The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

A new objective method for determining exercise gas exchange thresholds by respiratory frequency in middle-aged men

PURPOSE

To evaluate the agreement between the two Gas Exchange Thresholds (GETs = GET1 and GET2), identified by the conventional V-Slope method, and two Respiratory Frequency Thresholds (fRTs = fRT1 and fRT2) obtained from a novel, low-cost, and simple method of breakpoint determination.

METHODS

Fifty middle-aged males (age: 50-58 years; V ˙ o2peak: 37.5 ± 8.6 mL·Kg-1·min-1), either healthy or with chronic illnesses, underwent an incremental cycle exercise test to determine maximal oxygen uptake ( V ˙ o2max/ V ˙ o2peak), GETs and fRTs.

RESULTS

There were no statistical differences [P > 0.05; ES: 0.17 to 0.32, small] between absolute and relative (56-60% V ˙ o2peak) oxygen uptake ( V ˙ o2) values at GET1 with those obtained at fRT1, nor between V ˙ o2 values at GET2 with those at fRT2 (76-78% V ˙ o2peak). Heart rate (HR) at fRT1, and V ˙ o2 and HR at fRT2 showed very large correlations (r = 0.75-0.82; P < 0.001) and acceptable precision (SEE < 7-9%) in determination of their corresponding values at GET1 and GET2. The precision in the estimation of V ˙ o2 at GET1 from fRT1 was moderate (SEE = 15%), while those of power output at GET1 (SEE = 23%) and GET2 (SEE = 12%) from their corresponding fRTs values were very poor to moderate.

CONCLUSION

HR at fRT1 and V ˙ o2 and HR at fRT2, determined using a new objective and portable approach, may potentially serve as viable predictors of their respective GETs. This method may offer a simplified, cost-effective, and field-based approach for determining exercise threshold intensities during graded exercise.

Cardiorespiratory DB: Collection of cardiorespiratory data acquired during normal breathing, deep breathing and breath holding

The database is constituted by 50 datasets containing cardiorespiratory signals acquired from 50 healthy volunteer subjects (one dataset for each subject; 23 males and 27 females; age: 23±5 years) while performing normal breathing, deep breathing, and breath holding, and two spreadsheet files, namely the "SubjectsInfo.xlsx" and "DBInfo.xlsx" containing the metadata of subjects (including demographic data) and of acquired signals, respectively. Cardiorespiratory signals consisted in simultaneously recorded 12-lead electrocardiograms acquired by the clinical M12 Global InstrumentationⓇ digital Holter ECG recorder, and single-lead electrocardiograms and respiration signals acquired by the wearable chest strap BioHarness 3.0 by Zephyr. The database may be useful to: (1) validate the use of wearable sensors in the acquisition of cardiorespiratory data during different respiration kinds, including apnea; (2) investigate the physiological association between cardiovascular and respiratory systems; (3) validate algorithms able to indirectly extract the respiration signal from the electrocardiogram; (4) study the fatigue level induced by a series of controlled respiration patterns; and (5) investigate the effect of COVID-19 infection on the cardiorespiratory system.

Is breathing frequency a potential means for monitoring exercise intensity in people with atrial fibrillation and coronary heart disease when heart rate is mitigated?

PURPOSE

Moderate-intensity aerobic exercise is safe and beneficial in atrial fibrillation (AF) and coronary heart disease (CHD). Irregular or rapid heart rates (HR) in AF and other heart conditions create a challenge to using HR to monitor exercise intensity. The purpose of this study was to assess the potential of breathing frequency (BF) to monitor exercise intensity in people with AF and CHD without AF.

METHODS

This observational study included 30 AF participants (19 Male, 70.7 ± 8.7 yrs) and 67 non-AF CHD participants (38 Male, 56.9 ± 11.4 yrs). All performed an incremental maximal exercise test with pulmonary gas exchange.

RESULTS

Peak aerobic power in AF ( V ˙ O2peak; 17.8 ± 5.0 ml.kg-1.min-1) was lower than in CHD (26.7 ml.kg-1.min-1) (p < .001). BF responses in AF and CHD were similar (BF peak: AF 34.6 ± 5.4 and CHD 36.5 ± 5.0 breaths.min-1; p = .106); at the 1st ventilatory threshold (BF@VT-1: AF 23.2 ± 4.6; CHD 22.4 ± 4.6 breaths.min-1; p = .240). % V ˙ O2peak at VT-1 were similar in AF and CHD (AF: 59%; CHD: 57%; p = .656).

CONCLUSION

With the use of wearable technologies on the rise, that now include BF, this first study provides an encouraging potential for BF to be used in AF and CHD. As the supporting data are based on incremental ramp protocol results, further research is required to assess BF validity to manage exercise intensity during longer bouts of exercise.

Prediction of Death in Intracerebral Hemorrhage Patients After Minimally Invasive Surgery by Vital Signs and Blood Glucose

OBJECTIVE

This study examined the impact of vital signs and blood glucose levels on the long-term prognosis of intracerebral hemorrhage (ICH) patients treated with minimally invasive surgery (MIS).

METHODS

The patients diagnosed with ICH and treated with MIS within 24 hours of admission at the ∗∗ Hospital between January 2020 and October 2021 were included. The relationship between a range of indicators, including vital signs, blood glucose levels, and patient mortality at discharge and 3 or 12 months postdischarge were analyzed.

RESULTS

A total of 195 consecutive patients were included, of which 16 patients passed away during hospitalization, 29 and 34 within 3 and 12 months postdischarge, respectively. The multivariate analysis revealed that hospital death positively correlated with age ≥66.50 years, fasting blood glucose ≥8.25 mmol/L on the third day after MIS, systolic blood pressure ≥166.00 mmHg on the third day, and heart rate ≥89.50 beats/min at discharge (area under the curve [AUC] = 0.927). Death at 3 months positively correlated with male sex, blood glucose before dinner ≥8.15 mmol/L on the second day after MIS, body temperature ≥36.95°C at discharge, and heart rate ≥89.50 beats/minute at discharge (AUC = 0.810). Death at 12 months positively correlated with age ≥61.50 years, body temperature ≥36.95°C at discharge, and heart rate ≥92.50 beats/min on the third day after MIS (AUC = 0.824).

CONCLUSIONS

The prognosis of ICH patients after MIS is closely related to their vital signs and blood glucose levels at various stages of hospitalization.

Influence of Respiratory Frequency of Slow-Paced Breathing on Vagally-Mediated Heart Rate Variability

Breathing techniques, particularly slow-paced breathing (SPB), have gained popularity among athletes due to their potential to enhance performance by increasing cardiac vagal activity (CVA), which in turn can help manage stress and regulate emotions. However, it is still unclear whether the frequency of SPB affects its effectiveness in increasing CVA. Therefore, this study aimed to investigate the effects of a brief SPB intervention (i.e., 5 min) on CVA using heart rate variability (HRV) measurement as an index. A total of 75 athletes (22 female; Mage = 22.32; age range = 19-31) participated in the study, attending one lab session where they performed six breathing exercises, including SPB at different frequencies (5 cycles per minute (cpm), 5.5 cpm, 6 cpm, 6.5 cpm, 7 cpm), and a control condition of spontaneous breathing. The study found that CVA was significantly higher in all SPB conditions compared to the control condition, as indexed by both root mean square of the successive differences (RMSSD) and low-frequency HRV (LF-HRVms2). Interestingly, LF-HRVms2 was more sensitive in differentiating the respiratory frequencies than RMSSD. These results suggest that SPB at a range of 5 cpm to 7 cpm can be an effective method to increase CVA and potentially improve stress management and emotion regulation in athletes. This short SPB exercise can be a simple yet useful tool for athletes to use during competitive scenarios and short breaks in competitions. Overall, these findings highlight the potential benefits of incorporating SPB into athletes' training and competition routines.

The use of successive systolic differences in photoplethysmographic (PPG) signals for respiratory rate estimation

Most PPG-based methods for extracting the respiratory rate (RR) rely on changes in the PPG signal's amplitude, baseline, or frequency. However, several other parameters may provide more valuable information for accurate RR computation. In this study, we explored the capabilities of the respiratory-induced variations in successive systolic differences (RISSDV) of PPG signals to estimate RR. We partitioned fifty-three publicly available recordings into eight 1-min segments and identified peaks and troughs of the PPG signals to quantify respiratory-induced variations in amplitude (RIAV), baseline (RIIV), frequency (RIFV), and peak-to-peak amplitude differences (RISSDV). RR values were extracted by determining the peak frequency of the power spectral density of the four variations and the reference respiratory signal. We assessed each feature's performance by computing the root-mean-squared (RMSE) and mean absolute errors (MAE). RISSDV errors were significantly lower than those of RIAV (RMSE and MAE: p < 0.001), RIIV (RMSE: p < 0.01; MAE p < 0.05), and RIFV (RMSE and MAE: p < 0.001), and it appeared less sensitive to absent or missed PPG pulses than respiratory-induced frequency variations. Further research is necessary to extrapolate these findings to subjects under ambulatory rather than stationary conditions, including pediatric and neonatal populations.

