Hydrophilic 1T-WS2 Nanosheet Arrays toward Conductive Textiles for High-Efficient and Continuous Hydroelectric Generation and Storage.

Flexible hydroelectric generators (HEGs) are promising self-powered devices that spontaneously derive electrical power from moisture. However, achieving the desired compatibility between a continuous operating voltage and superior current density remains a significant challenge. Herein, a textile-based van der Waals heterostructure is rationally designed between conductive 1T phase tungsten disulfide@carbonized silk (1T-WS2@CSilk) and carbon black@cotton (CB@Cotton) fabrics with an asymmetric distribution of oxygen-containing functional groups, which enhances the proton concentration gradients toward high-performance wearable HEGs. The vertically staggered 1T-WS2 nanosheet arrays on the CSilk fabric provide abundant hydrophilic nanochannels for rapid carrier transport. Furthermore, the moisture-induced primary battery formed between the active aluminum (Al) electrode and the conductive textiles introduces the desired electric field to facilitate charge separation and compensate for the decreased streaming potential. These devices exhibit a power density of 21.6 µW cm-2, an open-circuit voltage (Voc) of 0.65 V sustained for over 10 000 s, and a current density of 0.17 mA cm-2. This performance makes them capable of supplying power to commercial electronics and human respiratory monitoring. This study presents a promising strategy for the refined design of wearable electronics.

Clinical validation of a capnodynamic method for measuring end-expiratory lung volume in critically ill patients.

RATIONALE: End-expiratory lung volume (EELV) is reduced in mechanically ventilated patients, especially in pathologic conditions. The resulting heterogeneous distribution of ventilation increases the risk for ventilation induced lung injury. Clinical measurement of EELV however, remains difficult. OBJECTIVE: Validation of a novel continuous capnodynamic method based on expired carbon dioxide (CO2) kinetics for measuring EELV in mechanically ventilated critically-ill patients. METHODS: Prospective study of mechanically ventilated patients scheduled for a diagnostic computed tomography exploration. Comparisons were made between absolute and corrected EELVCO2 values, the latter accounting for the amount of CO2 dissolved in lung tissue, with the reference EELV measured by computed tomography (EELVCT). Uncorrected and corrected EELVCO2 was compared with total CT volume (density compartments between - 1000 and 0 Hounsfield units (HU) and functional CT volume, including density compartments of - 1000 to - 200HU eliminating regions of increased shunt. We used comparative statistics including correlations and measurement of accuracy and precision by the Bland Altman method. MEASUREMENTS AND MAIN RESULTS: Of the 46 patients included in the final analysis, 25 had a diagnosis of ARDS (24 of which COVID-19). Both EELVCT and EELVCO2 were significantly reduced (39 and 40% respectively) when compared with theoretical values of functional residual capacity (p < 0.0001). Uncorrected EELVCO2 tended to overestimate EELVCT with a correlation r2 0.58; Bias - 285 and limits of agreement (LoA) (+ 513 to - 1083; 95% CI) ml. Agreement improved for the corrected EELVCO2 to a Bias of - 23 and LoA of (+ 763 to - 716; 95% CI) ml. The best agreement of the method was obtained by comparison of corrected EELVCO2 with functional EELVCT with a r2 of 0.59; Bias - 2.75 (+ 755 to - 761; 95% CI) ml. We did not observe major differences in the performance of the method between ARDS (most of them COVID related) and non-ARDS patients. CONCLUSION: In this first validation in critically ill patients, the capnodynamic method provided good estimates of both total and functional EELV. Bias improved after correcting EELVCO2 for extra-alveolar CO2 content when compared with CT estimated volume. If confirmed in further validations EELVCO2 may become an attractive monitoring option for continuously monitor EELV in critically ill mechanically ventilated patients. TRIAL REGISTRATION: clinicaltrials.gov (NCT04045262).

Wireless Sensing System Based on Biodegradable Triboelectric Nanogenerator for Evaluating Sports and Sleep Respiratory.

