Low-Power Wearable Systems for Continuous Monitoring of Environment and Health for Chronic Respiratory Disease

iPREDICT: Characterization of Asthma Triggers and Selection of Digital Technology to Predict Changes in Disease Control

Purpose

The iPREDICT program aimed to develop an integrated digital health solution capable of continuous data streaming, predicting changes in asthma control, and enabling early intervention.

Patients and Methods

As part of the iPREDICT program, asthma triggers were characterized by surveying 221 patients (aged ≥18 years) with self-reported asthma for a risk-benefit analysis of parameters predictive of changes in disease control. Seventeen healthy volunteers (age 25-65 years) tested 13 devices to measure these parameters and assessed their usability attributes.

Results

Patients identified irritants such as chemicals, allergens, weather changes, and physical activity as triggers that were the most relevant to deteriorating asthma control. Device testing in healthy volunteers revealed variable data formats/units and quality issues, such as missing data and low signal-to-noise ratio. Based on user preference and data capture validity, a spirometer, vital sign monitor, and sleep monitor formed the iPREDICT integrated system for continuous data streaming to develop a personalized/predictive algorithm for asthma control.

Conclusion

These findings emphasize the need to systematically compare devices based on several parameters, including usability and data quality, to develop integrated digital technology programs for asthma care.

Advanced Energy Harvesters and Energy Storage for Powering Wearable and Implantable Medical Devices

Wearable and implantable active medical devices (WIMDs) are transformative solutions for improving healthcare, offering continuous health monitoring, early disease detection, targeted treatments, personalized medicine, and connected health capabilities. Commercialized WIMDs use primary or rechargeable batteries to power their sensing, actuation, stimulation, and communication functions, and periodic battery replacements of implanted active medical devices pose major risks of surgical infections or inconvenience to users. Addressing the energy source challenge is critical for meeting the growing demand of the WIMD market that is reaching valuations in the tens of billions of dollars. This review critically assesses the recent advances in energy harvesting and storage technologies that can potentially eliminate the need for battery replacements. With a key focus on advanced materials that can enable energy harvesters to meet the energy needs of WIMDs, this review examines the crucial roles of advanced materials in improving the efficiencies of energy harvesters, wireless charging, and energy storage devices. This review concludes by highlighting the key challenges and opportunities in advanced materials necessary to achieve the vision of self-powered wearable and implantable active medical devices, eliminating the risks associated with surgical battery replacement and the inconvenience of frequent manual recharging.

An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning

Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient's chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time-frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set-outperforming the deterministic time-frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously.

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.

iPREDICT: proof-of-concept study to develop a predictive model of changes in asthma control

BACKGROUND

The individualized PREdiction of DIsease Control using digital sensor Technology (iPREDICT) program was developed for asthma management using digital technology. Devices were integrated into daily lives of patients to establish a predictive model of asthma control by measuring changes from baseline health status with minimal device burden.

OBJECTIVES

To establish baseline disease characteristics of the study participants, detect changes from baseline associated with asthma events, and evaluate algorithms capable of identifying triggers and predicting asthma control changes from baseline data. Patient experience and compliance with the devices were also explored.

DESIGN

This was a multicenter, observational, 24-week, proof-of-concept study conducted in the United States.

METHODS

Patients (⩾12 years) with severe, uncontrolled asthma engaged with a spirometer, vital sign monitor, sleep monitor, connected inhaler devices, and two mobile applications with embedded patient-reported outcome (PRO) questionnaires. Prospective data were linked to data from electronic health records and transmitted to a secure platform to develop predictive algorithms. The primary endpoint was an asthma event: symptom worsening logged by patients (PRO); peak expiratory flow (PEF) < 65% or forced expiratory volume in 1 s < 80%; increased short-acting β2-agonist (SABA) use (>8 puffs/24 h or >4 puffs/day/48 h). For each endpoint, predictive models were constructed at population, subgroup, and individual levels.

RESULTS

Overall, 108 patients were selected: 66 (61.1%) completed and 42 (38.9%) were excluded for failure to respond/missing data. Predictive accuracy depended on endpoint selection. Population-level models achieved low accuracy in predicting endpoints such as PEF < 65%. Subgroups related to specific allergies, asthma triggers, asthma types, and exacerbation treatments demonstrated high accuracy, with the most accurate, predictive endpoint being >4 SABA puffs/day/48 h. Individual models, constructed for patients with high endpoint overlap, exhibited significant predictive accuracy, especially for PEF < 65% and >4 SABA puffs/day/48 h.

