Wearable technology: role in respiratory health and disease

Capacitive Sensing for Monitoring Stent Patency in the Central Airway

Central airway obstruction (CAO) is a respiratory disorder characterized by the blockage of the trachea and/or the main bronchi that can be life-threatening. Airway stenting is a palliative procedure for CAO commonly used given its efficacy. However, mucus impaction, secretion retention, and granulation tissue growth are known complications that can counteract the stent's benefits. To prevent these situations, patients are routinely brought into the hospital to check stent patency, incurring a burden for the patient and the health care system, unnecessarily when no problems are found. In this paper, we introduce a capacitive sensor embedded in a stent that can detect solid and colloidal obstructions in the stent, as such obstructions alter the capacitor's dielectric relative permittivity. In the case of colloidal obstructions (e.g., mucus), volumes as low as 0.1 ml can be detected. Given the small form factor of the sensor, it could be adapted to a variety of stent types without changing the standard bronchoscopy insertion method. The proposed system is a step forward in the development of smart airway stents that overcome the limitations of current stenting technology.Clinical Relevance- This establishes the foundation for smart stent technology to monitor stent patency as an alternative to rutinary bronchoscopies.

Telemonitoring Techniques for Lung Volume Measurement: Accuracy, Artifacts and Effort

Telemonitoring becomes more important in pulmonary research. It can be used to decrease the pressure on the health care system, to lower the costs of health care and to increase quality of life of patients. Previous studies show contradictory results regarding the effectiveness of telemonitoring. According to multiple researchers, inefficiency can be a result of poor study design, low data quality and usability issues. To counteract these issues, this review proves for an in-depth explanation of four (potential) telemonitoring systems in terms of work principle, accuracy, disturbing factors and usability. The evaluated systems are portable spirometry/breath-by-breath analyzers, respiratory inductance and magnetic plethysmography and electrical impedance tomography. These insights can be used to select the optimal technique for a specific purpose in future studies.

Using Telemedicine to Monitor the Patient with Chronic Respiratory Failure

Background: Advances in management have improved mortality of individuals with chronic respiratory failure (CRF), leading to an increase in need for long-term oxygen therapy and/or ventilatory support. These individuals require frequent visits and monitoring of their physiological parameters as well as of the functioning of their devices, such as ventilators or oxygen concentrators. Telemedicine is a clinical application of Information Communication Technology connecting patients to specialised care consultants. This narrative review aims to explore the current available telemonitoring options for individuals with CRF and reported or potential results. Methods: The research focused on EMBASE, CINALH, PubMed, and Scopus databases. Papers published between 2003 and 2021 in English were considered. Results: Different sensors, transmission devices and systems, and interventions are used with promising but not conclusive clinical results. However, legal problems are still unsolved, and economic advantages for health care systems, although potentially high, are still under debate. Conclusions: Telemonitoring systems for individuals with CRF are increasingly used; with promising results still to be clarified, legal, economical and organisational issues must be defined.

An Overview of Wearable Piezoresistive and Inertial Sensors for Respiration Rate Monitoring

The demand for wearable devices to measure respiratory activity is constantly growing, finding applications in a wide range of scenarios (e.g., clinical environments and workplaces, outdoors for monitoring sports activities, etc.). Particularly, the respiration rate (RR) is a vital parameter since it indicates serious illness (e.g., pneumonia, emphysema, pulmonary embolism, etc.). Therefore, several solutions have been presented in the scientific literature and on the market to make RR monitoring simple, accurate, reliable and noninvasive. Among the different transduction methods, the piezoresistive and inertial ones satisfactorily meet the requirements for smart wearable devices since unobtrusive, lightweight and easy to integrate. Hence, this review paper focuses on innovative wearable devices, detection strategies and algorithms that exploit piezoresistive or inertial sensors to monitor the breathing parameters. At first, this paper presents a comprehensive overview of innovative piezoresistive wearable devices for measuring user’s respiratory variables. Later, a survey of novel piezoresistive textiles to develop wearable devices for detecting breathing movements is reported. Afterwards, the state-of-art about wearable devices to monitor the respiratory parameters, based on inertial sensors (i.e., accelerometers and gyroscopes), is presented for detecting dysfunctions or pathologies in a non-invasive and accurate way. In this field, several processing tools are employed to extract the respiratory parameters from inertial data; therefore, an overview of algorithms and methods to determine the respiratory rate from acceleration data is provided. Finally, comparative analysis for all the covered topics are reported, providing useful insights to develop the next generation of wearable sensors for monitoring respiratory parameters.

Accuracy and Reliability of Commercial Wrist-Worn Pulse Oximeter During Normobaric Hypoxia Exposure Under Resting Conditions

Purpose: The present study analyzed peripheral blood oxygen saturation (S_pO₂) and heart rate (HR) measurements taken on the Garmin fēnix® 5X Plus watch, comparing them to measurements taken on a standard medical-grade pulse oximeter during normobaric hypoxia exposure under resting conditions. Methods: Thirteen women (mean ± SD: Age 20 ± 1 years, height 165 ± 5 cm, mass, 67 ± 9 kg) and ten men (mean ± SD: Age 21 ± 3 years, height 177 ± 6 cm, mass 78 ± 11 kg) sat inside a customized environmental chamber while the fraction of inspired oxygen (F_IO₂) was adjusted to simulate altitudes of 12,000; 10,000; 8,000; 6,000; and 900 ft. The novel commercial device (Garmin fēnix®) and a medical-grade pulse oximeter (Nonin® 7500) were used to measure S_pO₂ and HR in triplicate at each simulated altitude. Bland-Altman analyses were used to assess differences between methods. Results: Bland-Altman analysis indicated 3.3% bias for S_pO₂ measurements taken on the Garmin fēnix® at 12,000 ft of simulated altitude (limits of agreement: -1.9-8.6%). Mean differences in S_pO₂ measurements were smaller at the remaining simulated altitudes, where bias measurements ranged from 0.7% to 0.8%. The Garmin fēnix® also underestimated heart rate, but those discrepancies were minimal (bias measurements at all simulated altitude exposures were < 1.0 bpm). Conclusions: With the exception of readings taken at 12,000 ft of simulated altitude, the Garmin fēnix® exhibits minimal overestimation of S_pO₂ and minimal underestimation of HR during simulated altitude exposure. These data suggest the Garmin fēnix® watch may be a viable method to monitor S_pO₂ and HR under most ambient environmental conditions.

Role of new digital technologies and telemedicine in pulmonary rehabilitation : Smart devices in the treatment of chronic respiratory diseases

BACKGROUND

Asthma and chronic obstructive pulmonary diseases are conditions characterized by a variable progression. Some individuals experience longer asymptomatic periods while others acute worsening periods and/or exacerbations triggered by symptom multiplication factors. Medications are adjusted to the patients' respiratory function, self-assessment of health and emerging certain physical changes. A more effective treatment may be applied by real-time data registered during the patient's everyday life.

