Respiration rate and volume measurements using wearable strain sensors

Microfluidic Wearable Electrochemical Sensor Based on MOF-Derived Hexagonal Rod-Shaped Porous Carbon for Sweat Metabolite and Electrolyte Analysis

Wearable sensors enable the noninvasive continuous analysis of biofluid, which is of great importance for healthcare monitoring. In this work, a wearable sensor was seamlessly integrated with a microfluidic chip which was prepared by a three-dimensional printing technology for noninvasive and multiplexed analysis of metabolite and electrolytes in human sweat. The microfluidic chip could enable rapid sampling of sweat, which avoids the sweat evaporation and contamination. Using a Zn metal-organic framework as a sacrificial template, the hexagonal rod-shaped porous carbon nanorod (PCN) with high porosity, a large specific surface area, and excellent conductivity was synthesized and exhibited the robust electrocatalytic ability of uric acid (UA) oxidation. Therefore, the PCN-based sensor showed high sensitivity and good selectivity of UA with a wide linear range of 10-200 μM and a low detection limit of 4.13 μM. Meanwhile, the potentiometry-based ion-selective electrode was constructed for detection of pH and K+, respectively, with good sensitivity, selectivity, reproducibility, and stability. In addition, the testing under different bending states demonstrated that mechanical deformation had little effect on the electrochemical performance of the wearable sensors. Furthermore, we evaluated the utility of the wearable sensor for multiplexed real-time analysis of UA, pH, and K+ in sweat during aerobic exercise, and the effect of the amount of consumed purine-rich foods on uric acid metabolite levels in sweat and urine was further investigated. The relationship between urine UA and sweat UA was obtained. Overall, this wearable sensor enables multiple electrolyte and metabolite analysis in different noninvasive biofluids, suggesting its potential application in personalized disease prevention.

Classification of Breathing Phase and Path with In-Ear Microphones

In recent years, the use of smart in-ear devices (hearables) for health monitoring has gained popularity. Previous research on in-ear breath monitoring with hearables uses signal processing techniques based on peak detection. Such techniques are greatly affected by movement artifacts and other challenging real-world conditions. In this study, we use an existing database of various breathing types captured using an in-ear microphone to classify breathing path and phase. Having a small dataset, we use XGBoost, a simple and fast classifier, to address three different classification challenges. We achieve an accuracy of 86.8% for a binary path classifier, 74.1% for a binary phase classifier, and 67.2% for a four-class path and phase classifier. Our path classifier outperforms existing algorithms in recall and F1, highlighting the reliability of our approach. This work demonstrates the feasibility of the use of hearables in continuous breath monitoring tasks with machine learning.

A Novel Wearable Sensor for Measuring Respiration Continuously and in Real Time

In this work, a flexible textile-based capacitive respiratory sensor, based on a capacitive sensor structure, that does not require direct skin contact is designed, optimised, and evaluated using both computational modelling and empirical measurements. In the computational study, the geometry of the sensor was examined. This analysis involved observing the capacitance and frequency variations using a cylindrical model that mimicked the human body. Four designs were selected which were then manufactured by screen printing multiple functional layers on top of a polyester/cotton fabric. The printed sensors were characterised to detect the performance against phantoms and impacts from artefacts, normally present whilst wearing the device. A sensor that has an electrode ratio of 1:3:1 (sensor, reflector, and ground) was shown to be the most sensitive design, as it exhibits the highest sensitivity of 6.2% frequency change when exposed to phantoms. To ensure the replicability of the sensors, several batches of identical sensors were developed and tested using the same physical parameters, which resulted in the same percentage frequency change. The sensor was further tested on volunteers, showing that the sensor measures respiration with 98.68% accuracy compared to manual breath counting.

Wearable Applicability of Respiratory Airflow Transducers: Current Approaches and Future Directions

Advanced technologies employed in modern respiratory airflow transducers have exhibited powerful capabilities in accurately measuring respiratory flow under controlled and sedentary conditions, particularly in clinical settings. However, the wearable applicability of these transducers as face-mounted electronics for use in occupational and sporting activities remains unexplored. The present review addresses the critical wearability issue associated with current respiratory airflow transducers, including pneumotachographs, orifice flowmeters, turbine flowmeters, hot wire anemometers, ultrasound flowmeters, and piezoelectric airflow transducers. Furthermore, a comprehensive analysis and comparison of all factors that impact the wearable applicability of respiratory airflow transducers are conducted, considering dynamic accuracy, long-term usability, power consumption, calibration frequency, and cleaning requirements. The findings indicate that the piezoelectric airflow transducer stands out as a more viable option for wearables compared to other devices. We expect that this review will serve as a valuable engineering reference, guiding future research efforts in designing and developing wearable respiratory airflow transducers for ambulatory respiratory flow monitoring.

A noval transparent triboelectric nanogenerator as electronic skin for real-time breath monitoring

Against the backdrop of advancements in modern multifunctional wearable electronics, there is a growing demand for simple, sustainable, and portable electronic skin (e-skin), posing significant challenges. This study aims to delineate the development of a straightforward, transparent, highly sensitive, and high power-density electronic skin based on a triboelectric nanogenerator(S-TENG), designed for harvesting human body energy and real-time monitoring of the physiological motion status. Our e-skin incorporates thermally treated polyvinylidene fluoride (PVDF) fiber membranes as the contact layer and a film of silver nanowires as the conductive electrodes. The resulting contact-separation type e-skin exhibits an impressive transparency of 80 %, along with a nice sensitivity value, capable of detecting a light touch from a 0.13 g sponge and demonstrating good working stability and breathability. Leveraging the triboelectric effect, our e-skin generates an open-circuit voltage of 301 V and a short-circuit current of 2.7 μA under an extrinsic force of 8 N over an interaction area of 4 × 4 cm2, achieving a power density up to 306 mW/m2. With its signal processing circuitry, the integrated S-TENG showcases nice energy harvesting and signal transmission capabilities. Accordingly, we contend that S-TENG has potential applications in energy capture and real-time human motion state monitoring. This research is anticipated to blaze a novel and practical trail for self-powered wearable devices and personalized health rehabilitation training regimens.

Nanocomposite Multimodal Sensor Array Integrated with Auxetic Structure for an Intelligent Biometrics System

A multimodal sensor array, combining pressure and proximity sensing, has attracted considerable interest due to its importance in ubiquitous monitoring of cardiopulmonary health- and sleep-related biometrics. However, the sensitivity and dynamic range of prevalent sensors are often insufficient to detect subtle body signals. This study introduces a novel capacitive nanocomposite proximity-pressure sensor (NPPS) for detecting multiple human biometrics. NPPS consists of a carbon nanotube-paper composite (CPC) electrode and a percolating multiwalled carbon nanotube (MWCNT) foam enclosed in a MWCNT-coated auxetic frame. The fractured fibers in the CPC electrode intensify an electric field, enabling highly sensitive detection of proximity and pressure. When pressure is applied to the sensor, the synergic effect of MWCNT foam and auxetic deformation amplifies the sensitivity. The simple and mass-producible fabrication protocol allows for building an array of highly sensitive sensors to monitor human presence, sleep posture, and vital signs, including ballistocardiography (BCG). With the aid of a machine learning algorithm, the sensor array accurately detects blood pressure (BP) without intervention. This advancement holds promise for unrestricted vital sign monitoring during sleep or driving.