CONCLUSION

This multidimensional dataset enabled population-, subgroup-, and individual-level analyses, providing proof-of-concept evidence for development of predictive models of fluctuating asthma control.

A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis

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

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Behavioral Interpretation of Willingness to Use Wearable Health Devices in Community Residents: A Cross-Sectional Study

Wearable health devices (WHDs) have become increasingly advantageous in long-term health monitoring and patient management. However, most people have not yet benefited from such innovative technologies, and the willingness to accept WHDs and their influencing factors are still unclear. Based on two behavioral theories: the theory of planned behavior (TPB) and the diffusion of innovation (DOI), this study aims to explore the influencing factors of willingness to use WHDs in community residents from the perspective of both internal and external factors. A convenience sample of 407 community residents were recruited from three randomly selected Community Health Service Centers (CHSCs) in Nanjing, China, and were investigated with a self-developed questionnaires. The mean score of willingness to use WHDs was 17.00 (range 5-25). In the dimensions of TPB, perceived behavioral control (β = 1.979, p < 0.001) was the strongest influencing factor. Subjective norms (β = 1.457, p < 0.001) and attitudes (β = 0.651, p = 0.016) were also positively associated with willingness. In innovation characteristics of DOI, compatibility (β = 0.889, p < 0.001) and observability (β = 0.576, p = 0.003) had positive association with the willingness to wear a WHD. This study supports the applicability of the two behavioral theories to interpret the willingness to use WHDs in Chinese community residents. Compared with the innovative features of WHDs, individual cognitive factors were more critical predictors of willingness to use.

Self-Sustainable Biomedical Devices Powered by RF Energy: A Review

Wearable and implantable medical devices (IMDs) have come a long way in the past few decades and have contributed to the development of many personalized health monitoring and therapeutic applications. Sustaining these devices with reliable and long-term power supply is still an ongoing challenge. This review discusses the challenges and milestones in energizing wearable and IMDs using the RF energy harvesting (RFEH) technique. The review highlights the main integrating frontend blocks such as the wearable and implantable antenna design, matching network, and rectifier topologies. The advantages and bottlenecks of adopting RFEH technology in wearable and IMDs are reviewed, along with the system elements and characteristics that enable these devices to operate in an optimized manner. The applications of RFEH in wearable and IMDs medical devices are elaborated in the final section of this review. This article summarizes the recent developments in RFEH, highlights the gaps, and explores future research opportunities.

A Scoping Review on Wearable Devices for Environmental Monitoring and Their Application for Health and Wellness

This scoping review is focused on wearable devices for environmental monitoring. First, the main pollutants are presented, followed by sensing technologies that are used for the parameters of interest. Selected examples of wearables and portables are divided into commercially available and research-level projects. While many commercial products are in fact portable, there is an increasing interest in using a completely wearable technology. This allows us to correlate the pollution level to other personal information (performed activity, position, and respiratory parameters) and thus to estimate personal exposure to given pollutants. The fact that there are no univocal indices to estimate outdoor or indoor air quality is also an open problem. Finally, applications of wearables for environmental monitoring are discussed. Combining environmental monitoring with other devices would permit better choices of where to perform sports activities, especially in highly polluted areas, and provide detailed information on the living conditions of individuals.

First Steps of Asthma Management with a Personalized Ontology Model

Asthma is a chronic respiratory disease characterized by severe inflammation of the bronchial mucosa. Allergic asthma is the most common form of this health issue. Asthma is classified into allergic and non-allergic asthma, and it can be triggered by several factors such as indoor and outdoor allergens, air pollution, weather conditions, tobacco smoke, and food allergens, as well as other factors. Asthma symptoms differ in their frequency and severity since each patient reacts differently to these triggers. Formal knowledge is selected as one of the most promising solutions to deal with these challenges. This paper presents a new personalized approach to manage asthma. An ontology-driven model supported by Semantic Web Rule Language (SWRL) medical rules is proposed to provide personalized care for an asthma patient by identifying the risk factors and the development of possible exacerbations.

Impact of the Moderating Effect of National Culture on Adoption Intention in Wearable Health Care Devices: Meta-analysis

BACKGROUND

Wearable health care devices have not yet been commercialized on a large scale. Additionally, people in different countries have different utilization rates. Therefore, more in-depth studies on the moderating effect of national culture on adoption intention in wearable health care devices are necessary.

OBJECTIVE

This study aims to explore the summary results of the relationships between perceived usefulness and perceived ease of use with adoption intention in wearable health care devices and the impact of the moderating effect of national culture on these two relationships.