AIM AND METHODS

Introducing new modern digital technology in pulmonary rehabilitation (PR) to help tracking the patients' medication, thus we systematically reviewed the latest publications on telemedicine and pulmonary telerehabilitation.

CONCLUSION

The use of the latest digital technologies in PR is very exciting and offers great opportunities while treating patients affected by specific conditions. On the one hand, adherence to medication can be improved in patients with chronic respiratory diseases by using these new state of the art devices; on the other hand, digital devices will also be able to monitor various physiological parameters of patients during their usual everyday activities. Data can be stored on a smartphone and shared with the provider. Relying on this information, physicians will be able to tailor medications and dosage to the specific needs of individual patients. Telerehabilitation may be a sustainable solution to the growing burden of chronic respiratory disease worldwide. However, PR must keep its cornerstones, such as education and motivations, which are most successful when conducted in person. Many issues remain to be resolved in the future, e.g. cybersecurity while using smart devices since they offer unique opportunities for PR.

Respiratory function assessment at the time of a new respiratory virus pandemic

We must be aware that new respiratory virus pandemic can happen frequently. Standard lung function tests should keep their crucial role to assist the clinicians in the decision-making process, but they are at risk for the spread of infection because of the generated droplets. We used opto-electronic plethysmography to investigate the post-COVID-19 syndrome on 12 patients after ICU. We found normal ventilatory pattern at rest, a restrictive pattern located in the ribcage during vital capacity and surgical mask to significantly increase minute ventilation. The attention on unconventional respiratory function tests should be sponsored for the important information they can provide.

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.

Integration of Internet of Things with Quantum Dots: A State-of-the-art of Medicine

Internet of Things (IoT) emerges as disruptive innovation and development in the fields of drug delivery and biomedical sciences using on-target active transportation, sensors, wearable devices, real-time diagnostics, etc. Semiconducting fluorescence emitting material, quantum dots on integration with IoT displayed interesting results in the healthcare sector, especially in hospitals and pathological laboratories. Presently, the integrated system is used to improve productivity without the interference of human and offer a cost-effective system. This integrated system can be used for the detection of various diseases like epilepsy, cancer, diabetes, etc., and various biomedical applications like energy storage, lights, sensor technology, light filters, etc. The integrated technology is implemented into the field of medicine for simplifying the approaches in therapeutics and diagnostic applications. The collected and analyzed data are further useful for healthcare professionals to find patient-centric solutions. Artificial Intelligence-aided IoT emerges as a novel technology for transmitting and securing health data. Despite some of the limitations like e-waste and the risk of hacking, an IoT-based QD system will be considered as a modern healthcare provider with life-saving products for enriching the medical quality and real-time accessibility.

The Challenges and Pitfalls of Detecting Sleep Hypopnea Using a Wearable Optical Sensor: Comparative Study

Background

Obstructive sleep apnea (OSA) is the most prevalent respiratory sleep disorder occurring in 9% to 38% of the general population. About 90% of patients with suspected OSA remain undiagnosed due to the lack of sleep laboratories or specialists and the high cost of gold-standard in-lab polysomnography diagnosis, leading to a decreased quality of life and increased health care burden in cardio- and cerebrovascular diseases. Wearable sleep trackers like smartwatches and armbands are booming, creating a hope for cost-efficient at-home OSA diagnosis and assessment of treatment (eg, continuous positive airway pressure [CPAP] therapy) effectiveness. However, such wearables are currently still not available and cannot be used to detect sleep hypopnea. Sleep hypopnea is defined by ≥30% drop in breathing and an at least 3% drop in peripheral capillary oxygen saturation (Spo₂) measured at the fingertip. Whether the conventional measures of oxygen desaturation (OD) at the fingertip and at the arm or wrist are identical is essentially unknown.

Objective

We aimed to compare event-by-event arm OD (arm_OD) with fingertip OD (finger_OD) in sleep hypopneas during both naïve sleep and CPAP therapy.

Methods

Thirty patients with OSA underwent an incremental, stepwise CPAP titration protocol during all-night in-lab video-polysomnography monitoring (ie, 1-h baseline sleep without CPAP followed by stepwise increments of 1 cmH₂O pressure per hour starting from 5 to 8 cmH₂O depending on the individual). Arm_OD of the left biceps muscle and finger_OD of the left index fingertip in sleep hypopneas were simultaneously measured by frequency-domain near-infrared spectroscopy and video-polysomnography photoplethysmography, respectively. Bland-Altman plots were used to illustrate the agreements between arm_OD and finger_OD during baseline sleep and under CPAP. We used t tests to determine whether these measurements significantly differed.

Results

In total, 534 obstructive apneas and 2185 hypopneas were recorded. Of the 2185 hypopneas, 668 (30.57%) were collected during baseline sleep and 1517 (69.43%), during CPAP sleep. The mean difference between finger_OD and arm_OD was 2.86% (95% CI 2.67%-3.06%, t₆₆₇=28.28; P<.001; 95% limits of agreement [LoA] –2.27%, 8.00%) during baseline sleep and 1.83% (95% CI 1.72%-1.94%, t₁₅₁₆=31.99; P<.001; 95% LoA –2.54%, 6.19%) during CPAP. Using the standard criterion of 3% saturation drop, arm_OD only recognized 16.32% (109/668) and 14.90% (226/1517) of hypopneas at baseline and during CPAP, respectively.

Conclusions

arm_OD is 2% to 3% lower than standard finger_OD in sleep hypopnea, probably because the measured arm_OD originates physiologically from arterioles, venules, and capillaries; thus, the venous blood adversely affects its value. Our findings demonstrate that the standard criterion of ≥3% OD drop at the arm or wrist is not suitable to define hypopnea because it could provide large false-negative results in diagnosing OSA and assessing CPAP treatment effectiveness.

Wheeze Recognition Algorithm for Remote Medical Care Device in Children: Validation Study

BACKGROUND

Since 2020, peoples' lifestyles have been largely changed due to the COVID-19 pandemic worldwide. In the medical field, although many patients prefer remote medical care, this prevents the physician from examining the patient directly; thus, it is important for patients to accurately convey their condition to the physician. Accordingly, remote medical care should be implemented and adaptable home medical devices are required. However, only a few highly accurate home medical devices are available for automatic wheeze detection as an exacerbation sign.

OBJECTIVE

We developed a new handy home medical device with an automatic wheeze recognition algorithm, which is available for clinical use in noisy environments such as a pediatric consultation room or at home. Moreover, the examination time is only 30 seconds, since young children cannot endure a long examination time without crying or moving. The aim of this study was to validate the developed automatic wheeze recognition algorithm as a clinical medical device in children at different institutions.

METHODS

A total of 374 children aged 4-107 months in pediatric consultation rooms of 10 institutions were enrolled in this study. All participants aged ≥6 years were diagnosed with bronchial asthma and patients ≤5 years had reported at least three episodes of wheezes. Wheezes were detected by auscultation with a stethoscope and recorded for 30 seconds using the wheeze recognition algorithm device (HWZ-1000T) developed based on wheeze characteristics following the Computerized Respiratory Sound Analysis guideline, where the dominant frequency and duration of a wheeze were >100 Hz and >100 ms, respectively. Files containing recorded lung sounds were assessed by each specialist physician and divided into two groups: 177 designated as "wheeze" files and 197 as "no-wheeze" files. Wheeze recognitions were compared between specialist physicians who recorded lung sounds and those recorded using the wheeze recognition algorithm. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files, and evaluated the influence of age and sex on the wheeze detection sensitivity.