Contactless Monitoring System Versus Gold Standard for Respiratory Rate Monitoring in Emergency Department Patients: Pilot Comparison Study

BACKGROUND

Respiratory rate is a crucial indicator of disease severity yet is the most neglected vital sign. Subtle changes in respiratory rate may be the first sign of clinical deterioration in a variety of disease states. Current methods of respiratory rate monitoring are labor-intensive and sensitive to motion artifacts, which often leads to inaccurate readings or underreporting; therefore, new methods of respiratory monitoring are needed. The PulsON 440 (P440; TSDR Ultra Wideband Radios and Radars) radar module is a contactless sensor that uses an ultrawideband impulse radar to detect respiratory rate. It has previously demonstrated accuracy in a laboratory setting and may be a useful alternative for contactless respiratory monitoring in clinical settings; however, it has not yet been validated in a clinical setting.

OBJECTIVE

The goal of this study was to (1) compare the P440 radar module to gold standard manual respiratory rate monitoring and standard of care telemetry respiratory monitoring through transthoracic impedance plethysmography and (2) compare the P440 radar to gold standard measurements of respiratory rate in subgroups based on sex and disease state.

METHODS

This was a pilot study of adults aged 18 years or older being monitored in the emergency department. Participants were monitored with the P440 radar module for 2 hours and had gold standard (manual respiratory counting) and standard of care (telemetry) respiratory rates recorded at 15-minute intervals during that time. Respiratory rates between the P440, gold standard, and standard telemetry were compared using Bland-Altman plots and intraclass correlation coefficients.

RESULTS

A total of 14 participants were enrolled in the study. The P440 and gold standard Bland-Altman analysis showed a bias of -0.76 (-11.16 to 9.65) and an intraclass correlation coefficient of 0.38 (95% CI 0.06-0.60). The P440 and gold standard had the best agreement at normal physiologic respiratory rates. There was no change in agreement between the P440 and the gold standard when grouped by admitting diagnosis or sex.

CONCLUSIONS

Although the P440 did not have statistically significant agreement with gold standard respiratory rate monitoring, it did show a trend of increased agreement in the normal physiologic range, overestimating at low respiratory rates, and underestimating at high respiratory rates. This trend is important for adjusting future models to be able to accurately detect respiratory rates. Once validated, the contactless respiratory monitor provides a unique solution for monitoring patients in a variety of settings.

A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model

Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train-test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study's model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals.

Respiratory patterns and physical fitness in healthy adults: a cross-sectional study

BACKGROUND

The altered respiratory patterns have a significant impact on our health. However, the links between respiration patterns during spontaneous breathing and physical fitness remain unknown. Therefore, we sought to examine how the respiratory pattern during spontaneous breathing interacts with physical fitness.

METHODS

A total of 610 participants (aged 20-59 years) were enrolled; 163 men (age = 41 ± 11) and 401 women (age = 42 ± 9) were included for analysis. The parameters of the respiration pattern were respiration rate (RR) and inhalation/exhalation (I/E) ratio. The physical fitness components were body size, visuomotor reaction time, balance, flexibility, hand grip strength, back extension strength, vertical jump height, number of push-ups, number of sit-ups, and the maximum rate of oxygen consumption. The data were analyzed separately for two gender groups. Participants within each gender group were further divided into two age categories (young: 20-39 years, middle-aged: 40-59 years) for the analysis, and both correlational and comparative tests were used to solidify the results.

RESULTS

Neither RRs nor the I/E ratios were substantially correlated with physical fitness in women. In addition, the I/E ratios showed no significant correlation with physical fitness in young men, while the results from correlational and comparative tests were inconsistent in middle-aged men. Consistently, men with lower RRs exhibited significantly shorter visuomotor reaction times in two age groups, and demonstrated significantly higher vertical jump heights in the middle-aged group.

CONCLUSIONS

In women, respiratory patterns were not correlated with physical fitness. The relationship between middle-aged men's I/E ratios and their physical fitness warrants further investigation. Men with lower RRs may have better visual-motor coordination and/or sustained attention, while middle-aged men with lower RRs may also have greater leg explosive power and neuromuscular coordination, which should be considered for physical assessment and health improvement.

Smart Home Sleep Respiratory Monitoring System Based on a Breath-Responsive Covalent Organic Framework

A smart home sleep respiratory monitoring system based on a breath-responsive covalent organic framework (COF) was developed and utilized to monitor the sleep respiratory behavior of real sleep apnea patients in this work. The capacitance of the interdigital electrode chip coated with COFTPDA-TFPy exhibits thousands-level reversible responses to breath humidity gases, with subsecond response time and robustness against environmental humidity. A miniaturized printed circuit board, an open-face-mask-based respiratory sensor, and a smartphone app were constructed for the wearable wireless smart home sleep respiratory monitoring system. Leveraging the sensitive and rapid reversible response of COFs, the COF-based respiratory monitoring system can effectively record normal breath, rapid breath, and breath apnea, enabling over a thousand cycles of hour-level continuous monitoring during daily wear. Next, we took the groundbreaking step of advancing the humidity sensor to the clinical trial stage. In clinical experiments on real sleep apnea patients, the COF-based respiratory monitoring system successfully recorded hour-level sleep respiratory data and differentiated the breathing behavior characteristics and severity of sleep apnea patients and subjects with normal sleep function and primary snoring patients. This work successfully advanced humidity sensors into clinical research for real patients and demonstrated the enormous application potential of COF materials in clinical diagnosis.

Risk of death and readmission among individuals with heart failure and HIV: A systematic review and meta-analysis

The association between human immunodeficiency virus (HIV) status and readmissions and death outcomes in patients with established heart failure (HF) remains unclear. We conducted a systematic search of PubMed, EMBASE, Cochrane Library, and Web of Science up to March 1st, 2023, for cohort studies of adult patients (≥18 years) diagnosed with HF and recorded HIV status at baseline. Our analysis included 7 studies with 10,328 HF patients living with HIV and 48,757 HF patients without HIV. Compared to HF patients without HIV, those with HIV had a higher risk of all-cause deaths (HR: 1.20, 95% CI: 1.15-1.25). HIV infection was also associated with increased risks of HF-associated readmission (HR: 1.34, 95% CI: 1.03-1.75) and all-cause readmission (HR: 1.27, 95% CI: 1.10-1.46). Our study highlights the independent association between HIV and poor HF outcomes, emphasizing the need for improved management in individuals living with HIV.

Motion-compensation approach for quantitative digital subtraction angiography and its effect on in-vivo blood velocity measurement

Purpose

Quantitative monitoring of flow-altering interventions has been proposed using algorithms that quantify blood velocity from time-resolved two-dimensional angiograms. These algorithms track the movement of contrast oscillations along a vessel centerline. Vessel motion may occur relative to a statically defined vessel centerline, corrupting the blood velocity measurement. We provide a method for motion-compensated blood velocity quantification.

Approach

The motion-compensation approach utilizes a vessel segmentation algorithm to perform frame-by-frame vessel registration and creates a dynamic vessel centerline that moves with the vasculature. Performance was evaluated in-vivo through comparison with manually annotated centerlines. The method was also compared to a previous uncompensated method using best- and worst-case static centerlines chosen to minimize and maximize centerline placement accuracy. Blood velocities determined through quantitative DSA (qDSA) analysis for each centerline type were compared through linear regression analysis.

Results

Centerline distance errors were 0.3 ± 0.1    mm relative to gold standard manual annotations. For the uncompensated approach, the best- and worst-case static centerlines had distance errors of 1.1 ± 0.6 and 2.9 ± 1.2    mm , respectively. Linear regression analysis found a high R -squared between qDSA-derived blood velocities using gold standard centerlines and motion-compensated centerlines (R 2 = 0.97 ) with a slope of 1.15 and a small offset of - 0.6    cm / s . The use of static centerlines resulted in low coefficients of determination for the best case (R 2 = 0.35 ) and worst-case (R 2 = 0.20 ) scenarios, with slopes close to zero.