Estimation of ventilatory thresholds during exercise using respiratory wearable sensors

Ventilatory thresholds (VTs) are key physiological parameters used to evaluate physical performance and determine aerobic and anaerobic transitions during exercise. Current assessment of these parameters requires ergospirometry, limiting evaluation to laboratory or clinical settings. In this work, we introduce a wearable respiratory system that continuously tracks breathing during exercise and estimates VTs during ramp tests. We validate the respiratory rate and VTs predictions in 17 healthy adults using ergospirometry analysis. In addition, we use the wearable system to evaluate VTs in 107 recreational athletes during ramp tests outside the laboratory and show that the mean population values agree with physiological variables traditionally used to exercise prescription. We envision that respiratory wearables can be useful in determining aerobic and anaerobic parameters with promising applications in health telemonitoring and human performance.

Prospective observational study of 2 wearable strain sensors for measuring the respiratory rate

The respiratory rate is an important factor for assessing patient status and detecting changes in the severity of illness. Real-time determination of the respiratory rate will enable early responses to changes in the patient condition. Several methods of wearable devices have enabled remote respiratory rate monitoring. However, gaps persist in large-scale validation, patient-specific calibration, standardization and their usefulness in clinical practice has not been fully elucidated. The aim of this study was to evaluate the accuracy of 2 wearable stretch sensors, C-STRECH® which is used in clinical practice and a novel stretchable capacitor in measuring the respiratory rate. The respiratory rate of 20 healthy subjects was measured by a spirometer with the stretch sensor applied to 1 of 5 locations (umbilicus, lateral abdomen, epigastrium, lateral chest, or chest) of their body at rest while they were in a sitting or supine position before or after exercise. The sensors detected the largest amplitudes at the epigastrium and umbilicus compared to other sites of measurement for the sitting and supine positions, respectively. At rest, the respiratory rate of the sensors had an error of 0.06 to 2.39 breaths/minute, whereas after exercise, an error of 1.57 to 3.72 breaths/minute was observed compared to the spirometer. The sensors were able to detect the respiratory rate of healthy volunteers in the sitting and supine positions, but there was a need for improvement in detection after exercise.

SolunumWear: A smart textile system for dynamic respiration monitoring across various postures

We introduce SolunumWear, a multi-sensory e-textile system designed for respiration in daily life settings, addressing the gap in continuous, real-world respiration event monitoring. Leveraging a textile pressure sensor belt to capture chest movements and a wireless data acquisition system, SolunumWear offers a promising solution for both medical and wellness applications. The system's efficacy was evaluated through a human study involving 10 healthy adults (six female and four male) across various breathing rates and postures, demonstrating a strong correlation (R value = 0.836) with the gold-standard system. The study highlights the system's computational and communication efficiencies, with latencies of approximately 4.84 s and 2.13 ms, respectively. These findings highlight the efficacy of SolunumWear as a wireless, wearable technology for respiration monitoring in daily settings. This research contributes to the expanding body of knowledge on smart textile-based health monitoring technologies, demonstrating its potential to provide reliable respiratory data in real-world environments.

Breathing Monitoring in Soccer: Part I-Validity of Commercial Wearable Sensors

Growing evidence suggests that respiratory frequency (fR) is a valid marker of effort during high-intensity exercise, including sports of an intermittent nature, like soccer. However, very few attempts have been made so far to monitor fR in soccer with unobtrusive devices. This study assessed the validity of three strain-based commercial wearable devices measuring fR during soccer-specific movements. On two separate visits to the soccer pitch, 15 players performed a 30 min validation protocol wearing either a ComfTech® (CT) vest or a BioharnessTM (BH) 3.0 strap and a Tyme WearTM (TW) vest. fR was extracted from the respiratory waveform of the three commercial devices with custom-made algorithms and compared with that recorded with a reference face mask. The fR time course of the commercial devices generally resembled that of the reference system. The mean absolute percentage error was, on average, 7.03% for CT, 8.65% for TW, and 14.60% for BH for the breath-by-breath comparison and 1.85% for CT, 3.27% for TW, and 7.30% for BH when comparison with the reference system was made in 30 s windows. Despite the challenging measurement scenario, our findings show that some of the currently available wearable sensors are indeed suitable to unobtrusively measure fR in soccer.

Wireless and Battery-Free Sensor for Interstitial Fluid Pressure Monitoring

Congestive heart failure (CHF) is a fatal disease with progressive severity and no cure; the heart's inability to adequately pump blood leads to fluid accumulation and frequent hospital readmissions after initial treatments. Therefore, it is imperative to continuously monitor CHF patients during its early stages to slow its progression and enable timely medical interventions for optimal treatment. An increase in interstitial fluid pressure (IFP) is indicative of acute CHF exacerbation, making IFP a viable biomarker for predicting upcoming CHF if continuously monitored. In this paper, we present an inductor-capacitor (LC) sensor for subcutaneous wireless and continuous IFP monitoring. The sensor is composed of inexpensive planar copper coils defined by a simple craft cutter, which serves as both the inductor and capacitor. Because of its sensing mechanism, the sensor does not require batteries and can wirelessly transmit pressure information. The sensor has a low-profile form factor for subcutaneous implantation and can communicate with a readout device through 4 layers of skin (12.7 mm thick in total). With a soft silicone rubber as the dielectric material between the copper coils, the sensor demonstrates an average sensitivity as high as -8.03 MHz/mmHg during in vitro simulations.

The model of the brain as a complex system: Interactions of physical, neural and mental states with neurocognitive functions

The isolated approaching of physical, neural and mental states and the binary classification into stable traits and fluctuating states previously lead to a limited understanding concerning underlying processes and possibilities to explain, measure and regulate neural and mental performance along with the interaction of mental states and neurocognitive traits. In this article these states are integrated by i) differentiating the model of the brain as a complex, self-organizing system, ii) showing possibilities to measure this model, iii) offering a classification of mental states and iv) presenting a holistic operationalization of state regulations and trait trainings to enhance neural and mental high-performance on a macro- and micro scale. This model integrates current findings from the theory of constructed emotions, the theory of thousand brains and complex systems theory and yields several testable hypotheses to provide an integrated reference frame for future research and applied target points to regulate and enhance performance.