RESULTS

Detection of wheezes was not influenced by age and sex. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 96.6%, 98.5%, 98.3%, and 97.0%, respectively. Wheezes were automatically detected, and heartbeat sounds, voices, and crying were automatically identified as no-wheeze sounds by the wheeze recognition algorithm.

CONCLUSIONS

The wheeze recognition algorithm was verified to identify wheezing with high accuracy; therefore, it might be useful in the practical implementation of asthma management at home. Only a few home medical devices are available for automatic wheeze detection. The wheeze recognition algorithm was verified to identify wheezing with high accuracy and will be useful for wheezing management at home and in remote medical care.

Assessing the Tidal Volume through Wearables: A Scoping Review

The assessment of respiratory activity based on wearable devices is becoming an area of growing interest due to the wide range of available sensors. Accordingly, this scoping review aims to identify research evidence supporting the use of wearable devices to monitor the tidal volume during both daily activities and clinical settings. A screening of the literature (Pubmed, Scopus, and Web of Science) was carried out in December 2020 to collect studies: i. comparing one or more methodological approaches for the assessment of tidal volume with the outcome of a state-of-the-art measurement device (i.e., spirometry or optoelectronic plethysmography); ii. dealing with technological solutions designed to be exploited in wearable devices. From the initial 1031 documents, only 36 citations met the eligibility criteria. These studies highlighted that the tidal volume can be estimated by using different technologies ranging from IMUs to strain sensors (e.g., resistive, capacitive, inductive, electromagnetic, and optical) or acoustic sensors. Noticeably, the relative volumetric error of these solutions during quasi-static tasks (e.g., resting and sitting) is typically ≥10% but it deteriorates during dynamic motor tasks (e.g., walking). As such, additional efforts are required to improve the performance of these devices and to identify possible applications based on their accuracy and reliability.

Monitoring Symptoms of Infectious Diseases: Perspectives for Printed Wearable Sensors

Infectious diseases possess a serious threat to the world's population, economies, and healthcare systems. In this review, we cover the infectious diseases that are most likely to cause a pandemic according to the WHO (World Health Organization). The list includes COVID-19, Crimean-Congo Hemorrhagic Fever (CCHF), Ebola Virus Disease (EBOV), Marburg Virus Disease (MARV), Lassa Hemorrhagic Fever (LHF), Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), Nipah Virus diseases (NiV), and Rift Valley fever (RVF). This review also investigates research trends in infectious diseases by analyzing published research history on each disease from 2000-2020 in PubMed. A comprehensive review of sensor printing methods including flexographic printing, gravure printing, inkjet printing, and screen printing is conducted to provide guidelines for the best method depending on the printing scale, resolution, design modification ability, and other requirements. Printed sensors for respiratory rate, heart rate, oxygen saturation, body temperature, and blood pressure are reviewed for the possibility of being used for disease symptom monitoring. Printed wearable sensors are of great potential for continuous monitoring of vital signs in patients and the quarantined as tools for epidemiological screening.

Electrical Tomography Reconstruction Using Reconfigurable Waveforms in a FPGA

The principal objective of this research is to conceive a mobile system based on electrical tomography for subsurface imaging and monitoring in order to enable simultaneous recording of electrical potentials of cardiac and pulmonary activity. For an exploration of excitation waveforms in electrical tomography, specialized hardware is required. As the main principle of tomography is the measurement of electrical perturbations on an unknown object, it is crucial to synchronize excitation and sensing processes in a very precise way for the purpose of acquiring meaningful data. To cope with this problem, an FPGA device is used, with an architecture that allows us to trigger excitation signals and to read sensed data simultaneously via independent processes that share the same clock. In this way, waveform reconfiguration on frequency and shape can be provided and studied. The system is connected to a standard microcontroller SoC with a simple API that allows for IoT capabilities for on-line operation and tracking, given that the design is targeted for in vivo medical monitoring. As a result of the research work, a measuring device was developed, the surface data analyzed and the image was reconstructed using the selected configuration.

Telemedicine in the management of patients with chronic respiratory failure

Patients with chronic respiratory failure are often required to attend multiple hospital appointments, which may be difficult due to their physical disabilities and the amount of equipment they are required to bring. Their caregivers often struggle with the lack of immediate care available when the patient suffers difficulties at home. Telemedicine is an opportunity to bridge the gap between home and healthcare professionals by allowing the healthcare team to reach into patients' homes to provide more frequent support. The evidence for the use of telemedicine in patients with chronic respiratory failure remains equivocal. Although the uptake of telemedicine has been slow, the SARS-CoV-2 pandemic has resulted in the rapid dissemination of telemedicine to allow the delivery of care to vulnerable patients while reducing the need for their attendance in hospital. Logistical and legal challenges to the delivery of telemedicine remain, but the pandemic may serve as a driver to ameliorate these challenges and facilitate wider use of this technology to improve the experience of patients with chronic respiratory failure.

Educational aims

To provide an overview of the rationale for delivering care via telemedicine for patients with chronic respiratory failure.To provide the evidence base for establishing a telemedicine service.To highlight the potential opportunities and challenges in delivering a telemedicine service for patients with chronic respiratory failure.

A Home Telemedicine System for Continuous Respiratory Monitoring

This article presents a continuous home telemonitoring system for chronic respiratory patients using 5G connectivity developed in partnership with Vodafone as a part of the 5G Trial in Milan established by the Italian Ministry of Economic Development. The system features a wearable respiratory and activity monitor, an environmental sensor and a pulse oximeter sending the data through a 5G router to a Multi-Edge Computing server, incorporated in the Vodafone 5G infrastructure, where they are stored and accessible for visualization. In particular, activity, respiratory and environmental data are continuously streamed and collected. The solution has been tested on 18 healthy volunteers during non-supervised recordings lasting at least 48 hours. The combination of recognized activities and associated respiratory parameters provided statistically significant variations in breathing patterns between one activity and the other, thus giving more complete information to the clinicians than previously studied telemedicine systems based on spot-checks. In particular, statistically significant differences are found in tidal volume and minute ventilation between horizontal and vertical postures (p < 0.001) and between vertical postures and dynamic activities (p < 0.001); the respiratory rate shows statistically significant differences between horizontal and vertical postures (p < 0.001). Some environmental parameters have different mean values between day and night, such as carbon dioxide (p < 0.001). Trials on patients are needed to further study this telemedicine solution and make it commercially available in the future. The main further technical development suggested is the use of commercial 5G smartphones as routers, in order to make the system usable outside of home settings.

