scholarly journals HEAR: Approach for Heartbeat Monitoring with Body Movement Compensation by IR-UWB Radar

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3077 ◽  
Author(s):  
Wenfeng Yin ◽  
Xiuzhu Yang ◽  
Lei Li ◽  
Lin Zhang ◽  
Nattapong Kitsuwan ◽  
...  
Keyword(s):  
Heart Rate ◽  
Body Movement ◽  
Large Body ◽  
Ultra Wideband ◽  
Heart Rate Variability Analysis ◽  
Mean Deviation ◽  
Fast Time ◽  
Heart Rates ◽  
Mode Decomposition ◽  
Wideband Radar

Further applications of impulse radio ultra-wideband radar in mobile health are hindered by the difficulty in extracting such vital signals as heartbeats from moving targets. Although the empirical mode decomposition based method is applied in recovering waveforms of heartbeats and estimating heart rates, the instantaneous heart rate is not achievable. This paper proposes a Heartbeat Estimation And Recovery (HEAR) approach to expand the application to mobile scenarios and extract instantaneous heartbeats. Firstly, the HEAR approach acquires vital signals by mapping maximum echo amplitudes to the fast time delay and compensating large body movements. Secondly, HEAR adopts the variational nonlinear chirp mode decomposition in extracting instantaneous frequencies of heartbeats. Thirdly, HEAR extends the clutter removal method based on the wavelet decomposition with a two-parameter exponential threshold. Compared to heart rates simultaneously collected by electrocardiograms (ECG), HEAR achieves a minimum error rate 4.6% in moving state and 2.25% in resting state. The Bland–Altman analysis verifies the consistency of beat-to-beat intervals in ECG and extracted heartbeat signals with the mean deviation smaller than 0.1 s. It indicates that HEAR is practical in offering clinical diagnoses such as the heart rate variability analysis in mobile monitoring.

scholarly journals Non-Contact Vital Signs Monitoring of Dog and Cat Using a UWB Radar

Animals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 205 ◽  
Author(s):  
Pengfei Wang ◽  
Yangyang Ma ◽  
Fulai Liang ◽  
Yang Zhang ◽  
Xiao Yu ◽  
...  
Keyword(s):  
Heart Rate ◽  
Health Assessment ◽  
Pressure Sensors ◽  
Vital Signs ◽  
Noise Removal ◽  
Ultra Wideband ◽  
Response To Treatment ◽  
Vital Signs Monitoring ◽  
Mode Decomposition ◽  
Wideband Radar

As pets are considered members of the family, their health has received widespread attention. Since pets cannot talk and complain when they feel uncomfortable, monitoring vital signs becomes very helpful in disease detection, as well as observing their progression and response to treatment. In this study, we proposed an ultra-wideband radar-based, non-contact animal vital sign monitoring scheme that could monitor the breathing and heart rate of a pet in real-time. The primary advantage of the ultra-wideband radar was its ability to operate remotely without electrodes or wires and through any clothing or fur. Because of the existing noise and clutter in non-contact detection, background noise removal was applied. Furthermore, the respiration rate was directly obtained through spectrum analysis, while the heartbeat signal was extracted by the variational mode decomposition algorithm. By using electrocardiogram measurements, we verified the accuracy of the radar technology in detecting the anesthetized animals’ respiratory rate and heart rate. Besides, three beagles and five cats in a non-sedated state were measured by radar and contact pressure sensors simultaneously; the experimental results showed that radar could effectively measure the respiration of cats and dogs, and the accuracy rate was over 95%. Due to its excellent performance, the proposed method has the potential to become a new choice in application scenarios, such as pet sleep monitoring and health assessment.

scholarly journals Extreme bradycardia and tachycardia in the world’s largest animal

2019 ◽  
Vol 116 (50) ◽  
pp. 25329-25332 ◽  
Author(s):  
J. A. Goldbogen ◽  
D. E. Cade ◽  
J. Calambokidis ◽  
M. F. Czapanskiy ◽  
J. Fahlbusch ◽  
...  
Keyword(s):  
Heart Rate ◽  
Large Diameter ◽  
Large Body ◽  
Fold Increase ◽  
Oxygen Depletion ◽  
Blue Whale ◽  
Heart Rates ◽  
Physiological Limits ◽  
Low Mass ◽  
Lunge Feeding

The biology of the blue whale has long fascinated physiologists because of the animal’s extreme size. Despite high energetic demands from a large body, low mass-specific metabolic rates are likely powered by low heart rates. Diving bradycardia should slow blood oxygen depletion and enhance dive time available for foraging at depth. However, blue whales exhibit a high-cost feeding mechanism, lunge feeding, whereby large volumes of prey-laden water are intermittently engulfed and filtered during dives. This paradox of such a large, slowly beating heart and the high cost of lunge feeding represents a unique test of our understanding of cardiac function, hemodynamics, and physiological limits to body size. Here, we used an electrocardiogram (ECG)-depth recorder tag to measure blue whale heart rates during foraging dives as deep as 184 m and as long as 16.5 min. Heart rates during dives were typically 4 to 8 beats min−1 (bpm) and as low as 2 bpm, while after-dive surface heart rates were 25 to 37 bpm, near the estimated maximum heart rate possible. Despite extreme bradycardia, we recorded a 2.5-fold increase above diving heart rate minima during the powered ascent phase of feeding lunges followed by a gradual decrease of heart rate during the prolonged glide as engulfed water is filtered. These heart rate dynamics explain the unique hemodynamic design in rorqual whales consisting of a large-diameter, highly compliant, elastic aortic arch that allows the aorta to accommodate blood ejected by the heart and maintain blood flow during the long and variable pauses between heartbeats.