Fabrication of a Capacitive 3D Spacer Fabric Pressure Sensor with a Dielectric Constant Change for High Sensitivity

Smart wearable sensors are increasingly integrated into everyday life, interfacing with the human body to enable real-time monitoring of biological signals. This study focuses on creating high-sensitivity capacitive-type sensors by impregnating polyester-based 3D spacer fabric with a Carbon Nanotube (CNT) dispersion. The unique properties of conductive particles lead to nonlinear variations in the dielectric constant when pressure is applied, consequently affecting the gauge factor. The results reveal that while the fabric without CNT particles had a gauge factor of 1.967, the inclusion of 0.04 wt% CNT increased it significantly to 5.210. As sensor sensitivity requirements vary according to the application, identifying the necessary CNT wt% is crucial. Artificial intelligence, particularly the Multilayer Perception (MLP) model, enables nonlinear regression analysis for this purpose. The MLP model created and validated in this research showed a high correlation coefficient of 0.99564 between the model predictions and actual target values, indicating its effectiveness and reliability.

An elaboration on sample size determination for correlations based on effect sizes and confidence interval width: a guide for researchers

Objectives

This paper aims to serve as a useful guide for sample size determination for various correlation analyses that are based on effect sizes and confidence interval width.

Materials and Methods

Sample size determinations are calculated for Pearson's correlation, Spearman's rank correlation, and Kendall's Tau-b correlation. Examples of sample size statements and their justification are also included.

Results

Using the same effect sizes, there are differences between the sample size determination of the 3 statistical tests. Based on an empirical calculation, a minimum sample size of 149 is usually adequate for performing both parametric and non-parametric correlation analysis to determine at least a moderate to an excellent degree of correlation with acceptable confidence interval width.

Conclusions

Determining data assumption(s) is one of the challenges to offering a valid technique to estimate the required sample size for correlation analyses. Sample size tables are provided and these will help researchers to estimate a minimum sample size requirement based on correlation analyses.

Crosstalk-free graphene-liquid elastomer based printed sensors for unobtrusive respiratory monitoring

Flexible strain sensors have garnered attraction in the human healthcare domain. However, caveats like crosstalk and noise associated with the output signal of such a sensor often limit the accuracy. Hence, developing a strain sensor via frugal engineering is critical, thereby warranting its mass utility. A stencil printable graphene/liquid elastomeric crosstalk-free strain sensor for unobtrusive respiratory monitoring is reported herein. Printing supports the frugality of the process and avoids complex fabrication. The sensor was mounted on a wearable mask, and the sensor console was fabricated. The console demonstrated the capability to detect the respiratory profile at room and low temperature (-26 °C) with an SNR of -12.85 dB. Developed sensors could nullify the impact of temperature and humidity and generate respiratory signals due to strain induced by breathing. A model experiment was conducted to support the fidelity of the strain mechanism. The console demonstrated excellent stability (over 500 cycles) with a sensitivity of -196.56 (0-0.17% strain) and 117.49 (0.17-0.34% strain). The console could accurately determine conditions like eupnea, tachypnoea, etc., and transmit the data wirelessly via Bluetooth. These findings solve major caveats in flexible sensor development by focusing on selectivity, sensitivity, and stability.

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

Novel statistical time series data augmentation and machine learning based classification of unobtrusive respiration data for respiration Digital Twin model

Digital Twin (DT), a concept of Healthcare (4.0), represents the subject's biological properties and characteristics in a digital model. DT can help in monitoring respiratory failures, enabling timely interventions, personalized treatment plans to improve healthcare, and decision-support for healthcare professionals. Large-scale implementation of DT technology requires extensive patient data for accurate monitoring and decision-making with Machine Learning (ML) and Deep Learning (DL). Initial respiration data was collected unobtrusively with the ESP32 Wi-Fi Channel State Information (CSI) sensor. Due to limited respiration data availability, the paper proposes a novel statistical time series data augmentation method for generating larger synthetic respiration data. To ensure accuracy and validity in the augmentation method, correlation methods (Pearson, Spearman, and Kendall) are implemented to provide a comparative analysis of experimental and synthetic datasets. Data processing methodologies of denoising (smoothing and filtering) and dimensionality reduction with Principal Component Analysis (PCA) are implemented to estimate a patient's Breaths Per Minute (BPM) from raw respiration sensor data and the synthetic version. The methodology provided the BPM estimation accuracy of 92.3% from raw respiration data. It was observed that out of 27 supervised classifications with k-fold cross-validation, the Bagged Tree ensemble algorithm provided the best ML-supervised classification. In the case of binary-class and multi-class, the Bagged Tree ensemble showed accuracies of 89.2% and 83.7% respectively with combined real and synthetic respiration dataset with the larger synthetic dataset. Overall, this provides a blueprint of methodologies for the development of the respiration DT model.

Clinical human activity recognition based on a wearable patch of combined tri-axial ACC and ECG sensors

Background

In digital medicine, human activity recognition (HAR) can be used to track and assess a patient's progress throughout rehabilitation, enhancing the quality of life for the elderly and the disabled.

Methods

A patch-type flexible sensor that integrated dynamic electrocardiogram (ECG) and acceleration signal (ACC) was used to record the signals of the various behavioral activities of 20 healthy volunteers and 25 patients with pneumoconiosis. Seven HAR tasks were then carried out on the data using four different deep learning methods (CNN, LSTM, CNN-LSTM and GRU).

Results

When ECG and ACC were obtained simultaneously, the overall accuracy rates of HAR for healthy group were 0.9371, 0.8829, 0.9843 and 0.9486 by the CNN, LSTM, CNN-LSTM and GRU models, respectively. In contrast, the overall accuracy rates of HAR for the pneumoconiosis patients' group were 0.8850, 0.7975, 0.9425 and 0.8525 by the four corresponding models. The accuracy of HAR for both groups using all four models is higher than when only ACC signal is detected.

Conclusion

The addition of the ECG signal significantly improves HAR outcomes in the group of healthy individuals, while having relatively less enhancing effects on the group of patients with pneumoconiosis. When ECG and ACC signals were combined, the increase in HAR accuracy was notable compared to cases where no ECG data was provided. These results suggest that the combination of ACC and ECG data can represent a novel method for the clinical application of HAR.

Materials, Designs, and Implementations of Wearable Antennas and Circuits for Biomedical Applications: A Review

The intersection of biomedicine and radio frequency (RF) engineering has fundamentally transformed self-health monitoring by leveraging soft and wearable electronic devices. This paradigm shift presents a critical challenge, requiring these devices and systems to possess exceptional flexibility, biocompatibility, and functionality. To meet these requirements, traditional electronic systems, such as sensors and antennas made from rigid and bulky materials, must be adapted through material science and schematic design. Notably, in recent years, extensive research efforts have focused on this field, and this review article will concentrate on recent advancements. We will explore the traditional/emerging materials for highly flexible and electrically efficient wearable electronics, followed by systematic designs for improved functionality and performance. Additionally, we will briefly overview several remarkable applications of wearable electronics in biomedical sensing. Finally, we provide an outlook on potential future directions in this developing area.