The rapid growth of the Internet of Things and wearable sensors has led to advancements in monitoring technology in the field of health. One such advancement is the development of wearable respiratory sensors, which offer a new approach to real-time respiratory monitoring compared to traditional methods. However, the energy consumption of these sensors raises concerns about environmental pollution. To address the issue, this study proposes the use of a triboelectric nanogenerator (TENG) as a sustainable energy source. The electrical conductivity of the TENG is improved by incorporating chitosan and carbon nanotubes, with the added benefit of chitosan's biodegradability reducing negative environmental impact. A wireless intelligent respiratory monitoring system (WIRMS) is then introduced, which utilizes a degradable triboelectric nanogenerator for real-time respiratory monitoring, diagnosis, and prevention of obstructive respiratory diseases. WIRMS offers stable and highly accurate respiratory information monitoring, while enabling real-time and nondestructive transmission of information. In addition, machine learning technology is used for sleep respiration state analysis. The potential applications of WIRMS extend to wearables, medical monitoring and sports monitoring, thereby presenting innovative ideas for modern medical and sports monitoring.

Comparative evaluation of a surface-based respiratory monitoring system against a pressure sensor for 4DCT image reconstruction in phantoms.

Four-dimensional computed tomography (4DCT), which relies on breathing-induced motion, requires realistic surrogate information of breathing variations to reconstruct the tumor trajectory and motion variability of normal tissues accurately. Therefore, the SimRT surface-guided respiratory monitoring system has been installed on a Siemens CT scanner. This work evaluated the temporal and spatial accuracy of SimRT versus our commonly used pressure sensor, AZ-733 V. A dynamic thorax phantom was used to reproduce regular and irregular breathing patterns acquired by SimRT and Anzai. Various parameters of the recorded breathing patterns, including mean absolute deviations (MAD), Pearson correlations (PC), and tagging precision, were investigated and compared to ground-truth. Furthermore, 4DCT reconstructions were analyzed to assess the volume discrepancy, shape deformation and tumor trajectory. Compared to the ground-truth, SimRT more precisely reproduced the breathing patterns with a MAD range of 0.37 ± 0.27 and 0.92 ± 1.02 mm versus Anzai with 1.75 ± 1.54 and 5.85 ± 3.61 mm for regular and irregular breathing patterns, respectively. Additionally, SimRT provided a more robust PC of 0.994 ± 0.009 and 0.936 ± 0.062 for all investigated breathing patterns. Further, the peak and valley recognition were found to be more accurate and stable using SimRT. The comparison of tumor trajectories revealed discrepancies up to 7.2 and 2.3 mm for Anzai and SimRT, respectively. Moreover, volume discrepancies up to 1.71 ± 1.62% and 1.24 ± 2.02% were found for both Anzai and SimRT, respectively. SimRT was validated across various breathing patterns and showed a more precise and stable breathing tracking, (i) independent of the amplitude and period, (ii) and without placing any physical devices on the patient's body. These findings resulted in a more accurate temporal and spatial accuracy, thus leading to a more realistic 4DCT reconstruction and breathing-adapted treatment planning.

State of the art: Monitoring of the respiratory system during veno-venous extracorporeal membrane oxygenation.

Monitoring the patient receiving veno-venous extracorporeal membrane oxygenation (VV ECMO) is challenging due to the complex physiological interplay between native and membrane lung. Understanding these interactions is essential to understand the utility and limitations of different approaches to respiratory monitoring during ECMO. We present a summary of the underlying physiology of native and membrane lung gas exchange and describe different tools for titrating and monitoring gas exchange during ECMO. However, the most important role of VV ECMO in severe respiratory failure is as a means of avoiding further ergotrauma. Although optimal respiratory management during ECMO has not been defined, over the last decade there have been advances in multimodal respiratory assessment which have the potential to guide care. We describe a combination of imaging, ventilator-derived or invasive lung mechanic assessments as a means to individualise management during ECMO.

Flexible Chitosan-Based Capacitive Humidity Sensors for Respiratory Monitoring.

