Terahertz-Wave-Plethysmography (TPG): A New Principle of Radar Based Pulse Detection

Abstract This study presents findings at Terahertz (THz) frequency band for non-contact cardiac sensing application. For the first time, cardiac pulse information is simultaneously extracted using THz waves based on the two established principles in electronics and optics. The first fundamental principle is micro-Doppler (mD) motion effect, initially introduced in coherent laser radar system 1, 2 and first experimentally demonstrated for vital sign detection 3. This motion based method, primarily using coherent phase information from the radar receiver, has been widely exploited in microwave frequency bands and has recently found popularity in millimeter waves (mmWave). The second fundamental principle is reflectance based optical measurement using infrared or visible light. The variation in the light reflection is proportional to the volumetric change of the heart, often referred as photoplethysmography (PPG). PPG has been a popular technology for pulse diagnosis. Recently it has been widely incorporated into various smart wearables for long-term monitoring, such fitness training and sleep monitoring. Herein, the concept of Terahertz-Wave-Plethysmography (TPG) is introduced, which detects blood volume changes in the upper dermis tissue layer by measuring the reflectance of THz waves, similar to the existing remote PPG (rPPG) principle 4. The TPG principle is justified by scientific deduction, electromagnetic wave (EM) simulations and carefully designed experimental demonstrations. Additionally, pulse measurements from various peripheral body parts of interest (BOI), palm, inner elbow, temple, fingertip and forehead, are demonstrated using a wideband THz sensing system developed by Terahertz Electronics Lab at Arizona State University (ASU), Tempe. Among the BOIs under test, it is found that the measurements from forehead BOI gives the best accuracy with mean heart rate (HR) estimation error 1.51 beats per minute (BPM) and stand deviation (std) 1.08 BPM. The results validate the feasibility of radar based plethysmography for direct pulse monitoring. Finally, a comparative study on pulse sensitivity in TPG and rPPG is conducted. The results indicate that the TPG contains more pulsatile from the forehead BOI than that in the rPPG signals and thus generate better heart rate (HR) estimation statistic in the form of empirical cumulative distribution function (CDF) of HR estimation error.

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Gradient based empirical cumulative distribution function approximation for robust aerodynamic design

Aerospace Science and Technology ◽

10.1016/j.ast.2021.106630 ◽

2021 ◽

pp. 106630

Author(s):

Elisa Morales ◽

Andrea Bornaccioni ◽

Domenico Quagliarella ◽

Renato Tognaccini

Keyword(s):

Distribution Function ◽

Function Approximation ◽

Cumulative Distribution Function ◽

Cumulative Distribution ◽

Aerodynamic Design ◽

Empirical Cumulative Distribution Function ◽

Gradient Based

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Contactless Video-Based Heart Rate Monitoring of a Resting and an Anesthetized Pig

Animals ◽

10.3390/ani11020442 ◽

2021 ◽

Vol 11 (2) ◽

pp. 442

Author(s):

Meiqing Wang ◽

Ali Youssef ◽

Mona Larsen ◽

Jean-Loup Rault ◽

Daniel Berckmans ◽

...

Keyword(s):

Heart Rate ◽

Estimation Error ◽

Well Being ◽

Absolute Error ◽

Mean Value ◽

Video Data ◽

Window Functions ◽

The Face ◽

Short Time ◽

Better Than

Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism’s state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm (PE3.5). The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in PE3.5. The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.

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Evaluation of the Performance of CMIP6 Models in Reproducing Rainfall Patterns over North Africa

Atmosphere ◽

10.3390/atmos12040475 ◽

2021 ◽

Vol 12 (4) ◽

pp. 475

Author(s):

Hassen Babaousmail ◽

Rongtao Hou ◽

Brian Ayugi ◽

Moses Ojara ◽

Hamida Ngoma ◽

...

Keyword(s):

North Africa ◽

Rainfall Variability ◽

Coupled Model ◽

Cumulative Distribution ◽

Climatic Research Unit ◽

Model Ensemble ◽

Empirical Cumulative Distribution Function ◽

Cycle Simulation ◽

Taylor Diagram ◽

Statistical Metrics

This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) are used as proxy to observational datasets to examine the capability of 15 CMIP6 models’ and their ensemble in simulating rainfall during 1951–2014. In addition, robust statistical metrics, empirical cumulative distribution function (ECDF), Taylor diagram (TD), and Taylor skill score (TSS) are utilized to assess models’ performance in reproducing annual and seasonal and monthly rainfall over the study domain. Results show that CMIP6 models satisfactorily reproduce mean annual climatology of dry/wet months. However, some models show a slight over/under estimation across dry/wet months. The models’ overall top ranking from all the performance analyses ranging from mean cycle simulation, trend analysis, inter-annual variability, ECDFs, and statistical metrics are as follows: EC-Earth3-Veg, UKESM1-0-LL, GFDL-CM4, NorESM2-LM, IPSL-CM6A-LR, and GFDL-ESM4. The mean model ensemble outperformed the individual CMIP6 models resulting in a TSS ratio (0.79). For future impact studies over the study domain, it is advisable to employ the multi-model ensemble of the best performing models.