Digital technologies for step counting: between promises of reliability and risks of reductionism

Step counting is among the fundamental features of wearable technology, as it grounds several uses of wearables in biomedical research and clinical care, is at the center of emerging public health interventions and recommendations, and is gaining increasing scientific and political importance. This paper provides a perspective of step counting in wearable technology, identifying some limitations to the ways in which wearable technology measures steps and indicating caution in current uses of step counting as a proxy for physical activity. Based on an overview of the current state of the art of technologies and approaches to step counting in digital wearable technologies, we discuss limitations that are methodological as well as epistemic and ethical-limitations to the use of step counting as a basis to build scientific knowledge on physical activity (epistemic limitations) as well as limitations to the accessibility and representativity of these tools (ethical limitations). As such, using step counting as a proxy for physical activity should be considered a form of reductionism. This is not per se problematic, but there is a need for critical appreciation and awareness of the limitations of reductionistic approaches. Perspective research should focus on holistic approaches for better representation of physical activity levels and inclusivity of different user populations.

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.

Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19

The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions.

Sensor Selection for Tidal Volume Determination via Linear Regression-Impact of Lasso versus Ridge Regression

The measurement of respiratory volume based on upper body movements by means of a smart shirt is increasingly requested in medical applications. This research used upper body surface motions obtained by a motion capture system, and two regression methods to determine the optimal selection and placement of sensors on a smart shirt to recover respiratory parameters from benchmark spirometry values. The results of the two regression methods (Ridge regression and the least absolute shrinkage and selection operator (Lasso)) were compared. This work shows that the Lasso method offers advantages compared to the Ridge regression, as it provides sparse solutions and is more robust to outliers. However, both methods can be used in this application since they lead to a similar sensor subset with lower computational demand (from exponential effort for full exhaustive search down to the order of O (n2)). A smart shirt for respiratory volume estimation could replace spirometry in some cases and would allow for a more convenient measurement of respiratory parameters in home care or hospital settings.

An Efficient Ambient-Moisture-Driven Wearable Electrical Power Generator

Existing devices for generating electrical power from water vapor in ambient air require high levels of relative humidity (RH), cannot operate for prolonged periods, and provide insufficient output for most practical applications. Here a heterogeneous moisture-driven electrical power generator (MODEG) is developed in the form of a free-standing bilayer of polyelectrolyte films, one consisting of a hygroscopic matrix of graphene oxide(GO)/polyaniline(PANI) [(GO)PANI] and the other consisting of poly(diallyldimethylammonium chloride)(PDDA)-modified fluorinated Nafion (F-Nafion (PDDA)). One MODEG unit (1 cm2 ) can deliver a stable open-circuit output of 0.9 V at 8 µA for more than 10 h with a matching external load. The device works over a wide range of temperature (-20 to +50 °C) and relative humidity (30% to 95% RH). It is shown that series and parallel combinations of MODEG units can directly supply sufficient power to drive commercial electronic devices such as light bulbs, supercapacitors, circuit boards, and screen displays. The (GO)PANI:F-Nafion (PDDA) hybrid film is embedded in a mask to harvest the energy from exhaled water vapor in human breath under real-life conditions. The device could consistently generate 450-600 mV during usual breathing, and provides sufficient power to drive medical devices, wearables, and emergency communication.

Simulation of a new respiratory phase sorting method for 4D-imaging using optical surface information towards precision radiotherapy

BACKGROUND

Respiratory signal detection is critical for 4-dimensional (4D) imaging. This study proposes and evaluates a novel phase sorting method using optical surface imaging (OSI), aiming to improve the precision of radiotherapy.

METHOD

Based on 4D Extended Cardiac-Torso (XCAT) digital phantom, OSI in point cloud format was generated from the body segmentation, and image projections were simulated using the geometries of Varian 4D kV cone-beam-CT (CBCT). Respiratory signals were extracted respectively from the segmented diaphragm image (reference method) and OSI respectively, where Gaussian Mixture Model and Principal Component Analysis (PCA) were used for image registration and dimension reduction respectively. Breathing frequencies were compared using Fast-Fourier-Transform. Consistency of 4DCBCT images reconstructed using Maximum Likelihood Expectation Maximization algorithm was also evaluated quantitatively, where high consistency can be suggested by lower Root-Mean-Square-Error (RMSE), Structural-Similarity-Index (SSIM) value closer to 1, and larger Peak-Signal-To-Noise-Ratio (PSNR) respectively.

RESULTS

High consistency of breathing frequencies was observed between the diaphragm-based (0.232 Hz) and OSI-based (0.251 Hz) signals, with a slight discrepancy of 0.019Hz. Using end of expiration (EOE) and end of inspiration (EOI) phases as examples, the mean±1SD values of the 80 transverse, 100 coronal and 120 sagittal planes were 0.967, 0,972, 0.974 (SSIM); 1.657 ± 0.368, 1.464 ± 0.104, 1.479 ± 0.297 (RMSE); and 40.501 ± 1.737, 41.532 ± 1.464, 41.553 ± 1.910 (PSNR) for the EOE; and 0.969, 0.973, 0.973 (SSIM); 1.686 ± 0.278, 1.422 ± 0.089, 1.489 ± 0.238 (RMSE); and 40.535 ± 1.539, 41.605 ± 0.534, 41.401 ± 1.496 (PSNR) for EOI respectively.

CONCLUSIONS

This work proposed and evaluated a novel respiratory phase sorting approach for 4D imaging using optical surface signals, which can potentially be applied to precision radiotherapy. Its potential advantages were non-ionizing, non-invasive, non-contact, and more compatible with various anatomic regions and treatment/imaging systems.

Optimal Positioning of Inertial Measurement Units in a Smart Shirt for Determining Respiratory Volume

Tidal volume can be estimated using the surface motions of the upper body induced by respiration. However, the precision and instrumentation of such estimation must be improved to allow widespread application. In this study, respiration induced changes in parameters that can be recorded with inertial measurement units are examined to determine tidal volumes. Based on the data of an optical motion capture system, the optimal positions of inertial measurement units (IMU) in a smart shirt for sets of 4, 5 or 6 sensors were determined. The errors observed indicate the potential to determine tidal volumes using IMUs in a smart shirt.Clinical Relevance- The measurement of respiratory volumes via a low-cost and unobtrusive smart shirt would be a major advance in clinical diagnostics. In particular, conventional methods are expensive, and uncomfortable for conscious patients if measurement is desired over an extended period. A smart-shirt based on inertial sensors would allow a comfortable measurement and could be used in many clinical scenarios - from sleep apnoea monitoring to homecare and respiratory monitoring of comatose patients.

An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions

PURPOSE

Breathing parameters change with activity and posture, but currently available solutions can perform measurements only during static conditions.