As one of the most important human health indicators, respiratory status is an important basis for the diagnosis of many diseases. However, the high cost of respiratory monitoring makes its use uncommon. This study introduces a low-cost, wearable, flexible humidity sensor for respiratory monitoring. Solution-processed chitosan (CS) placed on a polyethylene terephthalate substrate was used as the sensing layer. An Arduino circuit board was used to read humidity-sensitive voltage changes. The CS-based sensor demonstrated capacitive humidity sensitivity, whereby the capacitance instantly increased from 10-2 to 30 nF when the environmental humidity changed from 43% to 97%. The capacitance logarithm sensitivity and response voltage change was 35.9 pF/%RH and 0.8 V in the RH range from 56% to 97%. And the voltage variation between inhalation and exhalation was ~0.5 V during normal breathing. A rapid response time of ~0.7 s and a recovery time of ~2 s were achieved during respiration testing. Breathing modes (i.e., normal breathing, rest breathing, deep breathing, and fast breathing) and tonal changes during speech could be clearly distinguished. Therefore, such sensors provide a means for economical and convenient wearable respiratory monitoring, and they have the potential to be used for daily health examinations and professional medical diagnoses.

Tracing the Flu Symptom Progression via a Smart Face Mask.

Respiration and body temperature are largely influenced by the highly contagious influenza virus, which poses persistent global public health challenges. Here, we present a wireless all-in-one sensory face mask (WISE mask) made of ultrasensitive fibrous temperature sensors. The WISE mask shows exceptional thermosensitivity, excellent breathability, and wearing comfort. It offers highly sensitive body temperature monitoring and respiratory detection capabilities. Capitalizing on the advances in the Internet of Things and artificial intelligence, the WISE mask is further demonstrated by customized flexible circuitry, deep learning algorithms, and a user-friendly interface to continuously recognize the abnormalities of both the respiration and body temperature. The WISE mask represents a compelling approach to tracing flu symptom progression in a cost-effective and convenient manner, serving as a powerful solution for personalized health monitoring and point-of-care systems in the face of ongoing influenza-related public health concerns.

The evaluation of a non-invasive respiratory monitor in ards patients in supine and prone position.

PURPOSE: The Prone positioning in addition to non invasive respiratory support is commonly used in patients with acute respiratory failure. The aim of this study was to assess the accuracy of an impedance-based non-invasive respiratory volume monitor (RVM) in supine and in prone position. METHODS: In sedated, paralyzed and mechanically ventilated patients in volume-controlled mode with acute respiratory distress syndrome scheduled for prone positioning it was measured and compared non-invasively tidal volume and respiratory rate provided by the RVM in supine and, subsequently, in prone position, by maintaining unchanged the ventilatory setting. RESULTS: Forty patients were enrolled. No significant difference was found between measurements in supine and in prone position either for tidal volume (p = 0.795; p = 0.302) nor for respiratory rate (p = 0.181; p = 0.604). Comparing supine vs. prone position, the bias and limits of agreements for respiratory rate were 0.12 bpm (-1.4 to 1.6) and 20 mL (-80 to 120) for tidal volume. CONCLUSIONS: The RVM is accurate in assessing tidal volume and respiratory rate in prone compared to supine position. Therefore, the RVM could be applied in non-intubated patients with acute respiratory failure receiving prone positioning to monitor respiratory function.

What are standard monitoring devices for anesthesia in future?

Monitoring the patient's physiological functions is critical in clinical anesthesia. The latest version of the Japanese Society of Anesthesiologists' Guidelines for Safe Anesthesia Monitoring, revised in 2019, covers various factors, including electroencephalogram monitoring, oxygenation, ventilation, circulation, and muscle relaxation. However, with recent advances in monitoring technologies, the information provided has become more detailed, requiring practitioners to update their knowledge. At a symposium organized by the Journal of Anesthesia in 2023, experts across five fields discussed their respective topics: anesthesiologists need to interpret not only the values displayed on processed electroencephalogram monitors but also raw electroencephalogram data in the foreseeable future. In addition to the traditional concern of preventing hypoxemia, monitoring for potential hyperoxemia and the effects of mechanical ventilation itself will become increasingly important. The importance of using AI analytics to predict hypotension, assess nociception, and evaluate microcirculation may increase. With the recent increase in the availability of neuromuscular monitoring devices in Japan, it is important for anesthesiologists to become thoroughly familiar with the features of each device to ensure its effective use. There is a growing desire to develop and introduce a well-organized, integrated "single screen" monitor.

Deduced Respiratory Scores on COVID-19 Patients Learning from Exertion-Induced Dyspnea.