METHODS

We searched for studies published before September 2021 in the Web of Science, EBSCO, Engineering Village, China National Knowledge Infrastructure, IEEE Xplore, and Wiley Online Library databases. CMA (version 2.0, Biostat Inc) software was used to perform the meta-analysis. We conducted publication bias and heterogeneity tests on the data. The random-effects model was used to estimate the main effect size, and a sensitivity analysis was conducted. A meta-regression analysis was used to test the moderating effect of national culture.

RESULTS

This meta-analysis included 20 publications with a total of 6128 participants. Perceived usefulness (r=0.612, P<.001) and perceived ease of use (r=0.462, P<.001) positively affect adoption intention. The relationship between perceived usefulness and adoption intention is positively moderated by individualism/collectivism (β=.003, P<.001), masculinity/femininity (β=.008, P<.001) and indulgence/restraint (β=.005, P<.001), and negatively moderated by uncertainty avoidance (β=-.005, P<.001). The relationship between perceived ease of use and adoption intention is positively moderated by individualism/collectivism (β=.003, P<.001), masculinity/femininity (β=.006, P<.001) and indulgence/restraint (β=.009, P<.001), and negatively moderated by uncertainty avoidance (β=-.004, P<.001).

CONCLUSIONS

This meta-analysis provided comprehensive evidence on the positive relationship between perceived usefulness and perceived ease of use with adoption intention and the moderating effect of national culture on these two relationships. Regarding the moderating effect, perceived usefulness and perceived ease of use have a greater impact on adoption intention for people in individualistic, masculine, low uncertainty avoidance, and indulgence cultures, respectively.

Review on Wearable Thermoelectric Generators: From Devices to Applications

Wearable thermoelectric generators (WTEGs) can incessantly convert body heat into electricity to power electronics. However, the low efficiency of thermoelectric materials, tiny terminal temperature difference, rigidity, and neglecting optimization of lateral heat transfer preclude WTEGs from broad utilization. In this review, we aim to comprehensively summarize the state-of-the-art strategies for the realization of flexibility and high normalized power density in thermoelectric generators by establishing the links among materials, TE performance, and advanced design of WTEGs (structure, heatsinks, thermal regulation, thermal analysis, etc.) based on inorganic bulk TE materials. Each section starts with a concise summary of its fundamentals and carefully selected examples. In the end, we point out the controversies, challenges, and outlooks toward the future development of wearable thermoelectric devices and potential applications. Overall, this review will serve to help materials scientists, electronic engineers, particularly students and young researchers, in selecting suitable thermoelectric devices and potential applications.

Low-Cost Thermohygrometers to Assess Thermal Comfort in the Built Environment: A Laboratory Evaluation of Their Measurement Performance

A thermohygrometer is an instrument that is able to measure relative humidity and air temperature, which are two of the fundamental parameters to estimate human thermal comfort. To date, the market offers small and low-cost solutions for this instrument, providing the opportunity to bring electronics closer to the end-user and contributing to the proliferation of a variety of applications and open-source projects. One of the most critical aspects of using low-cost instruments is their measurement reliability. This study aims to determine the measurement performance of seven low-cost thermohygrometers throughout a 10-fold repeatability test in a climatic chamber with air temperatures ranging from about −10 to +40 °C and relative humidity from approximately 0 to 90%. Compared with reference sensors, their measurements show good linear behavior with some exceptions. A sub-dataset of the data collected is then used to calculate two of the most used indoor (PMV) and outdoor (UTCI) comfort indexes to define discrepancies between the indexes calculated with the data from the reference sensors and the low-cost sensors. The results suggest that although six of the seven low-cost sensors have accuracy that meets the requirements of ISO 7726, in some cases, they do not provide acceptable comfort indicators if the values are taken as they are. The linear regression analysis suggests that it is possible to correct the output to reduce the difference between reference and low-cost sensors, enabling the use of low-cost sensors to assess indoor thermal comfort in terms of PMV and outdoor thermal stress in UTCI and encouraging a more conscious use for environmental and human-centric research.

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.

Digital Healthcare for Airway Diseases from Personal Environmental Exposure

Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies' abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling.