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RF Vital Sign Sensing under Free Body Movement

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3478090 ◽

2021 ◽

Vol 5 (3) ◽

pp. 1-22

Author(s):

Jian Gong ◽

Xinyu Zhang ◽

Kaixin Lin ◽

Ju Ren ◽

Yaoxue Zhang ◽

...

Keyword(s):

Vital Sign ◽

Estimation Error ◽

Body Movement ◽

Vital Signs ◽

Motion Artifacts ◽

Body Parts ◽

Training Scheme ◽

Pattern Movement ◽

Free Body ◽

Average Estimation Error

Radio frequency (RF) sensors such as radar are instrumental for continuous, contactless sensing of vital signs, especially heart rate (HR) and respiration rate (RR). However, decades of related research mainly focused on static subjects, because the motion artifacts from other body parts may easily overwhelm the weak reflections from vital signs. This paper marks a first step in enabling RF vital sign sensing under ambulant daily living conditions. Our solution is inspired by existing physiological research that revealed the correlation between vital signs and body movement. Specifically, we propose to combine direct RF sensing for static instances and indirect vital sign prediction based on movement power estimation. We design customized machine learning models to capture the sophisticated correlation between RF signal pattern, movement power, and vital signs. We further design an instant calibration and adaptive training scheme to enable cross-subjects generalization, without any explicit data labeling from unknown subjects. We prototype and evaluate the framework using a commodity radar sensor. Under a variety of moving conditions, our solution demonstrates an average estimation error of 5.57 bpm for HR and 3.32 bpm for RR across multiple subjects, which largely outperforms state-of-the-art systems.

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Brain Networks Maintain a Scale-free Organization across Consciousness, Anesthesia, and Recovery

Anesthesiology ◽

10.1097/aln.0b013e3181f229b5 ◽

2010 ◽

Vol 113 (5) ◽

pp. 1081-1091 ◽

Cited By ~ 56

Author(s):

UnCheol Lee ◽

GabJin Oh ◽

Seunghwan Kim ◽

GyuJung Noh ◽

ByungMoon Choi ◽

...

Keyword(s):

General Anesthesia ◽

Minimum Spanning Tree ◽

Essential Feature ◽

Fundamental Principle ◽

Temporal Organization ◽

Cumulative Distribution ◽

Connection Matrix ◽

Scale Free ◽

States Of Consciousness ◽

Network Connections

Background Loss of consciousness is an essential feature of general anesthesia. Although alterations of neural networks during anesthesia have been identified in the spatial domain, there has been relatively little study of temporal organization. Methods Ten healthy male volunteers were anesthetized with an induction dose of propofol on two separate occasions. The duration of network connections in the brain was analyzed by multichannel electroencephalography and the minimum spanning tree method. Entropy of the connections was calculated based on Shannon entropy. The global temporal configuration of networks was investigated by constructing the cumulative distribution function of connection times in different frequency bands and different states of consciousness. Results General anesthesia was associated with a significant reduction in the number of network connections, as well as significant alterations of their duration. These changes were most prominent in the δ bandwidth and were also associated with a significant reduction in entropy of the connection matrix. Despite these and other changes, a global "scale-free" organization was consistently preserved across multiple subjects, anesthetic exposures, states of consciousness, and electroencephalogram frequencies. Conclusions Our data suggest a fundamental principle of temporal organization of network connectivity that is maintained during consciousness and anesthesia, despite local changes. These findings are consistent with a process of adaptive reconfiguration during general anesthesia.

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Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data

Remote Sensing ◽

10.3390/rs13214243 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4243

Author(s):

Mona Morsy ◽

Ruhollah Taghizadeh-Mehrjardi ◽

Silas Michaelides ◽

Thomas Scholten ◽

Peter Dietrich ◽

...