Dyspnea is one of the most common symptoms of many respiratory diseases, including COVID-19. Clinical assessment of dyspnea relies mainly on self-reporting, which contains subjective biases and is problematic for frequent inquiries. This study aims to determine if a respiratory score in COVID-19 patients can be assessed using a wearable sensor and if this score can be deduced from a learning model based on physiologically induced dyspnea in healthy subjects. Noninvasive wearable respiratory sensors were employed to retrieve continuous respiratory characteristics with user comfort and convenience. Overnight respiratory waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy subjects with exertion-induced dyspnea was also performed for blind comparison. The learning model was built from the self-reported respiratory features of 32 healthy subjects under exertion and airway blockage. A high similarity between respiratory features in COVID-19 patients and physiologically induced dyspnea in healthy subjects was observed. Learning from our previous dyspnea model of healthy subjects, we deduced that COVID-19 patients have consistently highly correlated respiratory scores in comparison with normal breathing of healthy subjects. We also performed a continuous assessment of the patient's respiratory scores for 12-16 h. This study offers a useful system for the symptomatic evaluation of patients with active or chronic respiratory disorders, especially the patient population that refuses to cooperate or cannot communicate due to deterioration or loss of cognitive functions. The proposed system can help identify dyspneic exacerbation, leading to early intervention and possible outcome improvement. Our approach can be potentially applied to other pulmonary disorders, such as asthma, emphysema, and other types of pneumonia.

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.

A technique to measure tidal volume during noninvasive respiratory support by continuous-flow helmet CPAP.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has promoted the use of helmet continuous positive airway pressure (CPAP) for noninvasive respiratory support in hypoxic respiratory failure patients, despite the lack of tidal volume monitoring. We evaluated a novel technique designed to measure tidal volume during noninvasive continuous-flow helmet CPAP. METHODS: A bench model of spontaneously breathing patients undergoing helmet CPAP therapy (three positive end-expiratory pressure [PEEP] levels) at different levels of respiratory distress was used to compare measured and reference tidal volumes. Tidal volume measurement by the novel technique was based on helmet outflow-trace analysis. Helmet inflow was increased from 60 to 75 and 90 L/min to match the patient's peak inspiratory flow; an additional subset of tests was conducted under the condition of purposely insufficient inflow (i.e., high respiratory distress and 60 L/min inflow). RESULTS: The tidal volumes examined herein ranged from 250 to 910 mL. The Bland‒Altman analysis showed a bias of -3.2 ± 29.3 mL for measured tidal volumes compared to the reference, corresponding to an average relative error of -1 ± 4.4%. Tidal volume underestimation correlated with respiratory rate (rho = .411, p = .004) but not with peak inspiratory flow, distress, or PEEP. When the helmet inflow was maintained purposely low, tidal volume underestimation occurred (bias - 93.3 ± 83.9 mL), corresponding to an error of -14.8 ± 6.3%. CONCLUSION: Tidal volume measurement is feasible and accurate during bench continuous-flow helmet CPAP therapy by the analysis of the outflow signal, provided that helmet inflow is adequate to match the patient's inspiratory efforts. Insufficient inflow resulted in tidal volume underestimation. In vivo data are needed to confirm these findings.

Nasal pressure swings as the measure of inspiratory effort in spontaneously breathing patients with de novo acute respiratory failure.

BACKGROUND: Excessive inspiratory effort could translate into self-inflicted lung injury, thus worsening clinical outcomes of spontaneously breathing patients with acute respiratory failure (ARF). Although esophageal manometry is a reliable method to estimate the magnitude of inspiratory effort, procedural issues significantly limit its use in daily clinical practice. The aim of this study is to describe the correlation between esophageal pressure swings (ΔPes) and nasal (ΔPnos) as a potential measure of inspiratory effort in spontaneously breathing patients with de novo ARF. METHODS: From January 1, 2021, to September 1, 2021, 61 consecutive patients with ARF (83.6% related to COVID-19) admitted to the Respiratory Intensive Care Unit (RICU) of the University Hospital of Modena (Italy) and candidate to escalation of non-invasive respiratory support (NRS) were enrolled. Clinical features and tidal changes in esophageal and nasal pressure were recorded on admission and 24 h after starting NRS. Correlation between ΔPes and ΔPnos served as primary outcome. The effect of ΔPnos measurements on respiratory rate and ΔPes was also assessed. RESULTS: ΔPes and ΔPnos were strongly correlated at admission (R2 = 0.88, p < 0.001) and 24 h apart (R2 = 0.94, p < 0.001). The nasal plug insertion and the mouth closure required for ΔPnos measurement did not result in significant change of respiratory rate and ΔPes. The correlation between measures at 24 h remained significant even after splitting the study population according to the type of NRS (high-flow nasal cannulas [R2 = 0.79, p < 0.001] or non-invasive ventilation [R2 = 0.95, p < 0.001]). CONCLUSIONS: In a cohort of patients with ARF, nasal pressure swings did not alter respiratory mechanics in the short term and were highly correlated with esophageal pressure swings during spontaneous tidal breathing. ΔPnos might warrant further investigation as a measure of inspiratory effort in patients with ARF. TRIAL REGISTRATION: NCT03826797 . Registered October 2016.

