Accuracy Of Rate Measurements Research Articles

Validity, Accuracy, and Safety Assessment of an Aerobic Interval Training Using an App-Based Prehabilitation Program (PROTEGO MAXIMA Trial) Before Major Surgery: Prospective, Interventional Pilot Study.

Major surgery is associated with significant morbidity and a reduced quality of life, particularly among older adults and individuals with frailty and impaired functional capacity. Multimodal prehabilitation can enhance functional recovery after surgery and reduce postoperative complications. Digital prehabilitation has the potential to be a resource-sparing and patient-empowering tool that improves patients' preoperative status; however, little remains known regarding their safety and accuracy as medical devices. This study aims to test the accuracy and validity of a new software in comparison to the gold-standard electrocardiogram (ECG)-based heart rate measurement. The PROTEGO MAXIMA trial was a prospective interventional pilot trial assessing the validity, accuracy, and safety of an app-based exercise program. The Prehab App calculates a personalized, risk-stratified aerobic interval training plan based on individual risk factors and utilizes wearables to monitor heart rate. Healthy students and patients undergoing major surgery were enrolled. A structured risk assessment was conducted, followed by a 6-minute walking test and a 37-minute supervised interval session. During the exercise, patients wore app-linked wearables for heart rate and distance measurements, which were compared with standard ECG and treadmill measurements. Safety, accuracy, and usability assessments included testing alarm signals, while the occurrence of adverse events served as the primary and secondary outcome measures. A total of 75 participants were included. The mean heart rate differences between wearables and standard ECG were ≤5 bpm (beats per minute) with a mean absolute percentage error of ≤5%. Regression analysis revealed a significant impact of the BMI (odds ratio 0.90, 95% CI 0.82-0.98, P=.02) and Timed Up and Go Test score (odds ratio 0.12, 95% CI 0.03-0.55, P=.006) on the accuracy of heart rate measurement; 29 (39%) patients experienced adverse events: pain (5/12, 42%), ECG electrode-related skin irritations (2/42, 17%), dizziness (2/42, 17%), shortness of breath (2/42, 17%), and fatigue (1/42, 8%). No cardiovascular or serious adverse events were reported, and no serious device deficiency was detected. There were no indications of clinically meaningful overexertion based on laboratory values measured before and after the 6-minute walking test and exercise. The differences in means and ranges were as follows: lactate (mmol/l), mean 0.04 (range -3 to 6; P=.47); creatinine kinase (U/l), mean 12 (range -7 to 43; P<.001); and sodium (mmol/l), mean -2 (range -11 to 12; P<.001). The interventional trial demonstrated the high safety of the exercise program and the accuracy of heart rate measurements using commercial wearables in patients before major surgery, paving the way for potential remote implementation in the future. German Clinical Trials Register DRKS00026985; https://drks.de/search/en/trial/DRKS00026985 and European Database on Medical Devices (EUDAMED) CIV-21-07-0307311. RR2-10.1136/bmjopen-2022-069394.

Heat Stroke Warning System Prototype for Athletes: A Pilot Study.

This research has developed a heat stroke warning system prototype for athletes utilizing the following sensors: DHT22, GY-906-BAA MLX90614, MAX30102. The device calculates the heat stroke risk and notifies users. The data is recorded, stored, displayed on a free-access website which graphs body temperature, ambient temperature, humidity, heart rate and heat stroke risk, and provides notifications for athletes engaged in outdoor activities. The researchers recorded sensors data (n = 1) for two sessions (12 min/session) in a closed room, at the sixth-minute marker, with an air conditioner activated to observe the changes observed by the sensors. For accuracy, the researchers employed Criterion-Related Validity, comparing sensor against standard equipment measurement. For reliability, we utilized Test-Retest Reliability, comparing sensor data from the first and second measurements. Accuracy and reliability were evaluated using the Pearson Correlation Coefficient, with significance set at p < 0.01. The DHT22 sensor demonstrates very high accuracy (r = 0.923) in ambient temperature and (r = 0.774) humidity measurements. It showed no significant reliability (r = 0.489) in temperature and (r = 0.185) humidity measurements. The GY-906-BAA MLX90614 sensor exhibited very high accuracy (r = 0.923) and reliability (r = 0.866) in body temperature measurements. The MAX30102 sensor lacked significant accuracy (r = 0.179) and reliability (r = 0.171) in heart rate measurements. The development of accuracy and reliability of sensors are important for preventing heat stroke in future applications.