Preliminary Evaluation of a Solar-Powered Wristband for Continuous Multi-Modal Electrochemical Monitoring

Continuous, non-invasive wearable measurement of metabolic biomarkers could provide vital insight into patient condition for personalized health and wellness monitoring. We present our efforts towards developing a wearable solar-powered electrochemical platform for multimodal sweat based metabolic monitoring. This wrist-worn wearable system consists of a flexible photovoltaic cell connected to a circuit board containing ultra low power circuitry for sensor data collection, energy harvesting, and wireless data transmission, all integrated into an elastic fabric wristband. The system continuously samples amperometric, potentiometric, temperature, and motion data and wirelessly transmits these to a data aggregator. The full wearable system is 7.5 cm long and 5 cm in diameter, weighs 22 grams, and can run directly from harvested light energy. Relatively low levels of light such as residential lighting (∼200 lux) are sufficient for continuous operation of the system. Excess harvested energy is stored in a small 37 mWh lithium polymer battery. The battery can be charged in ∼14 minutes under full sunlight and can power the system for ∼8 days when fully charged. The system has an average power consumption of 176 µW. The solar-harvesting performance of the system was characterized in a variety of lighting conditions, and the amperometric and potentiometric electrochemical capabilities of the system were validated in vitro.Clinical relevance-The presented solar-powered wearable system enables continuous wireless multi-modal electrochemical monitoring for uninterrupted sensing of metabolic biomarkers in sweat while harvesting energy from indoor lighting or sunlight.

2D Materials for Skin-Mountable Electronic Devices

Skin-mountable devices that can directly measure various biosignals and external stimuli and communicate the information to the users have been actively studied owing to increasing demand for wearable electronics and newer healthcare systems. Research on skin-mountable devices is mainly focused on those materials and mechanical design aspects that satisfy the device fabrication requirements on unusual substrates like skin and also for achieving good sensing capabilities and stable device operation in high-strain conditions. 2D materials that are atomically thin and possess unique electrical and optical properties offer several important features that can address the challenging needs in wearable, skin-mountable electronic devices. Herein, recent research progress on skin-mountable devices based on 2D materials that exhibit a variety of device functions including information input and output and in vitro and in vivo healthcare and diagnosis is reviewed. The challenges, potential solutions, and perspectives on trends for future work are also discussed.

Atrial Fibrillation Detection on Low-Power Wearables using Knowledge Distillation

The increasing complexity and memory requirements of neural networks have been slowing down the adoption of AI in low-power wearable devices, which impose important restrictions in computational power and memory footprint. These low-power systems are the key to obtain 24/7 monitoring systems necessary for the current personalized healthcare trend since they do not require constant charging. In this work, we apply Knowledge Distillation to our previously published convolutional-recurrent neural network for cardiac arrhythmia detection and classification. We show that the resulting network halves the memory footprint (138 K parameters) and the number of operations (1.84 MOp) compared to the baseline. By using Knowledge Distillation, this network also achieves significantly higher accuracy after quantization (increase in overall F₁ score from 0.779 to 0.828) and is capable of running into a nRF52832 System-on-Chip from Nordic Semiconductors. This promising result lays the groundwork for deployment on resource-constrained embedded platforms such as micro-controllers of the ARM Cortex-M family, thus potentially enabling continuous detection of cardiac arrhythmias in low-power wearable devices.

Towards a Self-Powered ECG and PPG Sensing Wearable Device

This paper presents a multifunctional sensor interface system-on-chip (SoC) for developing self-powered Electrocardiography (ECG) and Photoplethysmography (PPG) sensing wearable devices. The proposed SoC design consists of switch-capacitor-based LED driver and analog front-end (AFE) for PPG sensing, ECG sensing AFE, and power management unit for energy harvesting from Thermoelectric Generator (TEG), all integrated on a 2×2.5 mm² chip fabricated in 0.18μm standard CMOS process. We have performed post-layout simulation to verify the functionality and performance of the SoC. The LED driver employs the switch-capacitor-based architecture, which charges a storage capacitor up to 2.1 V and discharges accumulated charge to pass instantaneous current up to 40 mA through a selected LED. The PPG AFE converts the resulting photodiode (PD) current to voltage output with adjustable gain of 114-120 dBΩ and input-referred noise of 119 pA_RMS within 0.4 Hz-10 kHz. The ECG AFE provides adjustable mid-band gain of 47-63 dB, low-cut frequency of 1.5-6.3 Hz, and input-referred noise of 7.83 µV_RMS within 1.5 Hz- 1.2 kHz to amplify/filter the recorded ECG signals. The power management unit is able to perform sufficient energy harvesting with the TEG output voltage as low as 350 mV.