Conclusions

In-vivo validation of motion-compensated qDSA analysis demonstrated improved velocity quantification accuracy in vessels with motion, addressing an important clinical limitation of the current qDSA algorithm.

Navigating Pediatric Capnography: A Comprehensive Review of Scope and Limitations

This review comprehensively explores pediatric capnography, a vital tool in contemporary respiratory monitoring. The overview encompasses the foundational principles of capnography, elucidating its real-time measurement of carbon dioxide (CO2) in respiratory gases. The review emphasizes its paramount role in pediatric care and underscores capnography's significance in detecting respiratory abnormalities and guiding timely interventions. The distinctions between mainstream and sidestream capnography, the key to understanding their applications, are meticulously outlined. Addressing the importance of ongoing research and education, the review advocates for a dynamic approach to refine guidelines and optimize capnography utilization in pediatric settings. The conclusion reflects on the scope and limitations of pediatric capnography, acknowledging its transformative impact while advocating for a judicious recognition of constraints. As we navigate the future of pediatric respiratory care, the synergy of research, education, and clinical application emerges as the cornerstone for advancing pediatric capnography to new horizons.

Identification of Respiration Types Through Respiratory Signal Derived From Clinical and Wearable Electrocardiograms

GOAL

To evaluate suitability of respiratory signals derived from clinical 12-lead electrocardiograms (ECGs) and wearable 1-lead ECG to identify different respiration types.

METHODS

ECGs were simultaneously acquired through the M12R ECG Holter by Global Instrumentation and the chest strap BioHarness 3.0 by Zephyr from 42 healthy subjects alternating normal breathing, breath holding, and deep breathing. Respiration signals were derived from the ECGs through the Segmented-Beat Modulation Method (SBMM)-based algorithm and the algorithms by Van Gent, Charlton, Soni and Sarkar, and characterized in terms of breathing rate and amplitude. Respiration classification was performed through a linear support vector machine and evaluated by F1 score.

RESULTS

Best F1 scores were 86.59%(lead V2) and 80.57%, when considering 12-lead and 1-lead ECGs, respectively, and using SBMM-based algorithm.

CONCLUSION

ECG-derived respiratory signals allow reliable identification of different respiration types even when acquired through wearable sensors, if associated to appropriate processing algorithms, such as the SBMM-based algorithm.

Wearable Systems for Unveiling Collective Intelligence in Clinical Settings

Nowadays, there is an ever-growing interest in assessing the collective intelligence (CI) of a team in a wide range of scenarios, thanks to its potential in enhancing teamwork and group performance. Recently, special attention has been devoted on the clinical setting, where breakdowns in teamwork, leadership, and communication can lead to adverse events, compromising patient safety. So far, researchers have mostly relied on surveys to study human behavior and group dynamics; however, this method is ineffective. In contrast, a promising solution to monitor behavioral and individual features that are reflective of CI is represented by wearable technologies. To date, the field of CI assessment still appears unstructured; therefore, the aim of this narrative review is to provide a detailed overview of the main group and individual parameters that can be monitored to evaluate CI in clinical settings, together with the wearables either already used to assess them or that have the potential to be applied in this scenario. The working principles, advantages, and disadvantages of each device are introduced in order to try to bring order in this field and provide a guide for future CI investigations in medical contexts.

Efficacy of Marker-Based Motion Capture for Respiratory Cycle Measurement: A Comparison with Spirometry

Respiratory rate monitoring is fundamental in clinical settings, and the accuracy of measurement methods is critical. This study aimed to develop and validate methods for assessing respiratory rate and the duration leof respiratory cycle phases in different body positions using optoelectronic plethysmography (OEP) based on a motion capture video system. Two analysis methods, the summation method and the triangle method were developed. The study focused on determining the optimal number of markers while achieving accuracy in respiratory parameter measurements. The results showed that most analysis methods showed a difference of ≤0.5 breaths per minute, with R2 ≥ 0.94 (p < 0.001) compared to spirometry. The best OEP methods for respiratory rate were the abdominal triangles and the sum of abdominal markers in all body positions. The study explored inspiratory and expiratory durations. The research found that 5-9 markers were sufficient to accurately determine respiratory time components in all body positions, reducing the marker requirements compared to previous studies. This interchangeability of OEP methods with standard spirometry demonstrates the potential of non-invasive methods for the simultaneous assessment of body segment movements, center of pressure dynamics, and respiratory movements. Future research is required to improve the clinical applicability of these methods.

Robust in-vehicle respiratory rate detection using multimodal signal fusion

Continuous health monitoring in private spaces such as the car is not yet fully exploited to detect diseases in an early stage. Therefore, we develop a redundant health monitoring sensor system and signal fusion approaches to determine the respiratory rate during driving. To recognise the breathing movements, we use a piezoelectric sensor, two accelerometers attached to the seat and the seat belt, and a camera behind the windscreen. We record data from 15 subjects during three driving scenarios (15 min each) city, highway, and countryside. An additional chest belt provides the ground truth. We compare the four convolutional neural network (CNN)-based fusion approaches: early, sensor-based late, signal-based late, and hybrid fusion. We evaluate the performance of fusing for all four signals to determine the portion of driving time and the signal combination. The hybrid algorithm fusing all four signals is most effective in detecting respiratory rates in the city ([Formula: see text]), highway ([Formula: see text]), and countryside ([Formula: see text]). In summary, 60% of the total driving time can be used to measure the respiratory rate. The number of signals used in the multi-signal fusion improves reliability and enables continuous health monitoring in a driving vehicle.

Cardiorespiratory dynamics during respiratory maneuver in athletes

Introduction: The modern practice of sports medicine and medical rehabilitation requires the search for subtle criteria for the development of conditions and recovery of the body after diseases, which would have a prognostic value for the prevention of negative effects of training and rehabilitation tools, and also testify to the development and course of mechanisms for counteracting pathogenetic processes in the body. The purpose of this study was to determine the informative directions of the cardiorespiratory system parameters dynamics during the performing a maneuver with a change in breathing rate, which may indicate the body functional state violation.

Methods: The results of the study of 183 healthy men aged 21.2 ± 2.3 years who regularly engaged in various sports were analyzed. The procedure for studying the cardiorespiratory system included conducting combined measurements of indicators of activity of the respiratory and cardiovascular systems in a sitting position using a spiroarteriocardiograph device. The duration of the study was 6 min and involved the sequential registration of three measurements with a change in breathing rate (spontaneous breathing, breathing at 0.1 Hz and 0.25 Hz).

Results: Performing a breathing maneuver at breathing 0.1 Hz and breathing 0.25 Hz in comparison with spontaneous breathing leads to multidirectional significant changes in heart rate variability indicators–TP (ms2), LF (ms2), LFHF (ms2/ms2); of blood pressure variability indicators–TPDBP (mmHg2), LFSBP (mmHg2), LFDBP (mmHg2), HFSBP (mmHg2); of volume respiration variability indicators - LFR, (L×min-1)2; HFR, (L×min-1)2; LFHFR, (L×min-1)2/(L×min-1)2; of arterial baroreflex sensitivity indicators - BRLF (ms×mmHg-1), BRHF (ms×mmHg-1). Differences in indicators of systemic hemodynamics and indicators of cardiovascular and respiratory systems synchronization were also informative.

Conclusion: According to the results of the study, it is shown that during performing a breathing maneuver with a change in the rate of breathing, there are significant changes in cardiorespiratory parameters, the analysis of which the increments made it possible to determine of the changes directions dynamics, their absolute values and informative limits regarding the possible occurrence of the cardiorespiratory interactions dysregulation.