METHODS

This article presents an innovative wearable sensor system constituted by three inertial measurement units to simultaneously estimate respiratory rate (RR) in static and dynamic conditions and perform human activity recognition (HAR) with the same sensing principle. Two units are aimed at detecting chest wall breathing-related movements (one on the thorax, one on the abdomen); the third is on the lower back. All units compute the quaternions describing the subject's movement and send data continuously with the ANT transmission protocol to an app. The 20 healthy subjects involved in the research (9 men, 11 women) were between 23 and 54 years old, with mean age 26.8, mean height 172.5 cm and mean weight 66.9 kg. Data from these subjects during different postures or activities were collected and analyzed to extract RR.

RESULTS

Statistically significant differences between dynamic activities ("walking slow", "walking fast", "running" and "cycling") and static postures were detected (p < 0.05), confirming the obtained measurements are in line with physiology even during dynamic activities. Data from the reference unit only and from all three units were used as inputs to artificial intelligence methods for HAR. When the data from the reference unit were used, the Gated Recurrent Unit was the best performing method (97% accuracy). With three units, a 1D Convolutional Neural Network was the best performing (99% accuracy).

CONCLUSION

Overall, the proposed solution shows it is possible to perform simultaneous HAR and RR measurements in static and dynamic conditions with the same sensor system.

Interoceptive Awareness of the Breath Preserves Attention and Language Networks amidst Widespread Cortical Deactivation: A Within-Participant Neuroimaging Study

Interoception, the representation of the body's internal state, serves as a foundation for emotion, motivation, and wellbeing. Yet despite its centrality in human experience, the neural mechanisms of interoceptive attention are poorly understood. The Interoceptive/Exteroceptive Attention Task (IEAT) is a novel neuroimaging paradigm that compares behavioral tracking of the respiratory cycle (Active Interoception) to tracking of a visual stimulus (Active Exteroception). Twenty-two healthy participants completed the IEAT during two separate scanning sessions (N = 44) as part of a randomized control trial of mindful awareness in body-oriented therapy (MABT). Compared with Active Exteroception, Active Interoception deactivated somatomotor and prefrontal regions. Greater self-reported interoceptive sensibility (MAIA scale) predicted sparing from deactivation within the anterior cingulate cortex (ACC) and left-lateralized language regions. The right insula, typically described as a primary interoceptive cortex, was only specifically implicated by its deactivation during an exogenously paced respiration condition (Active Matching) relative to self-paced Active Interoception. Psychophysiological interaction (PPI) analysis characterized Active Interoception as promoting greater ACC connectivity with lateral prefrontal and parietal regions commonly referred to as the dorsal attention network (DAN). In contrast to evidence relating accurate detection of liminal interoceptive signals such as the heartbeat to anterior insula activity, interoceptive attention toward salient signals such as the respiratory cycle may involve reduced cortical activity but greater ACC-DAN connectivity, with greater sensibility linked to reduced deactivation within the ACC and language-processing regions.

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.

A systematic review of neurophysiological sensing for the assessment of acute pain

Pain is a complex and personal experience that presents diverse measurement challenges. Different sensing technologies can be used as a surrogate measure of pain to overcome these challenges. The objective of this review is to summarise and synthesise the published literature to: (a) identify relevant non-invasive physiological sensing technologies that can be used for the assessment of human pain, (b) describe the analytical tools used in artificial intelligence (AI) to decode pain data collected from sensing technologies, and (c) describe the main implications in the application of these technologies. A literature search was conducted in July 2022 to query PubMed, Web of Sciences, and Scopus. Papers published between January 2013 and July 2022 are considered. Forty-eight studies are included in this literature review. Two main sensing technologies (neurological and physiological) are identified in the literature. The sensing technologies and their modality (unimodal or multimodal) are presented. The literature provided numerous examples of how different analytical tools in AI have been applied to decode pain. This review identifies different non-invasive sensing technologies, their analytical tools, and the implications for their use. There are significant opportunities to leverage multimodal sensing and deep learning to improve accuracy of pain monitoring systems. This review also identifies the need for analyses and datasets that explore the inclusion of neural and physiological information together. Finally, challenges and opportunities for designing better systems for pain assessment are also presented.

Fitbit Data to Assess Functional Capacity in Patients Before Elective Surgery: Pilot Prospective Observational Study

BACKGROUND

Preoperative assessment is crucial to prevent the risk of complications of surgical operations and is usually focused on functional capacity. The increasing availability of wearable devices (smartwatches, trackers, rings, etc) can provide less intrusive assessment methods, reduce costs, and improve accuracy.

OBJECTIVE

The aim of this study was to present and evaluate the possibility of using commercial smartwatch data, such as those retrieved from the Fitbit Inspire 2 device, to assess functional capacity before elective surgery and correlate such data with the current gold standard measure, the 6-Minute Walk Test (6MWT) distance.

METHODS

During the hospital visit, patients were evaluated in terms of functional capacity using the 6MWT. Patients were asked to wear the Fitbit Inspire 2 for 7 days (with flexibility of -2 to +2 days) after the hospital visit, before their surgical operation. Resting heart rate and daily steps data were retrieved directly from the smartwatch. Feature engineering techniques allowed the extraction of heart rate over steps (HROS) and a modified version of Non-Exercise Testing Cardiorespiratory Fitness. All measures were correlated with 6MWT.

RESULTS

In total, 31 patients were enrolled in the study (n=22, 71% men; n=9, 29% women; mean age 76.06, SD 4.75 years). Data were collected between June 2021 and May 2022. The parameter that correlated best with the 6MWT was the Non-Exercise Testing Cardiorespiratory Fitness index (r=0.68; P<.001). The average resting heart rate over the whole acquisition period for each participant had r=-0.39 (P=.03), even if some patients did not wear the device at night. The correlation of the 6MWT distance with the HROS evaluated at 1% quantile was significant, with Pearson coefficient of -0.39 (P=.04). Fitbit step count had a fair correlation of 0.59 with 6MWT (P<.001).

CONCLUSIONS

Our study is a promising starting point for the adoption of wearable technology in the evaluation of functional capacity of patients, which was strongly correlated with the gold standard. The study also identified limitations in the availability of metrics, variability of devices, accuracy and quality of data, and accessibility as crucial areas of focus for future studies.

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.