Investigating the Relationship between Cough Detection and Sampling Frequency for Wearable Devices

Cough detection can provide an important marker to monitor chronic respiratory conditions. However, manual techniques which require human expertise to count coughs are both expensive and time-consuming. Recent Automatic Cough Detection Algorithms (ACDAs) have shown promise to meet clinical monitoring requirements, but only in recent years they have made their way to non-clinical settings due to the required portability of sensing technologies and the extended duration of data recording. More precisely, these ACDAs operate at high sampling frequencies, which leads to high power consumption and computing requirements, making these difficult to implement on a wearable device. Additionally, reproducibility of their performance is essential. Unfortunately, as the majority of ACDAs were developed using private clinical data, it is difficult to reproduce their results. We, hereby, present an ACDA that meets clinical monitoring requirements and reliably operates at a low sampling frequency. This ACDA is implemented using a convolutional neural network (CNN), and publicly available data. It achieves a sensitivity of 92.7%, a specificity of 92.3%, and an accuracy of 92.5% using a sampling frequency of just 750 Hz. We also show that a low sampling frequency allows us to preserve patients' privacy by obfuscating their speech, and we analyze the trade-off between speech obfuscation for privacy and cough detection accuracy.Clinical relevance-This paper presents a new cough detection technique and preliminary analysis on the trade-off between detection accuracy and obfuscation of speech for privacy. These findings indicate that, using a publicly available dataset, we can sample signals at 750 Hz while still maintaining a sensitivity above 90%, suggested to be sufficient for clinical monitoring [1].

CMOS-MEMS VOC sensors functionalized via inkjet polymer deposition for high-sensitivity acetone detection

CMOS-MEMS microresonators have become excellent candidates for developing portable chemical VOC sensing systems thanks to their extremely large mass sensitivity, extraordinary miniaturization capabilities, and on-chip integration with CMOS circuitry to operate as a self-sustained oscillator. This paper presents two 4-anchored MEMS plate resonators, with a resonance frequency of 2.2 MHz and 380 kHz, fabricated together with the required circuitry using a commercial 0.35 μm CMOS technology and then coated with poly-4-vinylheduorocumyl alcohol (P4V) via inkjet deposition. Such P4V constitutes a functionalization layer for specific acetone detection as a key step in the development of an integrated device for non-invasive diabetes diagnosis through exhaled human breath. The coated sensor system has been proven to increase the acetone injection response by 6-times compared to the uncoated platform and shows a cross-sensitivity to butane of 1 : 11. Experimental data show an acetone sensitivity of -0.012 ppm Hz^-1 in the best case that, together with a measured frequency Allan deviation of 0.32 ppm, provides an expected limit of detection as low as 20 ppb of acetone. Additionally, this work presents an alternative resonator design with folded flexure anchors that provide a drastic reduction of the sensor temperature sensitivity and mitigate the impact of a fluid flow inherent to the calibration system.

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.

Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review

The so-called Internet of Things (IoT), which is rapidly increasing the number of network-connected and interconnected objects, could have a far-reaching impact in identifying the link between human health, well-being, and environmental concerns. In line with the IoT concept, many commercial wearables have been introduced in recent years, which differ from the usual devices in that they use the term "smart" alongside the terms "watches", "glasses", and "jewellery". Commercially available wearables aim to enhance smartphone functionality by enabling payment for commercial items or monitoring physical activity. However, what is the trend of scientific production about the concept of wearables regarding environmental monitoring issues? What are the main areas of interest covered by scientific production? What are the main findings and limitations of the developed solution in this field? The methodology used to answer the above questions is based on a systematic review. The data were acquired following a reproducible methodology. The main result is that, among the thermal, visual, acoustic, and air quality environmental factors, the last one is the most considered when using wearables even though in combination with some others. Another relevant finding is that of the acquired studies; in only one, the authors shared their wearables as an open-source device, and it will probably be necessary to encourage researchers to consider open-source as a means to promote scalability and proliferation of new wearables customized to cover different domains.

Wearable Respiration Monitoring: Interpretable Inference With Context and Sensor Biomarkers

Continuous monitoring of breathing rate (BR), minute ventilation (VE), and other respiratory parameters could transform care for and empower patients with chronic cardio-pulmonary conditions, such as asthma. However, the clinical standard for measuring respiration, namely Spirometry, is hardly suitable for continuous use. Wearables can track many physiological signals, like ECG and motion, yet respiration tracking faces many challenges. In this work, we infer respiratory parameters from wearable ECG and wrist motion signals. We propose a modular and generalizable classification-regression pipeline to utilize available context information, such as physical activity, in learning context-conditioned inference models. Novel morphological and power domain features from the wearable ECG are extracted to use with these models. Exploratory feature selection methods are incorporated in this pipeline to discover application-driven interpretable biomarkers. Using data from 15 subjects, we evaluate two implementations of the proposed inference pipeline: for BR and VE. Each implementation compares generalized linear model, random forest, support vector machine, Gaussian process regression, and neighborhood component analysis as regression models. Permutation, regularization, and relevance determination methods are used to rank the ECG features to identify robust ECG biomarkers across models and activities. This work demonstrates the potential of wearable sensors not only in continuous monitoring, but also in designing biomarker-driven preventive measures.