The rotation method for correcting renal depth in the determination of glomerular filtration rate using Tc-99m diethylenetriamine pentaacetic acid (DTPA)-based renal dynamic imaging in patients with hydronephrosis.

Kidney depth significantly affects the accuracy of glomerular filtration rate (GFR) measurement, and hydronephrosis-induced morphological changes further challenge its estimation through traditional formulas. This study evaluated the rotation method's efficacy in correcting kidney depth and depth difference during Tc-99m diethylenetriamine pentaacetic acid (DTPA) renal dynamic imaging for GFR assessment. This study analyzed 66 individuals treated at First Hospital of Shanxi Medical University with unilateral hydronephrosis between January 2022 and June 2023. Abdominal computed tomography (CT) scans were used to classify hydronephrosis severity into mild (20 cases), moderate (23 cases), and severe hydronephrosis groups (23 cases). Depth and depth differences of the kidneys were measured using CT, the rotation method, the Tonnesen formula, and the Li-Qian formula to evaluate their impact on total and single-kidney GFR. (I) Regarding renal depth and GFR, compared to CT, the Tonnesen formula underestimated both the depth and GFR for normal and hydronephrotic kidneys (NKs and HKs). The mean depth of normal kidneys (NKs) measured by the Tonnesen formula was 6.14 cm, approximately 19% lower than the 7.59 cm measured by CT. Similarly, the GFR of NKs estimated by the Tonnesen formula was 37.13 mL/min/1.73 m2, approximately 21% lower than the 47.24 mL/min/1.73 m2 measured by CT (P<0.05). The Li-Qian formula underestimated the renal depth and GFR for HKs. The mean depth of HKs measured by the Li-Qian formula was 7.62 cm, approximately 9% lower than the 8.41 cm measured by CT. Similarly, the GFR estimated by the Li-Qian formula was 25.50 mL/min/1.73 m2, about 19% lower than the 31.51 mL/min/1.73 m2 measured by CT (P<0.05). There were no statistically significant differences in the GFR or renal depth measurements between the rotation method and CT for both NKs and HKs (P>0.05). In HKs, the depth and GFR measured by the rotation method were 8.17 cm and 30.41 mL/min/1.73 m2, respectively, closely matching the CT measurements of 8.41 cm and 31.51 mL/min/1.73 m2. (II) A comparison of the renal depth and GFR in the mild, moderate, and severe hydronephrosis groups was conducted. Compared with CT, the Tonnesen formula undervalued renal depth and GFR across all severity levels (P<0.05); meanwhile, the Li-Qian formula underestimated the renal depth and GFR of the moderate and severe hydronephrosis groups (P<0.05). The rotation method demonstrated no variance across the three groups compared to CT (P>0.05). (III) In terms of depth difference, the Tonnesen and the Li-Qian formulae produced a significantly lower value than did CT (P<0.05). Statistical analyses showed no difference between the rotation and CT methods (P>0.05). In patients with hydronephrosis, the renal depth and depth difference measured by the rotation method are similar to those measured by CT, which can accurately correct the renal depth and depth difference without increasing the patient's additional radiation, enhancing the precision of total and separate renal GFR.

Assessment of Phase-Dependent Alterations in Cortical Glycolytic and Mitochondrial Metabolism Following Ischemic Stroke.