ANN model for users’ perception on IOT based smart healthcare monitoring devices and its impact with the effect of COVID 19

Purpose

COVID-19 was indeed a global epidemic that revolutionized the way of life, especially health-care services. The way health care will be delivered will undergo a dramatic change in the future. The aim is to analyse the increasing usage of health care systems along with digital technology and IoT especially during pandemic.

Design Methodology Approach

This research paper deals with users’ perception and their recommendation status of IoT-based smart health-care monitoring devices based on their perception, experience and level of importance to enhance the quality of life. An effective artificial neural networking (ANN)-based predictive model is designed to classify the user’s perception of usage of IoT-based smart health-care monitoring wearables based on their experience and knowledge.

Findings

The model developed has 96.7% accuracy. Among the various predictors chosen as inputs for the model, the findings indicate that self-comfort and trusted data from the device are of high priority. The present study focused only on some common factors derived from previous studies.

Research Limitations Implications

Although the performance of the proposed system was noticed to be good, the size of the sample is also limited to a few responses. Implications for future research and practices are discussed.

Originality Value

This is a novel study that aims to develop an ANN model on analyzing the user’s perception of IoT-based smart health-care wearables with the effect of COVID-19 pandemic. This paper elaborates on the ongoing efforts to restart the health-care services for survivability in the new normal situations.

A Battery-Less Wireless Respiratory Sensor Using Micro-Machined Thin-Film Piezoelectric Resonators

In this work, we present a battery-less wireless Micro-Electro-Mechanical (MEMS)-based respiration sensor capable of measuring the respiration profile of a human subject from up to 2 m distance from the transceiver unit for a mean excitation power of 80 µW and a measured SNR of 124.8 dB at 0.5 m measurement distance. The sensor with a footprint of ~10 cm2 is designed to be inexpensive, maximize user mobility, and cater to applications where disposability is desirable to minimize the sanitation burden. The sensing system is composed of a custom UHF RFID antenna, a low-loss piezoelectric MEMS resonator with two modes within the frequency range of interest, and a base transceiver unit. The difference in temperature and moisture content of inhaled and exhaled air modulates the resonance frequency of the MEMS resonator which in turn is used to monitor respiration. To detect changes in the resonance frequency of the MEMS devices, the sensor is excited by a pulsed sinusoidal signal received through an external antenna directly coupled to the device. The signal reflected from the device through the antenna is then analyzed via Fast Fourier Transform (FFT) to extract and monitor the resonance frequency of the resonator. By tracking the resonance frequency over time, the respiration profile of a patient is tracked. A compensation method for the removal of motion-induced artifacts and drift is proposed and implemented using the difference in the resonance frequency of two resonance modes of the same resonator.

Utility of a Smartphone-Based System (cvrPhone) in Estimating Minute Ventilation from Electrocardiographic Signals

Background: We investigated the ability of a novel stand-alone, smartphone-based system, the cvrPhone, in estimating the minute ventilation (MV) from body surface electrocardiographic (ECG) signals. Methods: Twelve lead ECG signals were collected from anesthetized and mechanically ventilated swine (n = 9) using standard surface electrodes and the cvrPhone. The tidal volume delivered to the animals was varied between 0, 250, 500, and 750 mL at respiration rates of 6 and 14 breaths/min. MV estimates were determined by the cvrPhone and were compared with the delivered ones. Results: The median relative estimation errors were 17%, -4%, 35%, -3%, -9%, and 1%, for true MVs of 1,500, 3,000, 3,500, 4,500, 7,000, and 10,500 breaths*mL/min, respectively. The MV estimates at each of the settings were significantly different from each other (p < 0.05). Conclusions: We have demonstrated that accurate MV estimations can be derived from standard body surface ECG signals, using a smartphone.

Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients

Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index (BI), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily BI was linearly associated with respiratory tract viral load (p < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 in predicting clinical worsening events (sensitivity 94.1% and specificity 88.9%) and prolonged hospitalization (sensitivity 66.7% and specificity 72.7%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.

Smart Textiles and Sensorized Garments for Physiological Monitoring: A Review of Available Solutions and Techniques

Several wearable devices for physiological and activity monitoring are found on the market, but most of them only allow spot measurements. However, the continuous detection of physiological parameters without any constriction in time or space would be useful in several fields such as healthcare, fitness, and work. This can be achieved with the application of textile technologies for sensorized garments, where the sensors are completely embedded in the fabric. The complete integration of sensors in the fabric leads to several manufacturing techniques that allow dealing with both the technological challenges entailed by the physiological parameters under investigation, and the basic requirements of a garment such as perspiration, washability, and comfort. This review is intended to provide a detailed description of the textile technologies in terms of materials and manufacturing processes employed in the production of sensorized fabrics. The focus is pointed at the technical challenges and the advanced solutions introduced with respect to conventional sensors for recording different physiological parameters, and some interesting textile implementations for the acquisition of biopotentials, respiratory parameters, temperature and sweat are proposed. In the last section, an overview of the main garments on the market is depicted, also exploring some relevant projects under development.

Low-Hysteresis and Ultrasensitive Microcellular Structures for Wearable Electronic Applications

Wearable technologies offer the opportunity to record human physiological signals in real time, in a noninvasive way, and the data can be used to aid in the early detection of abnormal health conditions. Here, we demonstrate how the interconnected porosity can be used to increase the sensitivity and linearity of capacitive pressure sensors. The finite element analysis supports the experimental observation that the movement of air during the dynamic mechanical loading is responsible for the high sensitivity observed (0.18 ± 0.01 kPa^-1) when compared with the solid poly(glycerol sebacate) sensor (0.0042 ± 0.0002 kPa^-1). The porous sensors present strain insensitivity and remarkable linearity over the entire range of applied mechanical pressure (0-6 kPa), capable of detecting both static and dynamic mechanical stimuli (17 nm/s), and a response time of 50 ms, without evidence of fatigue or electrical hysteresis over 10,000 mechanical cycles. The outstanding features of the porous sensors can find a broad range of applications in real-time health monitoring, from demanding movements like walking/running, to small deformations resulting from breathing or heart beating. The ultrasensitive microcellular structures synthesized in this study can be applied to other types of sensing transductions to obtain tunable and function-specific sensors with high sensitivity.

Wheezing Characteristics and Predicting Reactivity to Inhaled β2-Agonist in Children for Home Medical Care

Background: Given that wheezing is treated with inhaled β2-agonists, their effect should be reviewed before the condition becomes severe; however, few methods can currently predict reactivity to inhaled β2-agonists. We investigated whether preinhalation wheezing characteristics identified by lung sound analysis can predict reactivity to inhaled β2-agonists. Methods: In 202 children aged 10-153 months, wheezing was identified by auscultation. Lung sounds were recorded for 30 s in the chest region on the chest wall during tidal breathing. We analyzed the wheezing before and after β2-agonist inhalation. Wheezing was displayed as horizontal bars of intensity defined as a wheeze power band, and the wheezing characteristics (number, frequency, and maximum intensity frequency) were evaluated by lung sound analysis. The participants were divided into two groups: non-disappears (wheezing did not disappear after inhalation) and disappears (wheezing disappeared after inhalation). Wheezing characteristics before β2-agonist inhalation were compared between the two groups. The characteristics of wheezing were not affected by body size. The number of wheeze power bands of the non-responder group was significantly higher than those of the responder group (P < 0.001). The number of wheeze power bands was a predictor of reactivity to inhaled β2-agonists, with a cutoff of 11.1. The 95% confidence intervals of sensitivity, specificity, and positive and negative predictive values were 88.8, 42, 44, and 81.1% (P < 0.001), respectively. Conclusions: The number of preinhalation wheeze power bands shown by lung sound analysis was a useful indicator before treatment. This indicator could be a beneficial index for managing wheezing in young children.