e-Health Technological Ecosystems: Advanced Solutions to Support Informal Caregivers and Vulnerable Populations During the COVID-19 Outbreak

Introduction: This study highlights the importance of technological ecosystems in supporting informal caregivers and vulnerable populations in coping with the ongoing coronavirus disease 2019 (COVID-19) pandemic. Methods: This study integrates the available literature on internet of things (IoT) e-health ecosystem and informal care. Results: In the first part of this article, we describe the health consequences of quarantine and isolation and outline the potential role of informal care in containing the risk of spreading the infection and reducing the burden on the health care system. Then, we present an overview of the characteristics of emerging technological ecosystems in health care and how they can be adopted as a strategic option to achieve different goals: (1) support informal carers to help vulnerable populations during quarantine and isolation and facilitate the recovery process; (2) promote the adoption of e-health and telemedicine resources to reduce the well-documented burden experienced by caregivers; and (3) lessen the various forms of digital disadvantage among vulnerable individuals, who are at more risk to be digitally excluded. In the last part of this work, we introduce solutions to overcome potential challenges related to the development and adoption of advanced technological ecosystems and propose a reflection on the legacy of COVID-19 on telemedicine. Conclusions: Thanks to the disruptive potential of IoT for health and wellness promotion, technological ecosystems emerge as a valuable resource to support both informal carers and vulnerable populations. The main factors that represent a strategic advantage of a technological ecosystem are affordability, regulatory, and availability. A high degree of interconnection between all the stakeholders emerges as a key element for the provision of intergenerational care. The most important technical challenges of IoT e-health require to optimize privacy, security, and user-friendliness of IoT e-health.

Wearable Devices in Health Monitoring from the Environmental towards Multiple Domains: A Survey

The World Health Organization (WHO) recognizes the environmental, behavioral, physiological, and psychological domains that impact adversely human health, well-being, and quality of life (QoL) in general. The environmental domain has significant interaction with the others. With respect to proactive and personalized medicine and the Internet of medical things (IoMT), wearables are most important for continuous health monitoring. In this work, we analyze wearables in healthcare from a perspective of innovation by categorizing them according to the four domains. Furthermore, we consider the mode of wearability, costs, and prolonged monitoring. We identify features and investigate the wearable devices in the terms of sampling rate, resolution, data usage (propagation), and data transmission. We also investigate applications of wearable devices. Web of Science, Scopus, PubMed, IEEE Xplore, and ACM Library delivered wearables that we require to monitor at least one environmental parameter, e.g., a pollutant. According to the number of domains, from which the wearables record data, we identify groups: G1, environmental parameters only; G2, environmental and behavioral parameters; G3, environmental, behavioral, and physiological parameters; and G4 parameters from all domains. In total, we included 53 devices of which 35, 9, 9, and 0 belong to G1, G2, G3, and G4, respectively. Furthermore, 32, 11, 7, and 5 wearables are applied in general health and well-being monitoring, specific diagnostics, disease management, and non-medical. We further propose customized and quantified output for future wearables from both, the perspectives of users, as well as physicians. Our study shows a shift of wearable devices towards disease management and particular applications. It also indicates the significant role of wearables in proactive healthcare, having capability of creating big data and linking to external healthcare systems for real-time monitoring and care delivery at the point of perception.

Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm.

A review of wearable and unobtrusive sensing technologies for chronic disease management

With the rapidly increasing number of patients with chronic disease, numerous recent studies have put great efforts into achieving long-term health monitoring and patient management. Specifically, chronic diseases including cardiovascular disease, chronic respiratory disease and brain disease can threaten patients' health conditions over a long period of time, thus effecting their daily lives. Vital health parameters, such as heart rate, respiratory rate, SpO2 and blood pressure, are closely associated with patients’ conditions. Wearable devices and unobtrusive sensing technologies can detect such parameters in a convenient way and provide timely predictions on health condition deterioration by tracking these biomedical signals and health parameters. In this paper, we review current advancements in wearable devices and unobtrusive sensing technologies that can provides possible tools and technological supports for chronic disease management. Current challenges and future directions of related techniques are addressed accordingly.