Cardiorespiratory dynamics during respiratory maneuver in athletes

Introduction: The modern practice of sports medicine and medical rehabilitation requires the search for subtle criteria for the development of conditions and recovery of the body after diseases, which would have a prognostic value for the prevention of negative effects of training and rehabilitation tools, and also testify to the development and course of mechanisms for counteracting pathogenetic processes in the body. The purpose of this study was to determine the informative directions of the cardiorespiratory system parameters dynamics during the performing a maneuver with a change in breathing rate, which may indicate the body functional state violation. Methods: The results of the study of 183 healthy men aged 21.2 ± 2.3 years who regularly engaged in various sports were analyzed. The procedure for studying the cardiorespiratory system included conducting combined measurements of indicators of activity of the respiratory and cardiovascular systems in a sitting position using a spiroarteriocardiograph device. The duration of the study was 6 min and involved the sequential registration of three measurements with a change in breathing rate (spontaneous breathing, breathing at 0.1 Hz and 0.25 Hz). Results: Performing a breathing maneuver at breathing 0.1 Hz and breathing 0.25 Hz in comparison with spontaneous breathing leads to multidirectional significant changes in heart rate variability indicators-TP (ms2), LF (ms2), LFHF (ms2/ms2); of blood pressure variability indicators-TPDBP (mmHg2), LFSBP (mmHg2), LFDBP (mmHg2), HFSBP (mmHg2); of volume respiration variability indicators - LFR, (L×min-1)2; HFR, (L×min-1)2; LFHFR, (L×min-1)2/(L×min-1)2; of arterial baroreflex sensitivity indicators - BRLF (ms×mmHg-1), BRHF (ms×mmHg-1). Differences in indicators of systemic hemodynamics and indicators of cardiovascular and respiratory systems synchronization were also informative. Conclusion: According to the results of the study, it is shown that during performing a breathing maneuver with a change in the rate of breathing, there are significant changes in cardiorespiratory parameters, the analysis of which the increments made it possible to determine of the changes directions dynamics, their absolute values and informative limits regarding the possible occurrence of the cardiorespiratory interactions dysregulation.

Continuous Critical Respiratory Parameter Measurements Using a Single Low-Cost Relative Humidity Sensor: Evaluation Study

BACKGROUND

Accurate and portable respiratory parameter measurements are critical for properly managing chronic obstructive pulmonary diseases (COPDs) such as asthma or sleep apnea, as well as controlling ventilation for patients in intensive care units, during surgical procedures, or when using a positive airway pressure device for sleep apnea.

OBJECTIVE

The purpose of this research is to develop a new nonprescription portable measurement device that utilizes relative humidity sensors (RHS) to accurately measure key respiratory parameters at a cost that is approximately 10 times less than the industry standard.

METHODS

We present the development, implementation, and assessment of a wearable respiratory measurement device using the commercial Bosch BME280 RHS. In the initial stage, the RHS was connected to the pneumotach (PNT) gold standard device via its external connector to gather breathing metrics. Data collection was facilitated using the Arduino platform with a Bluetooth Low Energy connection, and all measurements were taken in real time without any additional data processing. The device's efficacy was tested with 7 participants (5 men and 2 women), all in good health. In the subsequent phase, we specifically focused on comparing breathing cycle and respiratory rate measurements and determining the tidal volume by calculating the region between inhalation and exhalation peaks. Each participant's data were recorded over a span of 15 minutes. After the experiment, detailed statistical analysis was conducted using ANOVA and Bland-Altman to examine the accuracy and efficiency of our wearable device compared with the traditional methods.

RESULTS

The perfused air measured with the respiratory monitor enables clinicians to evaluate the absolute value of the tidal volume during ventilation of a patient. In contrast, directly connecting our RHS device to the surgical mask facilitates continuous lung volume monitoring. The results of the 1-way ANOVA showed high P values of .68 for respiratory volume and .89 for respiratory rate, which indicate that the group averages with the PNT standard are equivalent to those with our RHS platform, within the error margins of a typical instrument. Furthermore, analysis utilizing the Bland-Altman statistical method revealed a small bias of 0.03 with limits of agreement (LoAs) of -0.25 and 0.33. The RR bias was 0.018, and the LoAs were -1.89 and 1.89.

CONCLUSIONS

Based on the encouraging results, we conclude that our proposed design can be a viable, low-cost wearable medical device for pulmonary parametric measurement to prevent and predict the progression of pulmonary diseases. We believe that this will encourage the research community to investigate the application of RHS for monitoring the pulmonary health of individuals.

Chitosan-Based Highly Sensitive Viable Humidity Sensor for Human Health Monitoring

We report a sustainable resistive-type humidity sensor based on chitosan (CS) film deposited on an interdigitated Ti/Au electrode coated SiO2 substrate using a simple drop cast approach for human health monitoring. The sensor revealed remarkably high sensitivity (5.8 MΩ/%RH), fast response/recovery time (21 s/25 s), low hysteresis (∼9.3%), excellent reversibility, wide detecting range (11-95% RH), and high selectivity toward water vapor. The calculated associated uncertainty at different %RH indicates the excellent repeatability and stable performance of the sensor. The developed sensor is tested for different human breath patterns, and it is found that the sensor can clearly distinguish between the variations in rate and depth of respiration patterns during normal, fast, deep, and nasal breathing and can monitor for apnea-like situations. The sensor is also utilized to perform noncontact skin humidity sensing. Overall, the developed CS film humidity sensor provides a viable approach for the detection of respiratory disorders and human health issues, detected by skin moisture, in a noninvasive manner.

Pulmonary and chest wall function in obese adults

Obesity is frequently associated with breathing disorders. To investigate if and how the highest levels of obesity impact respiratory function, 17 subjects with obesity (median age: 49 years; BMI: 39.7 kg/m2, 8 females) and 10 normal-weighted subjects (49 years; 23.9 kg/m2, 5 females) were studied. The abdominal volume occupied 41% in the obese group, being higher (p < 0.001) than the normal-weighted group (31%), indicating accumulation of abdominal fat. Restrictive lung defect was present in 17% of subjects with obesity. At rest in the supine position, subjects with obesity breathed with higher minute ventilation (11.9 L/min) and lower ribcage contribution (5.7%) than normal weighted subjects (7.5 L/min, p = 0.001 and 31.1%, p = 0.003, respectively), thus indicating thoracic restriction. Otherwise healthy obesity might not be characterized by a systematic restrictive lung pattern. Despite this, another sign of restriction could be poor thoracic expansion at rest in the supine position, resulting in increased ventilation. Class 3 obesity made respiratory rate further increased. Opto-electronic plethysmography and its thoraco-abdominal analysis of awake breathing add viable and interesting information in subjects with obesity that were complementary to pulmonary function tests. In addition, OEP is able to localize the restrictive effect of obesity.

Evaluation of three approaches used for respiratory measurement in healthy subjects

Objective. Respiration is one of the critical vital signs of human health status, and accurate respiratory monitoring has important clinical significance. There is substantial evidence that alterations in key respiratory parameters can be used to determine a patient's health status, aid in the selection of appropriate treatments, predict potentially serious clinical events and control respiratory activity. Although various approaches have been developed for respiration monitoring, no definitive conclusions have been drawn regarding the accuracy of these approaches because each has different advantages and limitations. In the present study, we evaluated the performance of three non-invasive respiratory measurement approaches, including transthoracic impedance (IMP), surface diaphragm electromyography-derived respiration (EMGDR) and electrocardiogram-derived respiration (ECGDR), and compared them with the direct measurement of airflow (FLW) in 33 male and 38 female healthy subjects in the resting state.Approach. The accuracy of six key respiratory parameters, including onset of inspiration (Ion), onset of expiration (Eon), inspiratory time (It), expiratory time (Et), respiratory rate (RR) and inspiratory-expiratory ratio (I:E), measured from the IMP, EMGDR and ECGDR, were compared with those annotated from the reference FLW.Main results. The correlation coefficients between the estimated inspiratory volume and reference value were 0.72 ± 0.20 for IMP, 0.62 ± 0.23 for EMGDR and 0.46 ± 0.21 for ECGDR (p< 0.01 among groups). The positive predictive value and sensitivity for respiration detection were 100% and 100%, respectively, for IMP, which were significantly higher than those of the EMGDR (97.2% and 95.5%,p< 0.001) and the ECGDR (96.9% and 90.0%,p< 0.001). Additionally, the mean error (ME) forIon,Eon,It,EtandRRdetection were markedly lower for IMP than for EMGDR and ECGDR (p< 0.001).Significance. Compared with EMGDR and ECGDR, the IMP signal had a higher positive predictive value, higher sensitivity and lower ME for respiratory parameter detection. This suggests that IMP is more suitable for dedicated respiratory monitoring and parameter evaluation.

Bioinspired Environment-Adaptable and Ultrasensitive Multifunctional Electronic Skin for Human Healthcare and Robotic Sensations

Multifunctional electronic skins (e-skins) that can sense various stimuli have demonstrated increasing potential in many fields. However, most e-skins are human-oriented that cannot work in hash environments such as high temperature, underwater, and corrosive chemicals, impairing their applications, especially in human-machine interfaces, intelligent machines, robotics, and so on. Inspired by the crack-shaped sensory organs of spiders, an environmentally robust and ultrasensitive multifunctional e-skin is developed. By developing a polyimide-based metal crack-localization strategy, the device has excellent environment adaptability since polyimide has high thermal stability and chemical durability. The localized cracked part serves as an ultrasensitive strain sensing unit, while the non-cracked serpentine part is solely responsible for temperature. Since the two units are made of the same material and process, the signals are decoupled easily. The proposed device is the first multifunctional e-skin that can be used in harsh environments, therefore is of great potential for both human and robot-oriented applications.