A Closer Look at Yoga Nidra: Sleep Lab Protocol

Extended sleep onset latency (SOL), or "sleep onset insomnia," can decrease total sleep time, increasing risk for many health conditions, including heart disease, stroke, and all-cause mortality. Sleep disorders persist in the United States despite current behavioral/pharmaceutical remedies, with 10% to 15% of the population suffering from insomnia. Mind-body therapies offer additional solutions, as meditation has been correlated with decreased SOL. More research on use of mind-body practices for insomnia is needed. This study investigates the guided meditation practice of Yoga Nidra (yogic sleep) as a promising intervention for sleep disorders because of its purported ability to induce mental, physical, and emotional relaxation. In this pilot study, we address the feasibility of Yoga Nidra for insomnia, appropriateness of our selected measurement systems, and effect of Yoga Nidra on brainwaves, sleep onset, and the autonomic nervous system. Our study sample includes 22 adults, ages 18-45, with insomnia. The design includes two clinic visits (V1, lying quietly for 90 min; V2, randomization to 90-min lying quietly vs. 30-min Yoga Nidra plus 60-min lying quietly), taking place 1 to 14 days apart. Outcomes measured during/after Yoga Nidra (vs. control) include sleep onset, electroencephalography (EEG) power, heart rate variability (HRV), and respiratory rate. Self-reported mood and anxiety will be measured before/after each visit. Resulting physiological, psychological, and feasibility data will be used to inform future clinical studies of Yoga Nidra for sleep and relaxation.

Factors Predicting Treatment of World Trade Center-Related Lung Injury: A Longitudinal Cohort Study

The factors that predict treatment of lung injury in occupational cohorts are poorly defined. We aimed to identify patient characteristics associated with initiation of treatment with inhaled corticosteroid/long-acting beta-agonist (ICS/LABA) >2 years among World Trade Center (WTC)-exposed firefighters. The study population included 8530 WTC-exposed firefighters. Multivariable logistic regression assessed the association of patient characteristics with ICS/LABA treatment for >2 years over two-year intervals from 11 September 2001-10 September 2017. Cox proportional hazards models measured the association of high probability of ICS/LABA initiation with actual ICS/LABA initiation in subsequent intervals. Between 11 September 2001-1 July 2018, 1629/8530 (19.1%) firefighters initiated ICS/LABA treatment for >2 years. Forced Expiratory Volume in 1 s (FEV1), wheeze, and dyspnea were consistently and independently associated with ICS/LABA treatment. High-intensity WTC exposure was associated with ICS/LABA between 11 September 2001-10 September 2003. The 10th percentile of risk for ICS/LABA between 11 September 2005-10 Septmeber 2007 was associated with a 3.32-fold increased hazard of actual ICS/LABA initiation in the subsequent 4 years. In firefighters with WTC exposure, FEV1, wheeze, and dyspnea were independently associated with prolonged ICS/LABA treatment. A high risk for treatment was identifiable from routine monitoring exam results years before treatment initiation.

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

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.

An Evaluation of Biometric Monitoring Technologies for Vital Signs in the Era of COVID-19

The novel coronavirus disease 2019 (COVID-19) global pandemic has shifted how many patients receive outpatient care. Telehealth and remote monitoring have become more prevalent, and measurements taken in a patient's home using biometric monitoring technologies (BioMeTs) offer convenient opportunities to collect vital sign data. Healthcare providers may lack prior experience using BioMeTs in remote patient care, and, therefore, may be unfamiliar with the many versions of BioMeTs, novel data collection protocols, and context of the values collected. To make informed patient care decisions based on the biometric data collected remotely, it is important to understand the engineering solutions embedded in the products, data collection protocols, form factors (physical size and shape), data quality considerations, and availability of validation information. This article provides an overview of BioMeTs available for collecting vital signs (temperature, heart rate, blood pressure, oxygen saturation, and respiratory rate) and discusses the strengths and limitations of continuous monitoring. We provide considerations for remote data collection and sources of validation information to guide BioMeT use in the era of COVID-19 and beyond.

Diagnosis and prognosis for exercise-induced muscle injuries: from conventional imaging to emerging point-of-care testing

With the development of modern society, we have witnessed a significant increase of people who join in sport exercises, which also brings significantly increasing exercise-induced muscle injuries, resulting in reduction and even cessation of participation in sports and physical activities. Although severely injured muscles can hardly realize full functional restoration, skeletal muscles subjected to minor muscle injuries (e.g., tears, lacerations, and contusions) hold remarkable regeneration capacity to be healed without therapeutic interventions. However, delayed diagnosis or inappropriate prognosis will cause exacerbation of the injuries. Therefore, timely diagnosis and prognosis of muscle injuries is important to the recovery of injured muscles. Here, in this review, we discuss the definition and classification of exercise-induced muscle injuries, and then analyze their underlying mechanism. Subsequently, we provide detailed introductions to both conventional and emerging techniques for evaluation of exercise-induced muscle injuries with focus on emerging portable and wearable devices for point-of-care testing (POCT). Finally, we point out existing challenges and prospects in this field. We envision that an integrated system that combines physiological and biochemical analyses is anticipated to be realized in the future for assessing muscle injuries.

Lung Function Variability in Children and Adolescents With and Without Asthma (LUV Study): Protocol for a Prospective, Nonrandomized, Clinical Trial

Background

Variability analysis of peak expiratory flow (PEF) and forced expiratory volume at 1 second (FEV1) has been used in research to predict exacerbations in adults with asthma. However, there is a paucity of data regarding PEF and FEV1 variability in healthy children and adolescents and those with asthma.

Objective

The objective of this study is the assessment of PEF and FEV1 variability in (1) healthy children and adolescents, to define the normal daily fluctuation of PEF and FEV1 and the parameters that may influence it, and (2) children and adolescents with asthma, to explore the differences from healthy subjects and reveal any specific variability changes prior to exacerbation.

Methods

The study will include 100 healthy children and adolescents aged 6-18 years (assessment of normal PEF and FEV1 variability) and 100 children and adolescents of the same age with diagnosed asthma (assessment of PEF and FEV1 variability in subjects with asthma). PEF and FEV1 measurements will be performed using an ultraportable spirometer (Spirobank Smart; MIR Medical International Research) capable of smartphone connection. Measurements will be performed twice a day between 7 AM and 9 AM and between 7 PM and 9 PM and will be dispatched via email to a central database for a period of 3 months. PEF and FEV1 variability will be assessed by detrended fluctuation and sample entropy analysis, aiming to define the normal pattern (healthy controls) and to detect and quantify any deviations among individuals with asthma. The anticipated duration of the study is 24 months.