Maintaining optimal brain metabolism supports neuronal function, synaptic communication, and cognitive processes. During ischemic stroke, brain metabolism and cellular bioenergetics within the neurovascular unit are disrupted, emphasizing the significance of understanding the physiology and pathology of the stroke brain. The objective of this study was to quantify and compare phase-dependent changes in glycolysis and oxidative phosphorylation following ischemic stroke by using the Seahorse XFe24 Analyzer. Since there are limited established methods to quantify glycolytic activity in brain tissue, we optimized the accuracy and reproducibility of extracellular acidification rate (ECAR) measurement by increasing the incubation time following exposure to each reagent. Following optimization, we quantified both ECAR and the oxygen consumption rate (OCR), a measure of oxidative phosphorylation, in cortical brain tissue punches corresponding to the penumbra from mice subjected to ischemic stroke. ECAR and OCR were quantified in tissue punches from the injured (ipsilateral) and the non-injured (contralateral) hemispheres at 48 hours, 7 days, and 14 days post-stroke. Normalized ECAR measurements showed elevated glycolytic activity in the ipsilateral and contralateral hemispheres at 7 days post-stroke compared to other time points. In contrast, normalized OCR measurements showed a modest increase in basal respiration within the ipsilateral hemispheres between 48 hours and 14 days post-stroke. In summary, the results demonstrate that ischemic stroke results in a distinct phase-dependent metabolic phenotype in both cortical hemispheres that persists up to 14 days after injury.

Assessing the accuracy of star formation rate measurements by direct star count in molecular clouds

Star formation estimates based on the counting of young stellar objects (YSOs) are commonly carried out for nearby star-forming regions in the Galaxy, and in principle could be extended to any star-forming region where direct star counts are possible. With this method, the SFRs are measured using the counts of YSOs in a particular class, a typical mass, and the lifetime associated with this class. Another variant of this method is to use the total number of YSOs found in a star-forming region along with a characteristic YSO timescale. However, the assumptions underlying the validity of this method, such as that of a constant star formation history (SFH), have never been fully tested, and it remains unclear as to whether or not the method is valid for all protostellar classes. In this work, we use Monte Carlo models to test the validity and robustness of the method. We build synthetic clusters in which stars form at times that are randomly drawn from a specified SFH distribution function. The latter is either constant or time dependent, with a burst like behavior. The masses of the YSOs are randomly drawn from a stellar initial mass function (IMF), which can be either similar to that of the Milky Way field or be variable within the limits of the variations observed among young stellar clusters in the Galaxy. For each star in every cluster, the lifetimes associated with the different protostellar classes are also randomly drawn from Gaussian distribution functions centered around their most likely value as suggested by the observations. We find that only the SFR derived using the Class 0 population can reproduce the true SFR at all epochs, and this is true irrespective of the shape of the SFH. For a constant SFH, the SFR derived using the more evolved populations of YSOs (Class I, Class F, Class II, and Class III) reproduce the real SFR only at later epochs, which correspond to epochs at which their numbers have reached a steady state. For a time-dependent burst-like SFH, all SFR estimates based on the number counts of the evolved populations fail to reproduce the true SFR. We show that these conclusions are independent of the IMF. We argue that the SFR based on the Class 0 alone can yield reliable estimates of the SFR. We also show how the offsets between Class I- and Class II-based SFRs and the true SFR plotted as a function of the number ratios of Class I and Class II versus Class III YSOs can be used in order to provide information on the SFH of observed molecular clouds.

Enhancing Contactless Respiratory Rate Measurement Accuracy: Integration of 24GHz FMCW Radar and XGBoost Machine Learning