scholarly journals Fast time series detrending with applications to heart rate variability analysis

2019 ◽  
Vol 30 (09) ◽  
pp. 1950069
Author(s):  
M. Andrecut
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Diffusion Equation ◽  
Time Series Analysis ◽  
Heart Rate Variability Analysis ◽  
Fast Time ◽  
Variability Analysis ◽  
Long Time ◽  
Hrv Analysis

In this paper, we discuss a new fast detrending method for the nonstationary RR time series used in Heart Rate Variability (HRV) analysis. The described method is based on the diffusion equation, and we show numerically that it is equivalent to the widely used Smoothing Priors Approach (SPA) and Wavelet Smoothing Approach (WSA) methods. The speed of the proposed method is comparable to the WSA method and it is several orders of magnitude faster than the SPA method, which makes it suitable for very long time series analysis.

scholarly journals Motion-Tolerant Non-Contact Heart-Rate Measurements from Radar Sensor Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1774
Author(s):  
Yu Rong ◽  
Arindam Dutta ◽  
Alex Chiriyath ◽  
Daniel W. Bliss
Keyword(s):  
Heart Rate ◽  
Vital Sign ◽  
Human Subjects ◽  
Body Movement ◽  
Processing Technique ◽  
Ultra Wideband ◽  
Signal Processing Technique ◽  
Active Motion ◽  
Microwave Radar ◽  
Radar Measurements

Microwave radar technology is very attractive for ubiquitous short-range health monitoring due to its non-contact, see-through, privacy-preserving and safe features compared to the competing remote technologies such as optics. The possibility of radar-based approaches for breathing and cardiac sensing was demonstrated a few decades ago. However, investigation regarding the robustness of radar-based vital-sign monitoring (VSM) is not available in the current radar literature. In this paper, we aim to close this gap by presenting an extensive experimental study of vital-sign radar approach. We consider diversity in test subjects, fitness levels, poses/postures, and, more importantly, random body movement (RBM) in the study. We discuss some new insights that lead to robust radar heart-rate (HR) measurements. A novel active motion cancellation signal-processing technique is introduced, exploiting dual ultra-wideband (UWB) radar system for motion-tolerant HR measurements. Additionally, we propose a spectral pruning routine to enhance HR estimation performance. We validate the proposed method theoretically and experimentally. Totally, we record and analyze about 3500 seconds of radar measurements from multiple human subjects.

A COVID-19 Non-contact Screening System Based on XGBoost and Logistic Regression (Preprint)

2021 ◽  
Author(s):  
Chunheng Shang ◽  
Yixian Qiao ◽  
Xiwen Liao ◽  
Xiaoning Yuan ◽  
Qin Cheng ◽  
...  
Keyword(s):  
Heart Rate ◽  
Logistic Regression ◽  
Body Movement ◽  
Recall Rate ◽  
Machine Learning Algorithms ◽  
Ultra Wideband ◽  
Monitoring Data ◽  
Screening System ◽  
Contact Screening ◽  
Radar Monitoring

BACKGROUND COVID-19 is a new infectious disease with high infectivity. At present, body temperature detection is the main method for primary screening, but this single detection method has poor accuracy and is easy to miss detection. OBJECTIVE The objective of our study was to propose a non-contact, high-precision COVID-19 screening system. METHODS We used impulse-radio ultra-wideband (IR-UWB) radar to detect the respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital, and compared them with 144 radar monitoring data of healthy controls. Then XGBoost and logistic regression(XGBoost+LR) algorithm was used to classify the data of patients and healthy people; feature selection was performed by SHAP value; using ten-fold cross-validation, XGBoost+LR algorithm was compared with five other classic classification algorithms, and the classification performance was evaluated by precision, recall, and the area under the ROC curve( AUC ). RESULTS The XGBoost+LR algorithm demonstrate excellent discrimination (precision=99.1 %, recall rate = 94.1 %, AUC=98.7 %), which is superior to several other single machine learning algorithms. In addition, the SHAP value indicate that number of apnea during REM(‘ REMSATims’) and mean heart rate(‘meanHR’) are important features for classification. CONCLUSIONS The COVID-19 non-contact screening system based on XGBoost+LR algorithm can accurately predict COVID-19 patients and can be applied in isolation wards to effectively help medical staff.

scholarly journals Person-Specific Heart Rate Estimation With Ultra-Wideband Radar Using Convolutional Neural Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 168484-168494 ◽  
Author(s):  
Shuqiong Wu ◽  
Takuya Sakamoto ◽  
Kentaro Oishi ◽  
Toru Sato ◽  
Kenichi Inoue ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Convolutional Neural Networks ◽  
Ultra Wideband ◽  
Rate Estimation ◽  
Wideband Radar ◽  
Heart Rate Estimation
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