Thermal Image Processing for Respiratory Estimation from Cubical Data with Expandable Depth

As healthcare costs continue to rise, finding affordable and non-invasive ways to monitor vital signs is increasingly important. One of the key metrics for assessing overall health and identifying potential issues early on is respiratory rate (RR). Most of the existing methods require multiple steps that consist of image and signal processing. This might be difficult to deploy on edge devices that often do not have specialized digital signal processors (DSP). Therefore, the goal of this study is to develop a single neural network realizing the entire process of RR estimation in a single forward pass. The proposed solution builds on recent advances in video recognition, capturing both spatial and temporal information in a multi-path network. Both paths process the data at different sampling rates to capture rapid and slow changes that are associated with differences in the temperature of the nostril area during the breathing episodes. The preliminary results show that the introduced end-to-end solution achieves better performance compared to state-of-the-art methods, without requiring additional pre/post-processing steps and signal-processing techniques. In addition, the presented results demonstrate its robustness on low-resolution thermal video sequences that are often used at the embedded edge due to the size and power constraints of such systems. Taking that into account, the proposed approach has the potential for efficient and convenient respiratory rate estimation across various markets in solutions deployed locally, close to end users.

Breathing Chest Wall Kinematics Assessment through a Single Digital Camera: A Feasibility Study

The identification of respiratory patterns based on the movement of the chest wall can assist in monitoring an individual's health status, particularly those with neuromuscular disorders, such as hemiplegia and Duchenne muscular dystrophy. Thoraco-abdominal asynchrony (TAA) refers to the lack of coordination between the rib cage and abdominal movements, characterized by a time delay in their expansion. Motion capture systems, like optoelectronic plethysmography (OEP), are commonly employed to assess these asynchronous movements. However, alternative technologies able to capture chest wall movements without physical contact, such as RGB digital cameras and time-of-flight digital cameras, can also be utilized due to their accessibility, affordability, and non-invasive nature. This study explores the possibility of using a single RGB digital camera to record the kinematics of the thoracic and abdominal regions by placing four non-reflective markers on the torso. In order to choose the positions of these markers, we previously investigated the movements of 89 chest wall landmarks using OEP. Laboratory tests and volunteer experiments were conducted to assess the viability of the proposed system in capturing the kinematics of the chest wall and estimating various time-related respiratory parameters (i.e., fR, Ti, Te, and Ttot) as well as TAA indexes. The results demonstrate a high level of agreement between the detected chest wall kinematics and the reference data. Furthermore, the system shows promising potential in estimating time-related respiratory parameters and identifying phase shifts indicative of TAA, thus suggesting its feasibility in detecting abnormal chest wall movements without physical contact with a single RGB camera.

Ventilation and perceived exertion are sensitive to changes in exercise tolerance: arm+leg cycling vs. leg cycling

Purpose: Growing evidence suggests that respiratory frequency (f R) is a marker of physical effort and a variable sensitive to changes in exercise tolerance. The comparison between arm+leg cycling (Arm+leg) and leg cycling (Leg) has the potential to further test this notion because a greater exercise tolerance is expected in the Arm+leg modality. We systematically compared Arm+leg vs. Leg using different performance tests. Methods: Twelve males underwent six performance tests in separate, randomized visits. Three tests were performed in each of the two exercise modalities, i.e. an incremental test and two time-to-exhaustion (TTE) tests performed at 90% or 75% of the peak power output reached in the Leg incremental test (PPOLeg). Exercise tolerance, perceived exertion, and cardiorespiratory variables were recorded during all the tests. Results: A greater exercise tolerance (p < 0.001) was found for Arm+leg in the incremental test (337 ± 32 W vs. 292 ± 28 W), in the TTE test at 90% of PPOLeg (638 ± 154 s vs. 307 ± 67 s), and in the TTE test at 75% of PPOLeg (1,675 ± 525 s vs. 880 ± 363 s). Unlike V ˙ O2 and heart rate, both f R and minute ventilation were lower (p < 0.003) at isotime in all the Arm+leg tests vs. Leg tests. Furthermore, a lower perceived exertion was observed in the Arm+leg tests, especially during the TTE tests (p < 0.001). Conclusion: Minute ventilation, f R and perceived exertion are sensitive to the improvements in exercise tolerance observed when comparing Arm+leg vs. Leg, unlike V ˙ O2 and heart rate.

A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis

Wireless sensing systems are required for continuous health monitoring and data collection. It allows for patient data collection in real time rather than through time-consuming and expensive hospital or lab visits. This technology employs wearable sensors, signal processing, and wireless data transfer to remotely monitor patients' health. The research offers a novel approach to providing primary diagnostics remotely with a digital health system for monitoring pulmonary health status using a multimodal wireless sensor device. The technology uses a compact wearable with new integration of acoustics and biopotentials sensors to monitor cardiovascular and respiratory activity to provide comprehensive and fast health status monitoring. Furthermore, the small wearable sensor size may stick to human skin and record heart and lung activities to monitor respiratory health. This paper proposes a sensor data fusion method of lung sounds and cardiograms for potential real-time respiration pattern diagnostics, including respiratory episodes like low tidal volume and coughing. With a p-value of 0.003 for sound signals and 0.004 for electrocardiogram (ECG), preliminary tests demonstrated that it was possible to detect shallow breathing and coughing at a meaningful level.

Eliminating the Trade-off Between Resolution and Sampling Rate in Magnetic Induction Based Cardiorespiratory Sensors

This paper proposes a novel algorithm that allows a significant improvement of the resolution of frequency modulated magnetic induction sensors while providing high sampling rates. We have implemented this approach in a frequency modulated magnetic induction sensor and our first measurements demonstrate the improvement of the sensor's signal quality.

RRDetection: Respiration Rate Estimation Using Earbuds During Physical Activities

In modern times, earbuds have become both popular and essential accessories for people to use with a wide range of devices in their everyday lives. Moreover, the respiration rate is a crucial vital sign that is sensitive to various pathological conditions. Many earbuds now come equipped with multiple sensing capabilities, including inertial and acoustic sensors. These sensors can be used by researchers to passively monitor users' vital signs, such as respiration rates. While current earbud-based breath rate estimation algorithms mostly focus on resting conditions, recent studies have demonstrated that respiration rates during physical activities can predict cardio-respiratory fitness for healthy individuals and pulmonary conditions for respiratory patients. To address this gap, we propose a novel algorithm called RRDetection that leverages the motion sensors in ordinary earbuds to detect respiration rates during light to moderate physical activities.

Internet of medical things. Measurement of respiratory dynamics using wearable sensors in post-COVID-19 patients

Nowadays, the measurement of respiratory dynamics is underrated at clinical setting and in the daily life of a subject and it still represents a challenge from a technical and medical point of view. In this article we propose a concept to measure some of its parameters, such as the respiratory rate (RR), using four inertial sensors. Two different experiments were performed to validate the concept. We analyzed the most suitable placement of each sensor to assess those features and we studied the reliability of the system to measure abnormal parameters of respiration (tachypnea, bradypnea and breath holding). Finally, we measured post-COVID-19 patients, some of them with breath alterations after more than a year of the diagnosis. Experimental results showed that the proposed system could be potentially used to measure the respiratory dynamics at clinical setting. Moreover, while RR can be easily calculated by any sensor, other parameters need to be measured with a sensor in a particular position.