Advancements in non-contact vital sign monitoring are crucial for enhancing patient measurements' accuracy and overall patient experiences. This research explores the integration of 24GHz Frequency-Modulated Continuous-Wave (FMCW) radar with the XGBoost machine learning algorithm to improve the detection of respiratory rate (RR). This innovative approach offers a promising alternative to traditional contact-based methods. The study utilizes FMCW radar to detect respiratory motion, while signal patterns are analyzed using XGBoost to ensure accuracy across various healthcare environments. The method involves collecting signals, pre-processing to remove noise and irrelevant data, and extracting features to be analyzed by the XGBoost algorithm. The collected dataset, which includes controlled and randomized respiratory rates from a diverse subject pool, establishes a solid basis for the algorithm's training and validation, ensuring extensive adaptability and precision. Empirical results show that XGBoost surpasses other machine learning models' accuracy and reliability. Importantly, this method significantly reduces error margins compared to established benchmarks, leading to substantial improvements in RR measurement. The implications of this study are wide-ranging, indicating that such a system could significantly enhance patient care standards by providing continuous, accurate, and non-intrusive monitoring, especially in settings where traditional methods are impractical or uncomfortable. Future research should aim to refine the system's real-world applicability, assess long-term reliability, and optimize the technology for integration into existing healthcare frameworks, thereby further transforming the landscape of patient monitoring technologies.

Heart Rate Measurement Accuracy During Intermittent Efforts Under Laboratory Conditions: A Comparative Analysis Between Chest Straps and Armband

Heart rate (HR) is the most frequently used variable to monitor athletes’ internal load during training and competition. High-intensity effort and abrupt HR changes during exercise have presented measurement accuracy issues depending on the chosen device. Therefore, this study aimed to compare two chest straps (Garmin HRM-Dual and Coospo H6) and one armband (Coospo HW807) during intermittent exercise under controlled laboratory conditions. Thirty active young men performed an indoor cycling protocol consisting of seven intermittent efforts with a 2 min effort stage followed by a 2 min recovery stage. The results show no difference between the chest straps (Garmin vs. Coospo), with a high level of agreement between the two devices (Bias = −0.2 bpm, LoAup = +2.5 bpm, LoAlow = −2.9 bpm, ICC = 0.6–1.0). Differences were found between the chest straps and the armband during effort stages (±5 bpm, p &lt; 0.05), with similar bias and LoA values in the Garmin Strap vs. Coospo Armband (Bias = −0.5 bpm, LoAup = 8.3 bpm, LoAlow = −9.3 bpm) and Coospo Strap vs. Coospo Armband (Bias = −0.4 bpm, LoAup = 8.3 bpm, LoAlow = −9.0 bpm) comparison. Chest straps (Garmin HRM-Dual and Coospo H6) accurately measure HR during intermittent exercise with abrupt HR changes. However, caution should be taken when using armbands (Coospo HW807) to monitor intermittent and high-intensity effort.

Simulation of Fluid Flow through Orifice Meter

The modeling of fluid flow using an orifice meter is an engineering endeavor aimed at thoroughly investigating and optimizing fluid flow measurement in various industrial applications. Orifice meters are highly regarded for their accurate measurement of fluid flow rates as they pass through a specifically designed orifice plate, utilizing the principles of fluid dynamics. The primary objective of this project is to develop a computational fluid dynamics (CFD) model capable of simulating the intricate fluid flow through an orifice meter. The simulation encompasses detailed geometric features of the orifice meter, including the precise dimensions of the orifice plate, pipe diameter, and any relevant taper angles. Additionally, the model takes into account various fluid parameters, boundary conditions, and factors such as velocity and pressure at different points within the system. The comprehensive study visualizes and quantifies key fluid properties, such as velocity profiles, pressure differentials, and flow velocities, across the entire length of the orifice meter using CFD analysis. This in-depth analysis provides crucial insights into the dynamic performance of the orifice meter. The obtained information holds significant potential to revolutionize flow rate measurement accuracy, efficiency, and cost-effectiveness in diverse industries, including water management, chemical processing, and oil and gas. The project's methodology for simulating fluid flow through orifice meters can bring about substantial improvements in the understanding and optimization of fluid flow in industrial processes.