Results

The study is funded by the “C. Caratheodory” Programme of the University of Patras, Greece (PN 47014/24.9.2018). It was approved by the Ethics Committee (decision 218/19-03-2019) and the Scientific Board (decision 329/02-04-2019) of the University Hospital of Patras, Greece. Patient recruitment started in January 2020, and as of June 2020, 100 healthy children have been enrolled (74 of them have completed the measurements). The anticipated duration of the study is 24 months. The first part of the study (assessment of lung function variability in healthy children and adolescents) will be completed in August 2020, and the results will be available for publication by October 2020.

Conclusions

Healthy children and adolescents may present normal short- and long-term fluctuations in lung function; the pattern of this variability may be influenced by age, sex, and environmental conditions. Significant lung function variability may also be present in children and adolescents with asthma, but the patterns may differ from those observed in healthy children and adolescents. Such data would improve our understanding regarding the chronobiology of asthma and permit the development of integrated tools for assessing the level of control and risk of future exacerbations.

Trial Registration

ClinicalTrials.gov NCT04163146; https://clinicaltrials.gov/ct2/show/NCT04163146

International Registered Report Identifier (IRRID)

DERR1-10.2196/20350

Comparison between the Airgo™ Device and a Metabolic Cart during Rest and Exercise

The aim of this study is to compare the accuracy of Airgo™, a non-invasive wearable device that records breath, with respect to a gold standard. In 21 healthy subjects (10 males, 11 females), four parameters were recorded for four min at rest and in different positions simultaneously by Airgo™ and SensorMedics 2900 metabolic cart. Then, a cardio-pulmonary exercise test was performed using the Erg 800S cycle ergometer in order to test Airgo™'s accuracy during physical effort. The results reveal that the relative error median percentage of respiratory rate was of 0% for all positions at rest and for different exercise intensities, with interquartile ranges between 3.5 (standing position) and 22.4 (low-intensity exercise) breaths per minute. During exercise, normalized amplitude and ventilation relative error medians highlighted the presence of an error proportional to the volume to be estimated. For increasing intensity levels of exercise, Airgo™'s estimate tended to underestimate the values of the gold standard instrument. In conclusion, the Airgo™ device provides good accuracy and precision in the estimate of respiratory rate (especially at rest), an acceptable estimate of tidal volume and minute ventilation at rest and an underestimation for increasing volumes.

Sensor technology for nursing research

BACKGROUND

Electronic sensors measuring biological and behavioral aspects of health and the environment are becoming ubiquitous and, with advances in data science and ehealth technology, provide opportunities for new inquiry and innovative approaches to nursing research.

PURPOSE

To conceptualize the use of sensor technology from the perspective of nursing science.

METHODS

This review reports the keynote presentation from the Expanding Science of Sensor Technology in Nursing Research Conference presented by the Council for Advancement of Nursing Science in 2019 FINDINGS: Electronic sensors enable collection, recording, and transmission of data in real time in real life settings, remote monitoring, self-monitoring, and communication between health care professionals and patients. A deliberative approach to selecting and applying electronic sensors and analyzing and interpreting the data is needed for successful research.

DISCUSSION

Electronic sensors have high potential to advance nursing science.

Nano-copper enhanced flexible device for simultaneous measurement of human respiratory and electro-cardiac activities

BACKGROUND

Dysfunction of human respiratory and electro-cardiac activities could affect the ability of the heart to pump blood and the lungs to inhale oxygen. Thus, a device could simultaneously measure electro-cardiac signal and respiratory pressure could provide vital signs for predicting early warning of cardio-pulmonary function-related chronic diseases such as cardiovascular disease, and respiratory system disease.

RESULTS

In this study, a flexible device integrated with piezo-resistive sensing element and voltage-sensing element was developed to simultaneously measure human respiration and electro-cardiac signal (including respiratory pressure, respiration frequency, and respiration rhythm; electro-cardio frequency, electro-cardio amplitude, and electro-cardio rhythm). When applied to the measurement of respiratory pressure, the piezo-resistive performance of the device was enhanced by nano-copper modification, which detection limitation of pressure can reduce to 100 Pa and the sensitivity of pressure can achieve to 0.053 ± 0.00079 kPa-1. In addition, the signal-to-noise ratio during bio-electrical measurement was increased to 10.7 ± 1.4, five times better than that of the non-modified device.

CONCLUSION

This paper presents a flexible device for the simultaneous detection of human respiration and cardiac electrical activity. To avoid interference between the two signals, the layout of the electrode and the strain sensor was optimized by FEA simulation analysis. To improve the piezo-resistive sensitivity and bio-electric capturing capability of the device, a feather-shaped nano-copper was modified onto the surface of carbon fiber. The operation simplicity, compact size, and portability of the device open up new possibilities for multi-parameter monitoring.

Evaluation of Remote Monitoring Device for Monitoring Vital Parameters against Reference Standard: A Diagnostic Validation Study for COVID-19 Preparedness

CONTEXT

Vital parameters including blood oxygen level, respiratory rate, pulse rate, and body temperature are crucial for triaging patients to appropriate medical care. Advances in remote health monitoring system and wearable health devices have created a new horizon for delivery of efficient health care from a distance.

MATERIALS AND METHODS

This diagnostic validation study included patients attending the outpatient department of the institute. The accuracy of device under study was compared against the gold standard patient monitoring systems used in intensive care units.

STATISTICAL ANALYSIS

The statistical analysis involved computation of intraclass correlation coefficient. Bland-Altman graphs with limits of agreement were plotted to assess agreement between methods. P <0.05 was considered statistically significant.

RESULTS

A total of 200 patients, including 152 males and 48 females in the age range of 2-80 years, formed the study group. A strong correlation (intraclass correlation coefficient; r > 0.9) was noted between the two devices for all the investigated parameters with significant P value (<0.01). Bland-Altman plot drawn for each vital parameter revealed observations in agreement from both the devices.

CONCLUSION

The wearable device can be reliably used for remote health monitoring. Its regulated use can help mitigate the scarcity of hospital beds and reduce exposure to health-care workers and demand of personal protection equipment.

Tidal volume via circumferences of the upper body: a pilot study

The gold standard for tidal volume measurement is spirometry. Based on retrospective data, this study evaluates different geometric lung models in their ability to deliver accurate tidal volumes from changes in thoracic and abdominal circumference. The geometric lung models showed good coefficients of determination (adjusted R² >0.97) compared to the tidal volumes measured by a body plethysmograph. Tidal volumes obtained by circumference changes might be used in surveillance systems to analyze respiration without a face mask.

Wearable devices for remote vital signs monitoring in the outpatient setting: an overview of the field

Early detection of physiological deterioration has been shown to improve patient outcomes. Due to recent improvements in technology, comprehensive outpatient vital signs monitoring is now possible. This is the first review to collate information on all wearable devices on the market for outpatient physiological monitoring.