Hardware Prototype for Wrist-Worn Simultaneous Monitoring of Environmental, Behavioral, and Physiological Parameters

We designed a low-cost wrist-worn prototype for simultaneously measuring environmental, behavioral, and physiological domains of influencing factors in healthcare. Our prototype continuously monitors ambient elements (sound level, toxic gases, ultraviolet radiation, air pressure, temperature, and humidity), personal activity (motion tracking and body positioning using gyroscope, magnetometer, and accelerometer), and vital signs (skin temperature and heart rate). An innovative three-dimensional hardware, based on the multi-physical-layer approach is introduced. Using board-to-board connectors, several physical hardware layers are stacked on top of each other. All of these layers consist of integrated and/or add-on sensors to measure certain domain (environmental, behavioral, or physiological). The prototype includes centralized data processing, transmission, and visualization. Bi-directional communication is based on Bluetooth Low Energy (BLE) and can connect to smartphones as well as smart cars and smart homes for data analytic and adverse-event alerts. This study aims to develop a prototype for simultaneous monitoring of the all three areas for monitoring of workplaces and chronic obstructive pulmonary disease (COPD) patients with a concentration on technical development and validation rather than clinical investigation. We have implemented 6 prototypes which have been tested by 5 volunteers. We have asked the subjects to test the prototype in a daily routine in both indoor (workplaces and laboratories) and outdoor. We have not imposed any specific conditions for the tests. All presented data in this work are from the same prototype. Eleven sensors measure fifteen parameters from three domains. The prototype delivers the resolutions of 0.1 part per million (PPM) for air quality parameters, 1 dB, 1 index, and 1 °C for sound pressure level, UV, and skin temperature, respectively. The battery operates for 12.5 h under the maximum sampling rates of sensors without recharging. The final expense does not exceed 133€. We validated all layers and tested the entire device with a 75 min recording. The results show the appropriate functionalities of the prototype for further development and investigations.

Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review

The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.

Inferring Respiratory Minute Volume from Wrist Motion

Exposure to air pollutants poses major health risk for patients with chronic pulmonary diseases such as asthma, bronchitis, and emphysema. Such risk can be mitigated by continuous exposure tracking. The effective dose of exposure is directly proportional to the respiratory minute volume, aka minute ventilation (VE). Till date, the clinical standard for measuring VE is Spirometry, a highly invasive and cumbersome modality, which is not suitable for continuous day-to-day use. This paper presents a novel non-invasive method toward continuous assessment of VE using a wrist-mount wearable motion sensor. Data from 25 healthy subjects were collected while they performed ambulatory and sedentary activities and physical exercises. Noise and artifacts of the motion signal are removed and the processed signal is used to extract explanatory features. The features are used to train and evaluate multiple regression models, among which, the probabilistic Gaussian process regression achieves the best performance in inferring VE from the wearable motion signal. The effects of inter- and intra-personal variations are explored to demonstrate the potential of the proposed method for continuously monitoring pollutant exposure risk in respiratory health applications.

Nanomaterial-Enabled Flexible and Stretchable Sensing Systems: Processing, Integration, and Applications

Nanomaterial-enabled flexible and stretchable electronics have seen tremendous progress in recent years, evolving from single sensors to integrated sensing systems. Compared with nanomaterial-enabled sensors with a single function, integration of multiple sensors is conducive to comprehensive monitoring of personal health and environment, intelligent human-machine interfaces, and realistic imitation of human skin in robotics and prosthetics. Integration of sensors with other functional components promotes real-world applications of the sensing systems. Here, an overview of the design and integration strategies and manufacturing techniques for such sensing systems is given. Then, representative nanomaterial-enabled flexible and stretchable sensing systems are presented. Following that, representative applications in personal health, fitness tracking, electronic skins, artificial nervous systems, and human-machine interactions are provided. To conclude, perspectives on the challenges and opportunities in this burgeoning field are considered.