Keyword(s):

Arid Regions ◽

Flash Flood ◽

Primary Source ◽

Remote Sensing Data ◽

Test Site ◽

Rain Gauge ◽

Cumulative Distribution ◽

Climatic Data ◽

Empirical Cumulative Distribution Function ◽

Rain Gauges

Water depletion is a growing problem in the world’s arid and semi-arid areas, where groundwater is the primary source of fresh water. Accurate climatic data must be obtained to protect municipal water budgets. Unfortunately, the majority of these arid regions have a sparsely distributed number of rain gauges, which reduces the reliability of the spatio-temporal fields generated. The current research proposes a series of measures to address the problem of data scarcity, in particular regarding in-situ measurements of precipitation. Once the issue of improving the network of ground precipitation measurements is settled, this may pave the way for much-needed hydrological research on topics such as the spatiotemporal distribution of precipitation, flash flood prevention, and soil erosion reduction. In this study, a k-means cluster analysis is used to determine new locations for the rain gauge network at the Eastern side of the Gulf of Suez in Sinai. The clustering procedure adopted is based on integrating a digital elevation model obtained from The Shuttle Radar Topography Mission (SRTM 90 × 90 m) and Integrated Multi-Satellite Retrievals for GPM (IMERG) for four rainy events. This procedure enabled the determination of the potential centroids for three different cluster sizes (3, 6, and 9). Subsequently, each number was tested using the Empirical Cumulative Distribution Function (ECDF) in an effort to determine the optimal one. However, all the tested centroids exhibited gaps in covering the whole range of elevations and precipitation of the test site. The nine centroids with the five existing rain gauges were used as a basis to calculate the error kriging. This procedure enabled decreasing the error by increasing the number of the proposed gauges. The resulting points were tested again by ECDF and this confirmed the optimum of thirty-one suggested additional gauges in covering the whole range of elevations and precipitation records at the study site.

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Man, Machine, and Homeostasis

Just Enough Physiology ◽

10.1093/med/9780199797790.003.0018 ◽

2011 ◽

pp. 141-148

Author(s):

James R. Munis

Keyword(s):

Blood Pressure ◽

Heart Rate ◽

Nervous System ◽

Autonomic Nervous System ◽

Fundamental Principle ◽

The Body ◽

Set Point ◽

Autonomic Nervous ◽

Claude Bernard ◽

Shed Light

Physiologist Claude Bernard lived in a time when very little was known about the mechanisms underlying physiologic findings, and he had ample access to clues garnered from observing machines. Let's consider homeostasis (a concept championed by Bernard), an example for which an engineered machine shed light on a fundamental principle of physiology. Homeostasis is simply the tendency of the body to maintain important physiologic variables (eg, heart rate, blood pressure, PACO2) at constant, preset values. An example is a simplified mechanical governor that could be used to regulate the rotational speed of a steam engine shaft. ‘Autoregulate’ might be a more apt word because the governor performs without external help or guidance, provided it is designed and built properly. It doesn't take much imagination to see an analogy between the mechanical governor and the autonomic nervous system. Both maintain specific variables at a constant set point through a process of feedback loops.

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Results of the Verification of the Statistical Distribution Model of Microseismicity Emission Characteristics

Archives of Mining Sciences ◽

10.1515/amsc-2016-0035 ◽

2016 ◽

Vol 61 (3) ◽

pp. 489-496

Author(s):

Aleksander Cianciara

Keyword(s):

Distribution Function ◽

Weibull Distribution ◽

Goodness Of Fit ◽

Cumulative Distribution Function ◽

Statistical Distribution ◽

Cumulative Distribution ◽

Distribution Model ◽

Emission Characteristics ◽

Goodness Of Fit Test ◽

Empirical Cumulative Distribution Function

Abstract The paper presents the results of research aimed at verifying the hypothesis that the Weibull distribution is an appropriate statistical distribution model of microseismicity emission characteristics, namely: energy of phenomena and inter-event time. It is understood that the emission under consideration is induced by the natural rock mass fracturing. Because the recorded emission contain noise, therefore, it is subjected to an appropriate filtering. The study has been conducted using the method of statistical verification of null hypothesis that the Weibull distribution fits the empirical cumulative distribution function. As the model describing the cumulative distribution function is given in an analytical form, its verification may be performed using the Kolmogorov-Smirnov goodness-of-fit test. Interpretations by means of probabilistic methods require specifying the correct model describing the statistical distribution of data. Because in these methods measurement data are not used directly, but their statistical distributions, e.g., in the method based on the hazard analysis, or in that that uses maximum value statistics.

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