Miniaturized retractable thin-film sensor for wearable multifunctional respiratory monitoring

As extremely important physiological indicators, respiratory signals can often reflect or predict the depth and urgency of various diseases. However, designing a wearable respiratory monitoring system with convenience, excellent durability, and high precision is still an urgent challenge. Here, we designed an easy-fabricate, lightweight, and badge reel-like retractable self-powered sensor (RSPS) with high precision, sensitivity, and durability for continuous detection of important indicators such as respiratory rate, apnea, and respiratory ventilation. By using three groups of interdigital electrode structures with phase differences, combined with flexible printed circuit boards (FPCBs) processing technology, a miniature rotating thin-film triboelectric nanogenerator (RTF-TENG) was developed. Based on discrete sensing technology, the RSPS has a sensing resolution of 0.13 mm, sensitivity of 7 P·mm-1, and durability more than 1 million stretching cycles, with low hysteresis and excellent anti-environmental interference ability. Additionally, to demonstrate its wearability, real-time, and convenience of respiratory monitoring, a multifunctional wearable respiratory monitoring system (MWRMS) was designed. The MWRMS demonstrated in this study is expected to provide a new and practical strategy and technology for daily human respiratory monitoring and clinical diagnosis.

Electronic Supplementary Material

Supplementary material (additional figures and movies, including the production process of respiratory monitoring straps, the mechanical analysis of RSPS, RTF-TENG versus vector TENG sensors, the simulation studies of TE-TENG and FT-TENG, the additional characterization of RTF-TENG, the tensile and robustness tests of RSPS, the characterizations of the MWRMS during different sleeping positions, detailed circuit schematic of the MWRMS, the displacements and phase relations of RSPS, MWRMS for multifunctional respiratory monitoring) is available in the online version of this article at 10.1007/s12274-023-5420-1.

Hydrogel-Integrated Multimodal Response as a Wearable and Implantable Bidirectional Interface for Biosensor and Therapeutic Electrostimulation

A hydrogel that fuses long-term biologic integration, multimodal responsiveness, and therapeutic functions has received increasing interest as a wearable and implantable sensor but still faces great challenges as an all-in-one sensor by itself. Multiple bonding with stimuli response in a biocompatible hydrogel lights up the field of soft hydrogel interfaces suitable for both wearable and implantable applications. Given that, we proposed a strategy of combining chemical cross-linking and stimuli-responsive physical interactions to construct a biocompatible multifunctional hydrogel. In this hydrogel system, ureidopyrimidinone/tyramine (Upy/Tyr) difunctionalization of gelatin provides abundant dynamic physical interactions and stable covalent cross-linking; meanwhile, Tyr-doped poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) acts as a conductive filler to establish electrical percolation networks through enzymatic chemical cross-linking. Thus, the hydrogel is characterized with improved conductivity, conformal biointegration features (i.e., high stretchability, rapid self-healing, and excellent tissue adhesion), and multistimuli-responsive conductivity (i.e., temperature and urea). On the basis of these excellent performances, the prepared multifunctional hydrogel enables multimodal wearable sensing integration that can simultaneously track both physicochemical and electrophysiological attributes (i.e., motion, temperature, and urea), providing a more comprehensive monitoring of human health than current wearable monitors. In addition, the electroactive hydrogel here can serve as a bidirectional neural interface for both neural recording and therapeutic electrostimulation, bringing more opportunities for nonsurgical diagnosis and treatment of diseases.

Characterisation and Quantification of Upper Body Surface Motions for Tidal Volume Determination in Lung-Healthy Individuals

Measurement of accurate tidal volumes based on respiration-induced surface movements of the upper body would be valuable in clinical and sports monitoring applications, but most current methods lack the precision, ease of use, or cost effectiveness required for wide-scale uptake. In this paper, the theoretical ability of different sensors, such as inertial measurement units, strain gauges, or circumference measurement devices to determine tidal volumes were investigated, scrutinised and evaluated. Sixteen subjects performed different breathing patterns of different tidal volumes, while using a motion capture system to record surface motions and a spirometer as a reference to obtain tidal volumes. Subsequently, the motion-capture data were used to determine upper-body circumferences, tilt angles, distance changes, movements and accelerations-such data could potentially be measured using optical encoders, inertial measurement units, or strain gauges. From these parameters, the measurement range and correlation with the volume signal of the spirometer were determined. The highest correlations were found between the spirometer volume and upper body circumferences; surface deflection was also well correlated, while accelerations carried minor respiratory information. The ranges of thorax motion parameters measurable with common sensors and the values and correlations to respiratory volume are presented. This article thus provides a novel tool for sensor selection for a smart shirt analysis of respiration.

Home monitoring in interstitial lung diseases

The widespread use of smartphones and the internet has enabled self-monitoring and more hybrid-care models. The COVID-19 pandemic has further accelerated remote monitoring, including in the heterogenous and often vulnerable group of patients with interstitial lung diseases (ILDs). Home monitoring in ILD has the potential to improve access to specialist care, reduce the burden on health-care systems, improve quality of life for patients, identify acute and chronic disease worsening, guide treatment decisions, and simplify clinical trials. Home spirometry has been used in ILD for several years and studies with other devices (such as pulse oximeters, activity trackers, and cough monitors) have emerged. At the same time, challenges have surfaced, including technical, analytical, and implementational issues. In this Series paper, we provide an overview of experiences with home monitoring in ILD, address the challenges and limitations for both care and research, and provide future perspectives. VIDEO ABSTRACT.

Ultrasensitive Strain Sensor Utilizing a AgF-AgNW Hybrid Nanocomposite for Breath Monitoring and Pulmonary Function Analysis

Breath monitoring and pulmonary function analysis have been the prime focus of wearable smart sensors owing to the COVID-19 outbreak. Currently used lung function meters in hospitals are prone to spread the virus and can result in the transmission of the disease. Herein, we have reported the first-ever wearable patch-type strain sensor for enabling real-time lung function measurements (such as forced volume capacity (FVC) and forced expiratory volume (FEV) along with breath monitoring), which can avoid the spread of the virus. The noninvasive and highly sensitive strain sensor utilizes the synergistic effect of two-dimensional (2D) silver flakes (AgFs) and one-dimensional (1D) silver nanowires (AgNWs), where AgFs create multiple electron transmission paths and AgNWs generate percolation networks in the nanocomposite. The nanocomposite-based strain sensor possesses a high optimized conductivity of 7721 Sm^-1 (and a maximum conductivity of 83,836 Sm^-1), excellent stretchability (>1000%), and ultrasensitivity (GFs of 35 and 87 when stretched 0-20 and 20-50%, respectively), thus enabling reliable detection of small strains produced by the body during breathing and other motions. The sensor patching site was optimized to accurately discriminate between normal breathing, quick breathing, and deep breathing and analyze numerous pulmonary functions, including the respiratory rate, peak flow, FVC, and FEV. Finally, the observed measurements for different pulmonary functions were compared with a commercial peak flow meter and a spirometer, and a high correlation was observed, which highlights the practical feasibility of continuous respiratory monitoring and pulmonary function analysis.

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.