Respiratory Rate Estimation during Walking and Running Using Breathing Sounds Recorded with a Microphone

Emerging evidence suggests that respiratory frequency (f_R) is a valid marker of physical effort. This has stimulated interest in developing devices that allow athletes and exercise practitioners to monitor this vital sign. The numerous technical challenges posed by breathing monitoring in sporting scenarios (e.g., motion artifacts) require careful consideration of the variety of sensors potentially suitable for this purpose. Despite being less prone to motion artifacts than other sensors (e.g., strain sensors), microphone sensors have received limited attention so far. This paper proposes the use of a microphone embedded in a facemask for estimating f_R from breath sounds during walking and running. f_R was estimated in the time domain as the time elapsed between consecutive exhalation events retrieved from breathing sounds every 30 s. Data were collected from ten healthy subjects (both males and females) at rest and during walking (at 3 km/h and 6 km/h) and running (at 9 km/h and 12 km/h) activities. The reference respiratory signal was recorded with an orifice flowmeter. The mean absolute error (MAE), the mean of differences (MOD), and the limits of agreements (LOAs) were computed separately for each condition. Relatively good agreement was found between the proposed system and the reference system, with MAE and MOD values increasing with the increase in exercise intensity and ambient noise up to a maximum of 3.8 bpm (breaths per minute) and -2.0 bpm, respectively, during running at 12 km/h. When considering all the conditions together, we found an MAE of 1.7 bpm and an MOD ± LOAs of -0.24 ± 5.07 bpm. These findings suggest that microphone sensors can be considered among the suitable options for estimating f_R during exercise.

Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters

Physical fatigue reduces productivity and quality of work while increasing the risk of injuries and accidents among safety-sensitive professionals. To prevent its adverse effects, researchers are developing automated assessment methods that, despite being highly accurate, require a comprehensive understanding of underlying mechanisms and variables' contributions to determine their real-life applicability. This work aims to evaluate the performance variations of a previously developed four-level physical fatigue model when alternating its inputs to have a comprehensive view of the impact of each physiological variable on the model's functioning. Data from heart rate, breathing rate, core temperature and personal characteristics from 24 firefighters during an incremental running protocol were used to develop the physical fatigue model based on an XGBoosted tree classifier. The model was trained 11 times with different input combinations resulting from alternating four groups of features. Performance measures from each case showed that heart rate is the most relevant signal for estimating physical fatigue. Breathing rate and core temperature enhanced the model when combined with heart rate but showed poor performance individually. Overall, this study highlights the advantage of using more than one physiological measure for improving physical fatigue modelling. The findings can contribute to variables and sensor selection in occupational applications and as the foundation for further field research.

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

Recent Advances in Multiplexed Wearable Sensor Platforms for Real-Time Monitoring Lifetime Stress: A Review

Researchers are interested in measuring mental stress because it is linked to a variety of diseases. Real-time stress monitoring via wearable sensor systems can aid in the prevention of stress-related diseases by allowing stressors to be controlled immediately. Physical tests, such as heart rate or skin conductance, have recently been used to assess stress; however, these methods are easily influenced by daily life activities. As a result, for more accurate stress monitoring, validations requiring two or more stress-related biomarkers are demanded. In this review, the combinations of various types of sensors (hereafter referred to as multiplexed sensor systems) that can be applied to monitor stress are discussed, referring to physical and chemical biomarkers. Multiplexed sensor systems are classified as multiplexed physical sensors, multiplexed physical-chemical sensors, and multiplexed chemical sensors, with the effect of measuring multiple biomarkers and the ability to measure stress being the most important. The working principles of multiplexed sensor systems are subdivided, with advantages in measuring multiple biomarkers. Furthermore, stress-related chemical biomarkers are still limited to cortisol; however, we believe that by developing multiplexed sensor systems, it will be possible to explore new stress-related chemical biomarkers by confirming their correlations to cortisol. As a result, the potential for further development of multiplexed sensor systems, such as the development of wearable electronics for mental health management, is highlighted in this review.

A Frequency Estimation Scheme Based on Gaussian Average Filtering Decomposition and Hilbert Transform: With Estimation of Respiratory Rate as an Example

Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform biomedical signal analysis. Among miscellaneous approaches, Hilbert-Huang transform (HHT) has been demonstrated to be a potential tool in biomedical applications. However, the problems of mode mixing, unnecessary redundant decomposition and boundary effect are the common deficits that occur during the procedure of empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD). The Gaussian average filtering decomposition (GAFD) technique has been shown to be appropriate in several biomedical scenarios and can be an alternative to EMD and EEMD. This research proposes the combination of GAFD and Hilbert transform that is termed the Hilbert-Gauss transform (HGT) to overcome the conventional drawbacks of HHT in TFA and frequency estimation. This new method is verified to be effective for the estimation of respiratory rate (RR) in finger photoplethysmography (PPG), wrist PPG and seismocardiogram (SCG). Compared with the ground truth values, the estimated RRs are evaluated to be of excellent reliability by intraclass correlation coefficient (ICC) and to be of high agreement by Bland-Altman analysis.

Physiological and Biomechanical Monitoring in American Football Players: A Scoping Review

American football is the sport with the highest rates of concussion injuries. Biomedical engineering applications may support athletes in monitoring their injuries, evaluating the effectiveness of their equipment, and leading industrial research in this sport. This literature review aims to report on the applications of biomedical engineering research in American football, highlighting the main trends and gaps. The review followed the PRISMA guidelines and gathered a total of 1629 records from PubMed (n = 368), Web of Science (n = 665), and Scopus (n = 596). The records were analyzed, tabulated, and clustered in topics. In total, 112 studies were selected and divided by topic in the biomechanics of concussion (n = 55), biomechanics of footwear (n = 6), biomechanics of sport-related movements (n = 6), the aerodynamics of football and catch (n = 3), injury prediction (n = 8), heat monitoring of physiological parameters (n = 8), and monitoring of the training load (n = 25). The safety of players has fueled most of the research that has led to innovations in helmet and footwear design, as well as improvements in the understanding and prevention of injuries and heat monitoring. The other important motivator for research is the improvement of performance, which has led to the monitoring of training loads and catches, and studies on the aerodynamics of football. The main gaps found in the literature were regarding the monitoring of internal loads and the innovation of shoulder pads.

Cellulose Nanocrystal-Based All-3D-Printed Pyro-Piezoelectric Nanogenerator for Hybrid Energy Harvesting and Self-Powered Cardiorespiratory Monitoring toward the Human-Machine Interface

Biomaterials with spontaneous piezoelectric property are highly emerging in recent times for the generation of electricity from mechanical energy sources that are amply available in nature. In this context, pyroelectricity, an integral property of piezoelectric materials, might be an interesting tool in harvesting thermal energy from the fluctuations of temperature. On the other hand, respiration and heart pulse are the significant human vital signs that can be used for early detection and prevention of cardiorespiratory diseases. Here, we report an all-three-dimensional (3D)-printed pyro-piezoelectric nanogenerator (Py-PNG) based on the most abundant and completely biodegradable biopolymer on earth, i.e., cellulose nanocrystal (CNC) for hybrid (mechanical as well as thermal) energy harvesting, and interestingly, the NG could be used as an e-skin sensor for application in self-powered noninvasive cardiorespiratory monitoring for personal healthcare. Notably, the CNC-based device will be biocompatible and economically advantageous due to its biomaterial-based supremacy and huge availability. This is an original approach with 3D geometrical advancement in designing a NG/sensor, where the unique all-3D-printed manner is adopted, and certainly, it has promising potential in reducing the number of processing steps to required equipment during the multilayer fabrication. The all-3D-printed NG/sensor shows outstanding mechano-thermal energy harvesting performance along with sensitivity and is capable of accurate detection of heart pulse as well as respiration, whenever and whichever required without the need of any battery or an external power supply. In addition, we have also extended its application in demonstrating a smart mask-based breath monitoring system. Thus, the real-time cardiorespiratory monitoring provides notable and fascinating information in medical diagnosis, stepping toward biomedical device development and human-machine interface.

Design and Testing of a Smart Facemask for Respiratory Monitoring during Cycling Exercise

Given the importance of respiratory frequency (f_R) as a valid marker of physical effort, there is a growing interest in developing wearable devices measuring f_R in applied exercise settings. Biosensors measuring chest wall movements are attracting attention as they can be integrated into textiles, but their susceptibility to motion artefacts may limit their use in some sporting activities. Hence, there is a need to exploit sensors with signals minimally affected by motion artefacts. We present the design and testing of a smart facemask embedding a temperature biosensor for f_R monitoring during cycling exercise. After laboratory bench tests, the proposed solution was tested on cyclists during a ramp incremental frequency test (RIFT) and high-intensity interval training (HIIT), both indoors and outdoors. A reference flowmeter was used to validate the f_R extracted from the temperature respiratory signal. The smart facemask showed good performance, both at a breath-by-breath level (MAPE = 2.56% and 1.64% during RIFT and HIIT, respectively) and on 30 s average f_R values (MAPE = 0.37% and 0.23% during RIFT and HIIT, respectively). Both accuracy and precision (MOD ± LOAs) were generally superior to those of other devices validated during exercise. These findings have important implications for exercise testing and management in different populations.