Towards Wearable Estimation of Tidal Volume via Electrocardiogram and Seismocardiogram Signals.

The current COVID-19 pandemic highlights the critical importance of ubiquitous respiratory health monitoring. The two fundamental elements of monitoring respiration are respiration rate (the frequency of breathing) and tidal volume (TV, the volume of air breathed by the lungs in each breath). Wearable sensing systems have been demonstrated to provide accurate measurement of respiration rate, but TV remains challenging to measure accurately with wearable and unobtrusive technology. In this work, we leveraged electrocardiogram (ECG) and seismocardiogram (SCG) measurements obtained with a custom wearable sensing patch to derive an estimate of TV from healthy human participants. Specifically, we fused both ECG-derived and SCG-derived respiratory signals (EDR and SDR) and trained a machine learning model with gas rebreathing as the ground truth to estimate TV. The respiration cycle modulates ECG and SCG signals in multiple different ways that are synergistic. Thus, here we extract EDRs and SDRs using a multitude of different demodulation techniques. The extracted features are used to train a subject independent machine learning model to accurately estimate TV. By fusing the extracted EDRs and SDRs, we were able to estimate the TV with a root-mean-square error (RMSE) of 181.45 mL and Pearson correlation coefficient (r) of 0.61, with a global subject-independent model. We further show that SDRs are better TV estimators than EDRs. Among SDRs, amplitude modulated (AM) SCG features are the most correlated to TV. We demonstrated that fusing EDRs and SDRs can result in moderately accurate estimation of TV using a subject-independent model. Additionally, we highlight the most informative features for estimating TV. This work presents a significant step towards achieving continuous, calibration free, and unobtrusive TV estimation, which could advance the state of the art in wearable respiratory monitoring.

An RFID-Based Method for Multi-Person Respiratory Monitoring.

Respiratory monitoring is widely used in the field of health care. Traditional respiratory monitoring methods bring much inconvenience to users. In recent years, a great number of respiratory monitoring methods based on wireless technology have emerged, but multi-person respiratory monitoring is still very challenging; therefore, this paper explores multi-person respiratory monitoring. Firstly, the characteristics of human respiratory movement have been analyzed, and a suitable tag deployment method for respiratory monitoring is proposed. Secondly, aiming at the ambiguity and entanglement of radio frequency identification (RFID) phase data, a method of removal of phase ambiguity and phase wrapping is given. Then, in order to monitor multi-person respiration in a noisy environment, the frequency extraction method and waveform reconstruction method of multi-person respiration are proposed. Finally, the feasibility of the method is verified by experiments.

Respiratory Monitoring by Ultrafast Humidity Sensors with Nanomaterials: A Review.

Respiratory monitoring is a fundamental method to understand the physiological and psychological relationships between respiration and the human body. In this review, we overview recent developments on ultrafast humidity sensors with functional nanomaterials for monitoring human respiration. Key advances in design and materials have resulted in humidity sensors with response and recovery times reaching 8 ms. In addition, these sensors are particularly beneficial for respiratory monitoring by being portable and noninvasive. We systematically classify the reported sensors according to four types of output signals: impedance, light, frequency, and voltage. Design strategies for preparing ultrafast humidity sensors using nanomaterials are discussed with regard to physical parameters such as the nanomaterial film thickness, porosity, and hydrophilicity. We also summarize other applications that require ultrafast humidity sensors for physiological studies. This review provides key guidelines and directions for preparing and applying such sensors in practical applications.