Wearable Plant Sensor for In Situ Monitoring of Volatile Organic Compound Emissions from Crops

Methanol is a major volatile organic compound (VOC) emitted from plants. Methanol emission reflects indirect plant defense against insects, promotes cell-to-cell communication, and adapts plants to various environmental stresses. This paper reports a wearable plant sensor that can monitor methanol emission directly on the leaf of a plant under field conditions with low cost, high portability, and easy installation and use. The sensor technology eliminates the need for complex sampling, expensive instruments, and skilled operators for conventional gas chromatography-mass spectrometry. The sensor uses a composite of conducting polymer microcrystallites and platinum nanoparticles (PtNPs). The conducting poly(2-amino-1,3,4-thiadiazole) or poly(ATD) provides a high electrocatalytic activity with redox behavior. The modification of poly(ATD) with catalytic PtNPs enables efficient electrochemical oxidation of methanol at a specific potential. The advantages of poly(ATD) and PtNPs are synergized for high sensitivity and selectivity of the sensor for detecting methanol emissions with a sub-ppm limit of detection. Further, the infusion of a polymer electrolyte into the porous electrode of the sensor enables an all-solid-state VOC sensor. The sensor is integrated into a miniature gas collection chamber and capped with a hydrophobic gas diffusion membrane to minimize the influence of environmental humidity on the sensor performance. The sensor is installed on the leaf surface. In situ detection shows a difference in methanol emission between the lower and upper leaves of greenhouse maize plants. Further, under field conditions, the sensor reveals a noticeable difference in methanol emission concentration between two genotypes (Mo17 and B73 inbred lines) of maize plants. Therefore, the sensor will provide a promising new means of directly monitoring volatile emission of plants, which is a physiological phenotype as a function of genes and environment.

Bioinspired Stretchable Transducer for Wearable Continuous Monitoring of Respiratory Patterns in Humans and Animals

A bio-inspired continuous wearable respiration sensor modeled after the lateral line system of fish is reported which is used for detecting mechanical disturbances in the water. Despite the clinical importance of monitoring respiratory activity in humans and animals, continuous measurements of breathing patterns and rates are rarely performed in or outside of clinics. This is largely because conventional sensors are too inconvenient or expensive for wearable sensing for most individuals and animals. The bio-inspired air-silicone composite transducer (ASiT) is placed on the chest and measures respiratory activity by continuously measuring the force applied to an air channel embedded inside a silicone-based elastomeric material. The force applied on the surface of the transducer during breathing changes the air pressure inside the channel, which is measured using a commercial pressure sensor and mixed-signal wireless electronics. The transducer produced in this work are extensively characterized and tested with humans, dogs, and laboratory rats. The bio-inspired ASiT may enable the early detection of a range of disorders that result in altered patterns of respiration. The technology reported can also be combined with artificial intelligence and cloud computing to algorithmically detect illness in humans and animals remotely, reducing unnecessary visits to clinics.

A BLE-Connected Piezoresistive and Inertial Chest Band for Remote Monitoring of the Respiratory Activity by an Android Application: Hardware Design and Software Optimization

Breathing is essential for human life. Issues related to respiration can be an indicator of problems related to the cardiorespiratory system; thus, accurate breathing monitoring is fundamental for establishing the patient’s condition. This paper presents a ready-to-use and discreet chest band for monitoring the respiratory parameters based on the piezoresistive transduction mechanism. In detail, it relies on a strain sensor realized with a pressure-sensitive fabric (EeonTex LTT-SLPA-20K) for monitoring the chest movements induced by respiration. In addition, the band includes an Inertial Measurement Unit (IMU), which is used to remove the motion artefacts from the acquired signal, thereby improving the measurement reliability. Moreover, the band comprises a low-power conditioning and acquisition section that processes the signal from sensors, providing a reliable measurement of the respiration rate (RR), in addition to other breathing parameters, such as inhalation (TI) and exhalation (TE) times, inhalation-to-exhalation ratio (IER), and flow rate (V). The device wirelessly transmits the extracted parameters to a host device, where a custom mobile application displays them. Different test campaigns were carried out to evaluate the performance of the designed chest band in measuring the RR, by comparing the measurements provided by the chest band with those obtained by breath count. In detail, six users, of different genders, ages, and physical constitutions, were involved in the tests. The obtained results demonstrated the effectiveness of the proposed approach in detecting the RR. The achieved performance was in line with that of other RR monitoring systems based on piezoresistive textiles, but which use more powerful acquisition systems or have low wearability. In particular, the inertia-assisted piezoresistive chest band obtained a Pearson correlation coefficient with respect to the measurements based on breath count of 0.96 when the user was seated. Finally, Bland–Altman analysis demonstrated that the developed system obtained 0.68 Breaths Per Minute (BrPM) mean difference (MD), and Limits of Agreement (LoAs) of +3.20 and −1.75 BrPM when the user was seated.

Assessing Respiratory Activity by Using IMUs: Modeling and Validation

This study aimed to explore novel inertial measurement unit (IMU)-based strategies to estimate respiratory parameters in healthy adults lying on a bed while breathing normally. During the experimental sessions, the kinematics of the chest wall were contemporaneously collected through both a network of 9 IMUs and a set of 45 uniformly distributed reflective markers. All inertial kinematics were analyzed to identify a minimum set of signals and IMUs whose linear combination best matched the tidal volume measured by optoelectronic plethysmography. The resulting models were finally tuned and validated through a leave-one-out cross-validation approach to assess the extent to which they could accurately estimate a set of respiratory parameters related to three trunk compartments. The adopted methodological approach allowed us to identify two different models. The first, referred to as Model 1, relies on the 3D acceleration measured by three IMUs located on the abdominal compartment and on the lower costal margin. The second, referred to as Model 2, relies on only one component of the acceleration measured by two IMUs located on the abdominal compartment. Both models can accurately estimate the respiratory rate (relative error < 1.5%). Conversely, the duration of the respiratory phases and the tidal volume can be more accurately assessed by Model 2 (relative error < 5%) and Model 1 (relative error < 5%), respectively. We further discuss possible approaches to overcome limitations and improve the overall accuracy of the proposed approach.

A self-powered wireless motion sensor based on a high-surface area reverse electrowetting-on-dielectric energy harvester

This paper presents a motion-sensing device with the capability of harvesting energy from low-frequency motion activities. Based on the high surface area reverse electrowetting-on-dielectric (REWOD) energy harvesting technique, mechanical modulation of the liquid generates an AC signal, which is modeled analytically and implemented in Matlab and COMSOL. A constant DC voltage is produced by using a rectifier and a DC-DC converter to power up the motion-sensing read-out circuit. A charge amplifier converts the generated charge into a proportional output voltage, which is transmitted wirelessly to a remote receiver. The harvested DC voltage after the rectifier and DC-DC converter is found to be 3.3 V, having a measured power conversion efficiency (PCE) of the rectifier as high as 40.26% at 5 Hz frequency. The energy harvester demonstrates a linear relationship between the frequency of motion and the generated output power, making it highly suitable as a self-powered wearable motion sensor.