A scoping review was undertaken. The monitors reviewed were limited to those that can function in the outpatient setting with minimal restrictions on the patient’s normal lifestyle, while measuring any or all of the vital signs: heart rate, ECG, oxygen saturation, respiration rate, blood pressure and temperature.

A total of 270 papers were included in the review. Thirty wearable monitors were examined: 6 patches, 3 clothing-based monitors, 4 chest straps, 2 upper arm bands and 15 wristbands. The monitoring of vital signs in the outpatient setting is a developing field with differing levels of evidence for each monitor. The most common clinical application was heart rate monitoring. Blood pressure and oxygen saturation measurements were the least common applications. There is a need for clinical validation studies in the outpatient setting to prove the potential of many of the monitors identified.

Research in this area is in its infancy. Future research should look at aggregating the results of validity and reliability and patient outcome studies for each monitor and between different devices. This would provide a more holistic overview of the potential for the clinical use of each device.

A Multimodal Wearable System for Continuous and Real-Time Breathing Pattern Monitoring During Daily Activity

OBJECTIVE

This study aims to understand breathing patterns during daily activities by developing a wearable respiratory and activity monitoring (WRAM) system.

METHODS

A novel multimodal fusion architecture is proposed to calculate the respiratory and exercise parameters and simultaneously identify human actions. A hybrid hierarchical classification (HHC) algorithm combining deep learning and threshold-based methods is presented to distinguish 15 complex activities for accuracy enhancement and fast computation. A series of signal processing algorithms are utilized and integrated to calculate breathing and motion indices. The designed wireless communication structure achieves the interactions among chest bands, mobile devices, and the data processing center.

RESULTS

The advantage of the proposed HHC method is evaluated by comparing the average accuracy (97.22%) and predictive time (0.0094 s) with machine learning and deep learning approaches. The nine breathing patterns during 15 activities were analyzed by investigating the data from 12 subjects. With 12 hours of naturalistic data collected from one participant, the WRAM system reports the breathing and exercise performance within the identified motions. The demonstration shows the ability of the WRAM system to monitor multiple users breathing and exercise status in real-time.

CONCLUSION

The present system demonstrates the usefulness of the framework of breathing pattern monitoring during daily activities, which may be potentially used in healthcare.

SIGNIFICANCE

The proposed multimodal based WRAM system offers new insights into the breathing function of exercise in action and presents a novel approach for precision medicine and health state monitoring.

Multi-Sensor-Fusion Approach for a Data-Science-Oriented Preventive Health Management System: Concept and Development of a Decentralized Data Collection Approach for Heterogeneous Data Sources

Investigations in preventive and occupational medicine are often based on the acquisition of data in the customer's daily routine. This requires convenient measurement solutions including physiological, psychological, physical, and sometimes emotional parameters. In this paper, the introduction of a decentralized multi-sensor-fusion approach for a preventive health-management system is described. The aim is the provision of a flexible mobile data-collection platform, which can be used in many different health-care related applications. Different heterogeneous data sources can be integrated and measured data are prepared and transferred to a superordinated data-science-oriented cloud-solution. The presented novel approach focuses on the integration and fusion of different mobile data sources on a mobile data collection system (mDCS). This includes directly coupled wireless sensor devices, indirectly coupled devices offering the datasets via vendor-specific cloud solutions (as e.g., Fitbit, San Francisco, USA and Nokia, Espoo, Finland) and questionnaires to acquire subjective and objective parameters. The mDCS functions as a user-specific interface adapter and data concentrator decentralized from a data-science-oriented processing cloud. A low-level data fusion in the mDCS includes the synchronization of the data sources, the individual selection of required data sets and the execution of pre-processing procedures. Thus, the mDCS increases the availability of the processing cloud and in consequence also of the higher level data-fusion procedures. The developed system can be easily adapted to changing health-care applications by using different sensor combinations. The complex processing for data analysis can be supported and intervention measures can be provided.

Digital health for COPD care: the current state of play

Chronic obstructive pulmonary disease (COPD) imposes a huge burden to our healthcare systems and societies. To alleviate the burden, digital health-"the use of digital technologies for health"-has been recognized as a potential solution for improving COPD care at scale. The aim of this review is to provide an overview of digital health interventions in COPD care. We accordingly reviewed recent and emerging evidence on digital transformation approaches for COPD care focusing on (I) self-management, (II) in-hospital care, (III) post-discharge care, (IV) hospital-at-home, (V) ambient environment, and (VI) public health surveillance. The emerging approaches included digital-technology-enabled homecare programs, electronic records, big data analytics, and environment-monitoring applications. The digital health approaches of telemonitoring, telehealth and mHealth support the self-management, post-discharge care, and hospital-at-home strategy, with prospective effects on reducing acute COPD exacerbations and hospitalizations. Electronic records and classification tools have been implemented; and their effectiveness needs to be further evaluated in future studies. Air pollution concentrations in the ambient environment are associated with declined lung functions and increased risks for hospitalization and mortality. In all the digital transformation approaches, clinical evidence on reducing mortality, the ultimate goal of digital health intervention, is often inconsistent or insufficient. Digital health transformation provides great opportunities for clinical innovations and discovery of new intervention strategies. Further research remains needed for achieving reliable improvements in clinical outcomes and cost-benefits in future studies.

Dual-Core Capacitive Microfiber Sensor for Smart Textile Applications

Wearable sensors for smart textile applications have garnered tremendous interest in recent years and can have enormous potential for human machine interfaces and digital health monitoring. Here, we report a soft capacitive microfiber sensor that can be woven seamlessly into textiles for strain measurement. Comprising a dual-lumen elastomeric microtube and liquid metallic alloy, the microfiber sensor enables continual strain perception even after being completely severed. In addition, our microfiber sensor is highly stretchable and flexible and exhibits tunable sensitivity, excellent linearity, a fast response, and negligible hysteresis. More importantly, the microfiber sensor is minimally affected by train rate and compression during strain sensing. Even under drastic environmental changes, the microfiber sensor presents good electrical stability. By integrating the microfiber sensor imperceptibly with textiles, we devise smart textile wearables to interpret hand gestures, detect limb motion, and monitor respiration rate. We believe that this sensor presents enormous potential in unobtrusive continuous health monitoring.

Indoor Air Quality in Inpatient Environments: A Systematic Review on Factors that Influence Chemical Pollution in Inpatient Wards

Introduction

Indoor air quality is one the main issues in which governments are focusing. In healing spaces, several research studies are reporting a growing number of data analysis and research works in order to guarantee and prevent health of users and workers. Currently the main investigations are about biological and physical risks; otherwise chemical ones are less investigated. Several countries are carrying out indoor air quality monitoring in those professional workplaces in which chemicals are used but also in some typically indoor (generic) spaces for the building hygiene assessment. The indoor air is affected by several factors that currently are analyzed punctually, without a whole scenario of all the variable performances. The authors have done a systematic review on the current state of the art and knowledge related to chemical pollution in healing spaces and the emerging strategies, supported by scientific literature, for healthy inpatient rooms and their indoor air.