Investigating Stroke Effects on Respiratory Parameters Using a Wearable Device: A Pilot Study on Hemiplegic Patients.

Quantitatively assessing personal health status is gaining increasing attention due to the improvement of diagnostic technology and the increasing occurrence of chronic pathologies. Monitoring physiological parameters allows for retrieving a general overview of the personal health status. Respiratory activity can provide relevant information, especially when pathologies affect the muscles and organs involved in breathing. Among many technologies, wearables may represent a valid solution for continuous and remote monitoring of respiratory activity, thus reducing healthcare costs. The most popular wearables used in this arena are based on detecting the breathing-induced movement of the chest wall. Therefore, their use in patients with impaired chest wall motion and abnormal respiratory kinematics can be challenging, but literature is still in its infancy. This study investigates the performance of a custom wearable device for respiratory monitoring in post-stroke patients. We tested the device on six hemiplegic patients under different respiratory regimes. The estimated respiratory parameters (i.e., respiratory frequency and the timing of the respiratory phase) demonstrated good agreement with the ones provided by a gold standard device. The promising results of this pilot study encourage the exploitation of wearables on these patients that may strongly impact the treatment of chronic diseases, such as hemiplegia.

Optical Monitoring of Breathing Patterns and Tissue Oxygenation: A Potential Application in COVID-19 Screening and Monitoring.

The worldwide outbreak of the novel Coronavirus (COVID-19) has highlighted the need for a screening and monitoring system for infectious respiratory diseases in the acute and chronic phase. The purpose of this study was to examine the feasibility of using a wearable near-infrared spectroscopy (NIRS) sensor to collect respiratory signals and distinguish between normal and simulated pathological breathing. Twenty-one healthy adults participated in an experiment that examined five separate breathing conditions. Respiratory signals were collected with a continuous-wave NIRS sensor (PortaLite, Artinis Medical Systems) affixed over the sternal manubrium. Following a three-minute baseline, participants began five minutes of imposed difficult breathing using a respiratory trainer. After a five minute recovery period, participants began five minutes of imposed rapid and shallow breathing. The study concluded with five additional minutes of regular breathing. NIRS signals were analyzed using a machine learning model to distinguish between normal and simulated pathological breathing. Three features: breathing interval, breathing depth, and O2Hb signal amplitude were extracted from the NIRS data and, when used together, resulted in a weighted average accuracy of 0.87. This study demonstrated that a wearable NIRS sensor can monitor respiratory patterns continuously and non-invasively and we identified three respiratory features that can distinguish between normal and simulated pathological breathing.

What is new in respiratory monitoring?

This paper provides a review of a selection of papers published in the Journal of Clinical Monitoring and Computing in 2020 and 2021 highlighting what is new within the field of respiratory monitoring. Selected papers cover work in pulse oximetry monitoring, acoustic monitoring, respiratory system mechanics, monitoring during surgery, electrical impedance tomography, respiratory rate monitoring, lung ultrasound and detection of patient-ventilator asynchrony.

A capaciflector provides continuous and accurate respiratory rate monitoring for patients at rest and during exercise.

Respiratory rate (RR) is a marker of critical illness, but during hospital care, RR is often inaccurately measured. The capaciflector is a novel sensor that is small, inexpensive, and flexible, thus it has the potential to provide a single-use, real-time RR monitoring device. We evaluated the accuracy of continuous RR measurements by capaciflector hardware both at rest and during exercise. Continuous RR measurements were made with capaciflectors at four chest locations. In healthy subjects (n = 20), RR was compared with strain gauge chest belt recordings during timed breathing and two different body positions at rest. In patients undertaking routine cardiopulmonary exercise testing (CPET, n = 50), RR was compared with pneumotachometer recordings. Comparative RR measurement bias and limits of agreement were calculated and presented in Bland-Altman plots. The capaciflector was shown to provide continuous RR measurements with a bias less than 1 breath per minute (BPM) across four chest locations. Accuracy and continuity of monitoring were upheld even during vigorous CPET exercise, often with narrower limits of agreement than those reported for comparable technologies. We provide a unique clinical demonstration of the capaciflector as an accurate breathing monitor, which may have the potential to become a simple and affordable medical device.Clinical trial number: NCT03832205 https://clinicaltrials.gov/ct2/show/NCT03832205 registered February 6th, 2019.