An integrated and conductive hydrogel-paper patch for simultaneous sensing of Chemical-Electrophysiological signals

Simultaneous monitoring of electrophysiological and biochemical signals is of great importance in healthcare and fitness management, while the fabrication of highly integrated and flexible devices is crucial to these applications. Herein, we devised a multifunctional and flexible hydrogel-paper patch (HPP) that was capable of simultaneously real-time monitoring of electrocardiogram (ECG) signal and biochemical signal (glucose content) in sweat during exercise. The self-assembly of the highly porous PEDOT:PSS hydrogel on paper fiber provided the HPP with good conductivity and hydrophilic wettability for efficient electron transmission and substance diffusion, thereby enabling it to serve as a low-impedance ECG electrode and a highly sensitive glucose sensor. Additionally, the spontaneous capillary flow effect allows the paper patch to be used as microfluidic channels for the collect and analysis of sweat. Moreover, the HPP is integrated with a flexible printed circuit board (FPCB) and works as a multifunctional wearable device mounted on the chest for real-time monitoring of electrophysiological and biochemical signals during exercise.

Vocalization associated respiration patterns: thermography-based monitoring and detection of preparation for calling

Vocal emission requires coordination with the respiratory system. Monitoring the increase in laryngeal pressure, which is needed for vocal production, allows detection of transitions from quiet respiration to vocalization-supporting respiration. Characterization of these transitions could be used to identify preparation for vocal emission and to examine the probability of it manifesting into an actual vocal production event. Specifically, overlaying the subject's respiration with conspecific calls can highlight events of call initiation and suppression, as a means of signalling coordination and avoiding jamming. Here, we present a thermal imaging-based methodology for synchronized respiration and vocalization monitoring of free-ranging meerkats. The sensitivity of this methodology is sufficient for detecting transient changes in the subject's respiration associated with the exertion of vocal production. The differences in respiration are apparent not only during the vocal output, but also prior to it, marking the potential time frame of the respiratory preparation for calling. A correlation between conspecific calls with elongation of the focal subject's respiration cycles could be related to fluctuations in attention levels or in the motivation to reply. This framework can be used for examining the capability for enhanced respiration control in animals during modulated and complex vocal sequences, detecting ‘failed’ vocalization attempts and investigating the role of respiration cues in the regulation of vocal interactions.

Summary: A thermography-based methodology for estimating breathing traces in free-ranging meerkats detects changes in respiration associated with the preparation and with the production of vocal signals by combining respiration monitoring with audio recordings.

Wearable Multi-Functional Sensing Technology for Healthcare Smart Detection

In recent years, considerable research efforts have been devoted to the development of wearable multi-functional sensing technology to fulfill the requirements of healthcare smart detection, and much progress has been achieved. Due to the appealing characteristics of flexibility, stretchability and long-term stability, the sensors have been used in a wide range of applications, such as respiration monitoring, pulse wave detection, gait pattern analysis, etc. Wearable sensors based on single mechanisms are usually capable of sensing only one physiological or motion signal. In order to measure, record and analyze comprehensive physical conditions, it is indispensable to explore the wearable sensors based on hybrid mechanisms and realize the integration of multiple smart functions. Herein, we have summarized various working mechanisms (resistive, capacitive, triboelectric, piezoelectric, thermo-electric, pyroelectric) and hybrid mechanisms that are incorporated into wearable sensors. More importantly, to make wearable sensors work persistently, it is meaningful to combine flexible power units and wearable sensors and form a self-powered system. This article also emphasizes the utility of self-powered wearable sensors from the perspective of mechanisms, and gives applications. Furthermore, we discuss the emerging materials and structures that are applied to achieve high sensitivity. In the end, we present perspectives on the outlooks of wearable multi-functional sensing technology.

Wearable Sensing Systems for Monitoring Mental Health

Wearable systems for monitoring biological signals have opened the door to personalized healthcare and have advanced a great deal over the past decade with the development of flexible electronics, efficient energy storage, wireless data transmission, and information processing technologies. As there are cumulative understanding of mechanisms underlying the mental processes and increasing desire for lifetime mental wellbeing, various wearable sensors have been devised to monitor the mental status from physiological activities, physical movements, and biochemical profiles in body fluids. This review summarizes the recent progress in wearable healthcare monitoring systems that can be utilized in mental healthcare, especially focusing on the biochemical sensors (i.e., biomarkers associated with mental status, sensing modalities, and device materials) and discussing their promises and challenges.

The Role of Unobtrusive Home-Based Continuous Sensing in the Management of Postacute Sequelae of SARS CoV-2

Amid the COVID-19 pandemic, it has been reported that greater than 35% of patients with confirmed or suspected COVID-19 develop postacute sequelae of SARS CoV-2 (PASC). PASC is still a disease for which preliminary medical data are being collected-mostly measurements collected during hospital or clinical visits-and pathophysiological understanding is yet in its infancy. The disease is notable for its prevalence and its variable symptom presentation, and as such, management plans could be more holistically made if health care providers had access to unobtrusive home-based wearable and contactless continuous physiologic and physical sensor data. Such between-hospital or between-clinic data can quantitatively elucidate a majority of the temporal evolution of PASC symptoms. Although not universally of comparable accuracy to gold standard medical devices, home-deployed sensors offer great insights into the development and progression of PASC. Suitable sensors include those providing vital signs and activity measurements that correlate directly or by proxy to documented PASC symptoms. Such continuous, home-based data can give care providers contextualized information from which symptom exacerbation or relieving factors may be classified. Such data can also improve the collective academic understanding of PASC by providing temporally and activity-associated symptom cataloging. In this viewpoint, we make a case for the utilization of home-based continuous sensing that can serve as a foundation from which medical professionals and engineers may develop and pursue long-term mitigation strategies for PASC.

Non-Intrusive Contact Respiratory Sensor for Vehicles

In this work, we propose a low-cost solution capable of collecting the driver's respiratory signal in a robust and non-intrusive way by contact with the chest and abdomen. It consists of a microcontroller and two piezoelectric sensors with their respective 3D printed plastic housings attached to the seat belt. An iterative process was conducted to find the optimal shape of the sensor housing. The location of the sensors can be easily adapted by sliding them along the seat belt. A few participants took part in three test sessions in a driving simulator. They had to perform various activities: resting, deep breathing, manual driving, and a non-driving-related task during automated driving. The subjects' breathing rates were calculated from raw data collected with a reference chest belt, each sensor alone, and the fusion of the two. Results indicate that respiratory rate could be assessed from a single sensor located on the chest with an average absolute error of 0.92 min^-1 across all periods, dropping to 0.13 min^-1 during deep breathing. Sensor fusion did not improve system performance. A 4-pole filter with a cutoff frequency of 1 Hz emerged as the best option to minimize the error during the different periods. The results suggest that such a system could be used to assess the driver's breathing rate while performing various activities in a vehicle.