Clinical validation of a contactless respiration rate monitor

Respiratory rate (RR) is an often underestimated and underreported vital sign with tremendous clinical value. As a predictor of cardiopulmonary arrest, chronic obstructive pulmonary disease (COPD) exacerbation or indicator of health state for example in COVID-19 patients, respiratory rate could be especially valuable in remote long-term patient monitoring, which is challenging to implement. Contactless devices for home use aim to overcome these challenges. In this study, the contactless Sleepiz One+ respiration monitor for home use during sleep was validated against the thoracic effort belt. The agreement of instantaneous breathing rate and breathing rate statistics between the Sleepiz One+ device and the thoracic effort belt was initially evaluated during a 20-min sleep window under controlled conditions (no body movement) on a cohort of 19 participants and secondly in a more natural setting (uncontrolled for body movement) during a whole night on a cohort of 139 participants. Excellent agreement was shown for instantaneous breathing rate to be within 3 breaths per minute (Brpm) compared to thoracic effort band with an accuracy of 100% and mean absolute error (MAE) of 0.39 Brpm for the setting controlled for movement, and an accuracy of 99.5% with a MAE of 0.48 Brpm for the whole night measurement, respectively. Excellent agreement was also achieved for the respiratory rate statistics over the whole night with absolute errors of 0.43, 0.39 and 0.67 Brpm for the 10th, 50th and 90th percentiles, respectively. Based on these results we conclude that the Sleepiz One+ can estimate instantaneous respiratory rate and its summary statistics at high accuracy in a clinical setting. Further studies are required to evaluate the performance in the home environment, however, it is expected that the performance is at similar level, as the measurement conditions for the Sleepiz One+ device are better at home than in a clinical setting.

A closer look at yoga nidra- early randomized sleep lab investigations

OBJECTIVES

We aimed to examine trial feasibility plus physiological and psychological effects of a guided meditation practice, Yoga Nidra, in adults with self-reported insomnia.

METHODS

Twenty-two adults with self-reported insomnia were recruited to attend two visits at our research center. At Visit 1 (V1), participants were asked to lie quietly for ninety minutes. The primary outcome was change in electroencephalography (EEG). Heart rate variability (HRV), respiratory rate and self-reported mood and anxiety were also measured. At Visit 2 (V2), the same protocol was followed, except half of participants were randomized to practice Yoga Nidra for the first 30-min.

RESULTS

There were no between-group changes (V1-V2) in alpha EEG power at O1 (Intervention: 13 ± 70%; Control: -20 ± 40%), HRV or sleep onset latency in response to Yoga Nidra. Respiratory rate, however, showed statistically significant difference between groups (Yoga Nidra -1.4 breaths per minute (bpm) change during and - 2.1 bpm afterwards vs. Control +0.2 bpm during and + 0.4 bpm after; p = .03 for both during and after). The intervention displayed good acceptability (well-tolerated) and credibility (perceived benefit ratings) with implementation success (target sample size reached; 5% dropout rate).

CONCLUSIONS

This preliminary clinical trial provides early evidence that Yoga Nidra is a well-tolerated, feasible intervention for adults reporting insomnia. Decreased respiratory rate in response to Yoga Nidra needs to be confirmed in more definitive studies.

TRIAL REGISTRATION INFORMATION

This trial was registered on ClinicalTrials.gov as "A Closer Look at Yoga Nidra: Sleep Lab Analyses" (NCT#03685227).

Cardiorespiratory optimal point as a submaximal evaluation tool in endurance athletes: An exploratory study

Introduction: The cardiorespiratory optimal point (COP) represents the lowest minute ventilation to oxygen consumption ratio (VE/VO2) and can be estimated during a CPET at submaximal intensity when an exercise test until volitional fatigue is not always advisable (i.e., a conflict zone where you cannot be confident of the security because near-competition, off-season, among other). COP's physiological components have not been wholly described yet. Therefore, this study seeks to identify the determinants of COP in highly trained athletes and its influence on maximum and sub-maximum variables during CPET through principal c omponent analysis (PCA) (explains the dataset's variance). Methods: Female (n = 9; age, 17.4 ± 3.1 y; maximal VO2 [VO2max]), 46.2 ± 5.9 mL/kg/min) and male (n = 24; age, 19.7 ± 4.0 y; VO2max, 56.1 ± 7.6 mL/kg/min) athletes performed a CPET to determine the COP, ventilatory threshold 1 (VT1) and 2 (VT2), and VO2max. The PCA was used to determine the relationship between variables and COP, explaining their variance. Results: Our data revealed that females and males displayed different COP values. Indeed, males showed a significant diminished COP compared to the female group (22.6 ± 2.9 vs. 27.2 ±3.4 VE/VO2, respectively); nevertheless, COP was allocated before VT1 in both groups. Discussion: PC analysis revealed that the COP variance was mainly explained (75.6%) by PC1 (expired CO₂ at VO2max) and PC2 (VE at VT2), possibly influencing cardiorespiratory efficiency at VO2max and VT2. Our data suggest that COP could be used as a submaximal index to monitor and assess cardiorespiratory system efficiency in endurance athletes. The COP could be particularly useful during the offseason and competitive periods and the return to the sports continuum.

Differential control of respiratory frequency and tidal volume during exercise

The lack of a testable model explaining how ventilation is regulated in different exercise conditions has been repeatedly acknowledged in the field of exercise physiology. Yet, this issue contrasts with the abundance of insightful findings produced over the last century and calls for the adoption of new integrative perspectives. In this review, we provide a methodological approach supporting the importance of producing a set of evidence by evaluating different studies together-especially those conducted in 'real' exercise conditions-instead of single studies separately. We show how the collective assessment of findings from three domains and three levels of observation support the development of a simple model of ventilatory control which proves to be effective in different exercise protocols, populations and experimental interventions. The main feature of the model is the differential control of respiratory frequency (f_R) and tidal volume (V_T); f_R is primarily modulated by central command (especially during high-intensity exercise) and muscle afferent feedback (especially during moderate exercise) whereas V_T by metabolic inputs. Furthermore, V_T appears to be fine-tuned based on f_R levels to match alveolar ventilation with metabolic requirements in different intensity domains, and even at a breath-by-breath level. This model reconciles the classical neuro-humoral theory with apparently contrasting findings by leveraging on the emerging control properties of the behavioural (i.e. f_R) and metabolic (i.e. V_T) components of minute ventilation. The integrative approach presented is expected to help in the design and interpretation of future studies on the control of f_R and V_T during exercise.

Clinical validation of a wearable respiratory rate device: A brief report

BACKGROUND

Respiratory rate (RR) is used for the diagnosis and management of medical conditions and can predict clinical changes. Heavy workload, understaffing, and errors related to poor recording make it underutilized. Wearable devices may facilitate its use.

METHODS

RR measurements using a wearable photoplethysmography-based monitor were compared with medical grade devices in complementary clinical scenarios: Study one included a comparison to a capnograph in 35 healthy volunteers; Study two included a comparison to a ventilator monitor in 18 ventilated patients; and Study three included a comparison to capnograph in 92 COVID-19 patients with active pulmonary disease. Pearson's correlations and Bland-Altman analysis were used to assess the accuracy and agreement between the measurement techniques, including stratification for Body Mass Index (BMI) and skin tone. Statistical significance was set at p ≤ 0.05.

RESULTS

High correlation was found in all studies (r = 0.991, 0.884, and 0.888, respectively, p < 0.001 for all). 95% LOA of ±2.3, 1.7-(-1.6), and ±3.9 with a bias of < 0.1 breaths per minute was found in Bland-Altman analysis in studies 1,2, and 3, respectively. In all, high accordance was found in all sub-groups.

CONCLUSIONS

RR measurements using the wearable monitor were highly-correlated with medical-grade devices in various clinical settings.

TRIAL REGISTRATION

ClinicalTrials.gov, https://clinicaltrials.gov/ct2/show/NCT03603860.

Step-adaptive sound guidance enhances locomotor-respiratory coupling in novice female runners: A proof-of-concept study

Introduction

Many runners struggle to find a rhythm during running. This may be because 20-40% of runners experience unexplained, unpleasant breathlessness at exercise onset. Locomotor-respiratory coupling (LRC), a synchronization phenomenon in which the breath is precisely timed with the steps, may provide metabolic or perceptual benefits to address these limitations. It can also be consciously performed. Hence, we developed a custom smartphone application to provide real-time LRC guidance based on individual step rate.