Modeling of the discharge coefcient of diferential pressure fowmeters: approximation by using radial-basis function neural networks

The discharge coefficient of flow transducers of liquids and gases of differential pressure flowmeters plays an important role in flow rate measurement. The problem of modeling and calculating the discharge coefficient of differential pressure flowmeters directly affects the accuracy of flow rate measurement of these devices. The results of modeling the discharge coefficient of the differential pressure flowmeter in the form of radial-basis neural networks are presented. The described structure of the neural network calculates the values of the discharge coefficient with an angular pressure tapping method. The article evaluates the error of approximation of the discharge coefficient by radial-basis function networks and provides recommendations for building such networks to solve problems of modeling the characteristics of differential pressure flowmeters. The article discusses the main advantages and disadvantages of using such networks as discharge coefficients of the differential pressure flowmeters. The research showed that the use of such networks is justified by their properties to approximate the discharge coefficient and their efficiency in measuring gas and liquid flow rates.

Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study.

Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations. We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting. Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA. All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001). Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories. RR2-10.3390/clockssleep6010010.

Feasibility study of the use of a wearable vital sign patch in an intensive care unit setting.

Multiple studies and review papers have concluded that early warning systems have a positive effect on clinical outcomes, patient safety and clinical performances. Despite the substantial evidence affirming the efficacy of EWS applications, persistent barriers hinder their seamless integration into clinical practice. Notably, EWS, such as the National Early Warning Score, simplify multifaceted clinical conditions into singular numerical indices, thereby risking the oversight of critical clinical indicators and nuanced fluctuations in patients' health status. Furthermore, the optimal deployment of EWS within clinical contexts remains elusive. Manual assessment of EWS parameters exacts a significant temporal toll on healthcare personnel. Addressing these impediments necessitates innovative approaches. In this regard, wearable medical technologies emerge as promising solutions capable of continual monitoring of hospitalized patients' vital signs. To overcome the barriers of the use of early warning scores, wearable medical technology has the potential to continuously monitor vital signs of hospitalised patients. However, a fundamental inquiry arises regarding the comparability of their reliability to the current used golden standards. This inquiry underscores the imperative for rigorous evaluation and validation of wearable medical technologies to ascertain their efficacy in augmenting extant clinical practices. This prospective, single-center study aimed to evaluate the accuracy of heart rate and respiratory rate measurements obtained from the Vivalink Cardiac patch in comparison to the ECG-based monitoring system utilized at AZ Maria Middelares Hospital in Ghent. Specifically, the study focused on assessing the concordance between the data obtained from the Vivalink Cardiac patch and the established ECG-based monitoring system among a cohort of ten post-surgical intensive care unit (ICU) patients. Of these patients, five were undergoing mechanical ventilation post-surgery, while the remaining five were not. The study proceeded by initially comparing the data recorded by the Vivalink Cardiac patch with that of the ECG-based monitoring system. Subsequently, the data obtained from both the Vivalink Cardiac patch and the ECG-based monitoring system were juxtaposed with the information derived from the ventilation machine, thereby providing a comprehensive analysis of the patch's performance in monitoring vital signs within the ICU setting. For heart rate, the Vivalink Cardiac patch was on average within a 5% error range of the ECG-based monitoring system during 85.11±10.81% of the measured time. For respiratory rate this was during 40.55±17.28% of the measured time. Spearman's correlation coefficient showed a very high correlation of 8 for heart rate and a moderate correlation of for respiratory rate. In comparison with the ventilated respiratory rate (ventilation machine) the Vivalink and ECG-based monitoring system both had a moderate correlation of A very high correlation was found between the heart rate measured by the Vivalink Cardiac patch and that of the ECG-based monitoring system of the hospital. Concerning respiratory rate the correlation between the data from the Vivalink Cardiac patch, the ECG-based monitoring system and the ventilation machine was found to be moderate.

Research on the flow characteristics of injecting hydrogen into natural gas pipelines and its impact on energy metering

Research on the flow characteristics of injecting hydrogen into natural gas pipelines and its impact on energy metering

Illumination-robust Camera-PPG via Skin-Guided Auto-Exposure.

