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.

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