Methodology

The systematic review has been done through the analysis of papers from SCOPUS, DOAJ, and PubMed databases. The survey sample considered 483 scientific articles, between 1989 and 2017, and starting the systematic reading and analysis of the abstracts, only 187 scientific papers were selected, and only 96 were accessible.

Discussion

Since scientific literature reports very different outputs and results, the resulting work from the survey is divided into specific fields of interest related to construction and finishing materials, installations, components, ventilation systems, processes, etc. Starting from the systematic reading, the paper classifies the factors of indoor air in four macroareas: outdoor air and microclimatic factors (temperature, relative humidity, air velocity, air change, etc.); management activities (management and maintenance activities, ventilation systems, HVAC, cleaning and disinfectant activities, etc.); design factors (room dimensions, furniture, finishing materials, etc.); and human presence and medical activities (users' presence, their health status, and medical activities carried out in inpatient rooms).

Conclusion

The systematic review gives rise to a broad scenario on the existing knowledge regarding the indoor air pollution, design, and management strategies for healthy spaces and several emerging topics. Although the aim of the investigation is strictly related to chemical pollution, several considerations from the biological point of view have been listed. The systematic review, supported by the existing scientific literature, becomes a starting point for considering the importance of the topic and to stimulate the knowledge around this field of interest for improving studies, analysis, and simulations.

Respiratory Motion Sensor Measuring Capacitance Constructed across Skin in Daily Activities

In this work, a respiratory sensor is studied, measuring the capacitance constructed by attached electrodes on the abdomen. Based on previous findings, that skin thickness changes caused by respiration provides the signal, the fitting condition of the electrode on the skin is stabilized using a 7-μm-thick dressing film. This film can be comfortably worn for a long time, while maintaining the electrode's position on the skin. This stabilized setup enables the detection of, not only respiration, as the cyclic capacitance change, but also of minute body volume changes over the daytime, as a change in the base line indicates the quality of the sensor signal. For this demonstration, the respiration signal is measured during the daily activity of exercise and 6-min walks.

Smart Vest for Respiratory Rate Monitoring of COPD Patients Based on Non-Contact Capacitive Sensing

In this paper, a first approach to the design of a portable device for non-contact monitoring of respiratory rate by capacitive sensing is presented. The sensing system is integrated into a smart vest for an untethered, low-cost and comfortable breathing monitoring of Chronic Obstructive Pulmonary Disease (COPD) patients during the rest period between respiratory rehabilitation exercises at home. To provide an extensible solution to the remote monitoring using this sensor and other devices, the design and preliminary development of an e-Health platform based on the Internet of Medical Things (IoMT) paradigm is also presented. In order to validate the proposed solution, two quasi-experimental studies have been developed, comparing the estimations with respect to the golden standard. In a first study with healthy subjects, the mean value of the respiratory rate error, the standard deviation of the error and the correlation coefficient were 0.01 breaths per minute (bpm), 0.97 bpm and 0.995 (p < 0.00001), respectively. In a second study with COPD patients, the values were −0.14 bpm, 0.28 bpm and 0.9988 (p < 0.0000001), respectively. The results for the rest period show the technical and functional feasibility of the prototype and serve as a preliminary validation of the device for respiratory rate monitoring of patients with COPD.

Qualitative and quantitative evaluation of a new wearable device for ECG and respiratory Holter monitoring

BACKGROUND

Recent advances in wearable technology make continuous cardiorespiratory monitoring possible, with potential applications in assessment of cardiopulmonary patients, healthy subjects and athletes. The aim of the present study was to qualitatively and quantitatively evaluate a new wearable device (Learn Inspire Free Entertain = L.I.F.E.) by embedding in a compression shirt a 12‑lead ECG system and 5 respiratory sensors.

METHODS

Thirty cardiorespiratory patients and ten healthy subjects were studied for 24 h during their usual life activities. In 8 healthy subjects, simultaneous measurements of the device and of an ergo-spirometer were performed during different levels of ventilation in five different body positions. The quality of ECG signals in terms of measurability of heart rate, P wave, QRS complex and ST segment, was analyzed by four expert cardiologists/respiratory physiologists using an arbitrary 1-5 scale. The sum of the respiratory signals was used to calculate the respiratory rate, inspiratory time and relative changes of tidal volume. These parameters were compared to ergo-spirometer measurements.

RESULTS

Median quality value was >3 for heart rate, QRS complex, ST segment and P wave (except in L3, aVL, aVF, V1 and V2 leads). Median quality of respiratory traces was >4 in patients and between 3 and 4 in healthy subjects. The respiratory monitoring of respiratory rate and inspiratory time was accurate in all body positions. Tidal volumes were underestimated due to a high level of ventilation.

CONCLUSIONS

The L.I.F.E. device provides an accurate continuous monitoring of cardiorespiratory signals during the 24 h both in normal subjects and cardiorespiratory patients.

Smart textile for respiratory monitoring and thoraco-abdominal motion pattern evaluation

The use of wearable systems for monitoring vital parameters has gained wide popularity in several medical fields. The focus of the present study is the experimental assessment of a smart textile based on 12 fiber Bragg grating sensors for breathing monitoring and thoraco-abdominal motion pattern analysis. The feasibility of the smart textile for monitoring several temporal respiratory parameters (ie, breath-by-breath respiratory period, breathing frequency, duration of inspiratory and expiratory phases), volume variations of the whole chest wall and of its compartments is performed on 8 healthy male volunteers. Values gathered by the textile are compared to the data obtained by a motion analysis system, used as the reference instrument. Good agreement between the 2 systems on both respiratory period (bias of 0.01 seconds), breathing frequency (bias of -0.02 breaths/min) and tidal volume (bias of 0.09 L) values is demonstrated. Smart textile shows good performance in the monitoring of thoraco-abdominal pattern and its variation, as well.

The role of tele-medicine in patients with respiratory diseases

INTRODUCTION

Tele-medicine is a clinical application connecting a patient with specialized care consultants by means of electronic platforms, potentially able to improve patients' self-management and allow for the care of patients with limited access to health services. This article summarizes the use of tele-medicine as a tool in managing patients suffering from some pathological respiratory conditions. Areas covered: We searched papers published between 1990 and 2017 dealing with tele-medicine and respiratory diseases, chronic obstructive pulmonary disease, asthma, interstitial lung disease, chronic respiratory failure, neuromuscular diseases, critical illness, home mechanical ventilation, and also legal and economic issues. Controlled trials report different results on feasibility, cost-effectiveness, and safety of tele-medicine. Expert commentary: Progress in tele-medicine widens the horizons in respiratory medicine: this tool may potentially reduce health care costs by moving some medical interventions from centralized locations in to patient's home, also allowing for the delivery of care in countries with limited access to it. Legal, safety, and privacy problems, as well as reimbursement issues, must still be defined and solved. At present time, we still need much more evidence to consider this modality as a real option in the management of these patients.