Methods

Sixteen novice-intermediate female runners completed two control runs outdoors and indoors at a self-selected speed with auditory step rate feedback. Then, the runs were replicated with individualized breath guidance at specific LRC ratios. Hexoskin smart shirts were worn and analyzed with custom algorithms to estimate continuous LRC frequency and phase coupling.

Results

LRC guidance led to a large significant increase in frequency coupling outdoor from 26.3 ± 10.7 (control) to 69.9 ± 20.0 % (LRC) "attached". There were similarly large differences in phase coupling between paired trials, and LRC adherence was stronger for the indoor treadmill runs versus outdoors. There was large inter-individual variability in running pace, preferred LRC ratio, and instruction adherence metrics.

Discussion

Our approach demonstrates how personalized, step-adaptive sound guidance can be used to support this breathing strategy in novice runners. Subsequent investigations should evaluate the skill learning of LRC on a longer time basis to effectively clarify its risks and advantages.

A Differential Inertial Wearable Device for Breathing Parameter Detection: Hardware and Firmware Development, Experimental Characterization

Breathing monitoring is crucial for evaluating a patient's health status. The technologies commonly used to monitor respiration are costly, bulky, obtrusive, and inaccurate, mainly when the user moves. Consequently, efforts have been devoted to providing new solutions and methodologies to overcome these limitations. These methods have several uses, including healthcare monitoring, measuring athletic performance, and aiding patients with respiratory diseases, such as COPD (chronic obtrusive pulmonary disease), sleep apnea, etc. Breathing-induced chest movements can be measured noninvasively and discreetly using inertial sensors. This research work presents the development and testing of an inertia-based chest band for breathing monitoring through a differential approach. The device comprises two IMUs (inertial measurement units) placed on the patient's chest and back to determine the differential inertial signal, carrying out information detection about the breathing activity. The chest band includes a low-power microcontroller section to acquire inertial data from the two IMUs and process them to extract the breathing parameters (i.e., RR-respiration rate; TI/TE-inhalation/exhalation time; IER-inhalation-to-exhalation time; V-flow rate), using the back IMU as a reference. A BLE transceiver wirelessly transmits the acquired breathing parameters to a mobile application. Finally, the test results demonstrate the effectiveness of the used dual-inertia solution; correlation and Bland-Altman analyses were performed on the RR measurements from the chest band and the reference, demonstrating a high correlation (r¯ = 0.92) and low mean difference (MD¯ = -0.27 BrPM (breaths per minute)), limits of agreement (LoA¯ = +1.16/-1.75 BrPM), and mean absolute error (MAE¯ = 1.15%). Additionally, the experimental results demonstrated that the developed device correctly measured the other breathing parameters (TI, TE, IER, and V), keeping an MAE of ≤5%. The obtained results indicated that the developed chest band is a viable solution for long-term breathing monitoring, both in stationary and moving users.

Rope Jumping Strength Monitoring on Smart Devices via Passive Acoustic Sensing

Rope jumping, as a fitness exercise recommended by many sports medicine practitioners, can improve cardiorespiratory capacity and physical coordination. Existing rope jump monitoring systems have limitations in terms of convenience, comfort, and exercise intensity evaluation. This paper presents a rope jump monitoring system using passive acoustic sensing. Our system exploits the off-the-shelf smartphone and headphones to capture the user's rope-jumping sound and breathing sound after exercise. Given the captured acoustic data, the system uses a short-time energy-based approach and the high correlation between rope jumping cycles to detect the rope-jumping sound frames, then applies a dual-threshold endpoint detection algorithm to calculate the number of rope jumps. Finally, our system performs regression predictions of exercise intensity based on features extracted from the jumping speed and the mel spectrograms of the user's breathing sound. The significant advantage of the system lies in the solution of the problem of poorly characterized mel spectrograms. We employ an attentive mechanism-based GAN to generate optimized breathing sound mel spectrograms and apply domain adversarial adaptive in the network to improve the migration capability of the system. Through extensive experiments, our system achieves (on average) 0.32 and 2.3% error rates for the rope jumping count and exercise intensity evaluation, respectively.

The transcultural adaptation and validation of the Chinese version of the Attitudes Toward Recognizing Early and Noticeable Deterioration scale

Background

In China, clinical deterioration events present a real problem for every clinical nurse. Patient deterioration is determined in part by nurses' attitudes toward early recognition of clinical deterioration. However, research on attitudes toward the early identification of clinical deterioration is still in its infancy, and even less research has been done on ward nurses' attitudes toward the early identification of clinical deterioration. To drive behavioral change and improve the care of deteriorating patients, nurses need comprehensive, valid, and reliable tools to assess their attitudes toward early identification of deterioration.

Objective

In this study, we aimed to translate the Attitudes Toward Recognizing Early and Noticeable Deterioration (ATREND) scale into Chinese and to assess its validity and reliability tests.

Methods

From March 2022 to July 2022, the ATREND scale was translated, back-translated, and cross-culturally adapted into the Chinese version using a modified Brislin translation model. Then, 460 ward nurses were recruited from tertiary Grade A general hospitals in two cities: Shenyang and Jinzhou in Liaoning Province, China. Reliability analyses were conducted using internal consistency, split-half, and test-retest reliability. We convened a committee of experts to determine the validity of the content. Tests of the structural validity of the scale were conducted using exploratory and validation factor analyses.

Results

The Cronbach's α value of the Chinese version of the ATREND scale was 0.804, and the Cronbach's α value of the dimensions ranged from 0.782 to 0.863. The split-half reliability and test-retest reliability were 0.846 and 0.711, respectively. Furthermore, the scale has an index of content validity of 0.922, indicating a high level of content validity. In exploratory factor analysis, eigenvalues, total variance explained, and scree plot supported a three-factor structure. The three-factor model supported by this study was confirmed by confirmatory factor analysis (CFA). Moreover, the model fitting indexes (e.g., χ ²/DF = 1.498, GFI = 0.954, RMSEA = 0.047) were all within acceptable limits based on the CFA.

Conclusion

The Chinese version of the scale is reliable and valid among ward nurses. Nursing educators and clinicians will be able to develop targeted educational programs to enhance the competence and behaviors of Chinese ward nurses in recognizing clinical deterioration. It will be based on the developed scale to assess Chinese nurses' attitudes and practices regarding early recognition of clinical deterioration. As a result, it is necessary to consider the Chinese scale's three-factor structure. The developed three-factor structured scale will assess Chinese ward nurses' attitudes and practices toward patient observation and vital sign-monitoring empowerment, enlightening them on the importance of patient observation, encouraging ward nurses to use a wider range of patient assessment techniques to capture early signs of clinical deterioration, and helping ward nurses to develop clinical confidence to monitor clinical deterioration.

Clinical characteristics of and risk factors for secondary bloodstream infection after pneumonia among patients infected with methicillin-resistant Staphylococcus aureus

Purpose

To investigate the clinical features and risk factors for methicillin-resistant Staphylococcus aureus (MRSA) pneumonia (MP) with secondary MRSA bloodstream infections (MRSA-BSI) (termed MP-BSI) compared with MP alone and to study the incidence of MP-BSI among patients with MP.

Methods

This was a retrospective, single-center study with clinical data derived from previous medical records. The cases were divided into groups: MP alone and MP-BSI. The determination of independent risk factors for MP-BSI relied on logistic regression analysis. Additionally, the crude outcomes were compared.

Results

A total of 435 patients with MP were recruited, with 18.9% (82/435) having MP-BSI. The median age was 62 (interquartile range, 51,72) years, and 74.5% of the patients were male. Multivariate analysis revealed that immunosuppression, community-acquired MP (CA-MP), time from initial to targeted antibiotic use, high Sequential Organ Failure Assessment (SOFA) score, increased respiratory rate, and elevated γ-GT level (all p < 0.05) were independent risk factors for MP-BSI, while targeted treatment with linezolid was a protective factor. Patients with MP-BSI had a longer duration of hospitalization (median days, 27.5 vs. 19, p = 0.001), a higher 28-day mortality rate (24.4% vs. 11.0%, p = 0.001), and a higher in-hospital mortality rate (26.8% vs. 14.7%, p = 0.009) than those with MP alone.

Conclusion

Secondary MRSA-BSI among patients with MP is not rare. Immunosuppression, CA-MP, time from initial to targeted antibiotic use, high SOFA score, increased respiratory rate and elevated γ-GT level are all independent risk factors for MP-BSI; however, linezolid, as a targeted antibiotic, is a protective factor. Moreover, patients with MP may have worse clinical outcomes when they develop MRSA-BSI.