In case of varying lighting conditions, especially in frontal lighting and backlighting, current remote photoplethysmography (rPPG) methods face many challenges. This study proposes a novel strategy, called Skin-Guided Auto-Exposure (Skin-AE) that significantly enhances the quality of rPPG signal extraction under these adverse lighting conditions, by using facial skin intensity to control the camera exposure setting in real-time. The Skin-AE utilizes a Proportional-Integral-Derivative (PID) algorithm to dynamically adjust the exposure time according to the average brightness of the facial region. This approach allows for adaptive optimization of facial brightness in real-time, significantly improving the performance of rPPG extraction in frontal lighting and backlighting conditions. The results indicate that Skin-AE improved the accuracy of heart rate measurement to 94.25%, significantly higher than 57.96% obtained by the Global-AE algorithm of an industrial camera (IDS). This demonstrates the effectiveness of the Skin-AE method in measuring rPPG signals.

A closed-loop micropump and flow meter for high-precision drug delivery in an implantable neural probe.

In this study, a closed-loop micropump system and flow meter with a proportional-integral-derivative (PID) controller is introduced to improve the accuracy of flow rate measurement and drug delivery. The micropumps are fabricated using Polydimethylsiloxane (PDMS) and have a drug reservoir capacity of 7µL. A capacitance-based flow meter is employed as the feedback mechanism for the PID controller. The flow rate range investigated is between 15 to 45 pL/min, with a resolution of 1.2 pF per 100 pL/min. The implementation of the PID controller enhances the accuracy of drug delivery by up to 27%.

Radiation Safety Assessment in Prostate Cancer Treatment: A Predictive Approach for I-125 Brachytherapy.

This study uses Monte Carlo simulation and experimental measurements to develop a predictive model for estimating the external dose rate associated with permanent radioactive source implantation in prostate cancer patients. The objective is to estimate the accuracy of the patient's external dose rate measurement. First, I-125 radioactive sources were implanted into Mylar window water phantoms to simulate the permanent implantation of these sources in patients. Water phantom experimental measurement was combined with Monte Carlo simulation to develop predictive equations, whose performance was verified against external clinical data. The model's accuracy in predicting the external dose rate in patients with permanently implanted I-125 radioactive sources was high (R2 = 0.999). A comparative analysis of the experimental measurements and the Monte Carlo simulations revealed that the maximum discrepancy between the measured and calculated values for the water phantom was less than 5.00%. The model is practical for radiation safety assessments, enabling the evaluation of radiation exposure risks to individuals around patients with permanently implanted I-125 radioactive sources.

Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.

Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology.

The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents.

A Preliminary Investigation about the Influence of WIMU PROTM Location on Heart Rate Accuracy: A Comparative Study in Cycle Ergometer.

Technological development has boosted the use of multi-sensor devices to monitor athletes' performance, but the location and connectivity between devices have been shown to affect data reliability. This preliminary study aimed to determine whether the placement of a multi-sensor device (WIMU PROTM) could affect the heart rate signal reception (GARMINTM chest strap) and, therefore, data accuracy. Thirty-two physical education students (20 men and 12 women) performed 20 min of exercise in a cycle ergometer based on the warm-up of the Function Threshold Power 20 test in laboratory conditions, carrying two WIMU PROTM devices (Back: inter-scapula; Bicycle: bicycle's handlebar-20 cm from the chest) and two GARMINTM chest straps. A one-dimensional statistical parametric mapping test found full agreement between the two situations (inter-scapula vs. bicycle's handlebar). Excellent intra-class correlation values were obtained during the warm-up (ICC = 0.99, [1.00-1.00], p < 0.001), the time trial test (ICC = 0.99, [1.00-1.00], p < 0.001) and the cool-down (ICC = 0.99, [1.00-1.00], p < 0.001). The Bland-Altman plots confirmed the total agreement with a bias value of 0.00 ± 0.1 bpm. The interscapular back placement of the WIMU PROTM device does not affect heart rate measurement accuracy with a GARMINTM chest strap during cycling exercise in laboratory conditions.

Industrial design of wearable intelligent devices based on wireless networks

Industrial design of wearable intelligent devices based on wireless networks

Application of invariance theory methods for measuring the flow rate of natural gas with excess hydrogen sulfide content

Application of invariance theory methods for measuring the flow rate of natural gas with excess hydrogen sulfide content