A Multi-Beam Shared-Inductor Reconfigurable Voltage/SECE Mode Piezoelectric Energy Harvesting Interface Circuit

This paper presents an autonomous multi-input (multi-beam) reconfigurable power-management chip for optimal energy harvesting from weak multi-axial human motion using a multi-beam piezoelectric energy harvester (PEH). The proposed chip adaptively operates in either voltage-mode or synchronous-electrical-charge-extraction-mode (VM-SECE) to improve overall efficiency, extract maximum energy regardless of the PEH beams' impedance/voltage/frequency variations, and protect the chip against large inputs, eliminating the need for high-voltage processes. It can simultaneously harvest energy from up to 6 beams using only one shared off-chip inductor. It uses an active negative voltage converter to extend the input-voltage range to as low as 35 mV. In addition, an active voltage doubler with a small footprint is implemented for faster cold start. A prototype VM-SECE chip was fabricated in a 0.35-μm 2P4M standard CMOS process occupying 1.9 mm² active area. To fully characterize the chip performance, it was tested with both a commercial single-beam PEH and a custom-made PEH with five mechanically plucked thin-film beams. With the commercial PEH, compared to an on-chip full-wave active rectifier (FAR) with 95.6% efficiency, the VM-SECE chip harvested 3.28x more power for shock inputs at 1 Hz frequency and 4.39 g acceleration. With the custom 5-beam PEH for a pseudo-walking condition, compared to the on-chip FAR, the VM-SECE chip harvested 1.59x and 2.38x more power for 1-and 5-beam operations, respectively.

Returning to our roots: The use of geospatial data for nurse-led community research

In the early 20th century, public health nurse, Lillian Wald, addressed the social determinants of health (SDOH) through her work in New York City and her advocacy to improve policy in workplace conditions, education, recreation, and housing. In the early 21st century, addressing the SDOH is a renewed priority and provides nurse researchers with an opportunity to return to our roots. The purpose of this methods paper is to examine how the incorporation of geospatial data and spatial methodologies in community research can enhance the analyses of the complex relationships between social determinants and health. Geospatial technologies, software for mapping and working with geospatial data, statistical methods, and unique considerations are discussed. An exemplar for using geospatial data is presented regarding associations between neighborhood greenspace, neighborhood violence, and children's asthma control. This innovative use of geospatial data illustrates a new frontier in investigating nontraditional connections between the environment and SDOH outcomes.

Developing a Low-Cost Wearable Personal Exposure Monitor for Studying Respiratory Diseases Using Metal-Oxide Sensors

Global industrialization and urbanization have led to increased levels of air pollution. Those with respiratory diseases, such as asthma, are at the highest risk for adverse health effects and reduced quality of life. Studying the relationship between pollutants and symptoms is usually achieved with data from government air quality monitoring stations, but these fail to report the spatial and temporal resolution required to track a person's true exposure, especially when the majority of their time is spent indoors. We develop and build eight wrist-worn wearable devices, weighing only 64 g, to measure known asthma symptom triggers: ozone, total volatile organic compounds, temperature, humidity, and activity level. The devices use commercial off-the-shelf components, costing under $150 each to build. This report focuses on the design, calibration, and testing of the devices. Emphasis is placed on the calibration of a metal-oxide-semiconductor gas sensor for detecting ozone, which is a difficult task because of the large variations in ambient temperature and humidity found when using a wearable device. Examples of testing the devices in four real environments are also discussed: 11 days inside a well-ventilated laboratory, ten days outdoors during the summer, alternating the devices between indoor and outdoor environments to examine their response to quickly changing environments, and a field test where scripted activities are performed for a full day. The work demonstrates a wearable device for environmental health studies and addresses the challenges of existing sensors for real-world applications.

Influence of the aromatic surface on the capacity of adsorption of VOCs by magnetite supported organic-inorganic hybrids

It has been recently evidenced that hybrid magnetic nanomaterials based on perylene diimide (PDI) dopamine and iron oxide nanoparticles are useful for the adsorption and determination of volatile organic compounds (VOCs). However, NDI compounds are expensive and difficult to handle compared to smaller size diimides. Therefore, in this manuscript a combined experimental and theoretical investigation is reported including the analysis of the effect of changing the aromatic surface on the ability of these magnetite supported organic-inorganic hybrid nanoparticles (NPs) to adsorb several aromatic and non-aromatic VOCs. In particular, two new hybrid Fe3O4NPs are synthesized and characterized where the size of organic PDI dopamine linker is progressively reduced to naphthalene diimide (NDI) and pyromellitic diimide (PMDI). These materials were utilized to fill two sorbent tubes in series. Thermal desorption (TD) combined with capillary gas chromatography (GC)/flame detector (FID) was used to analyze both front and back tubes. Adsorption values (defined as % VOCs found in the front tube) were determined for a series of VOCs. The binding energies (DFT-D3 calculations) of VOC-Fe3O4NP complexes were also computed to correlate the electron-accepting ability of the arylene diimide (PDI, NDI or PMDI) with the adsorption capacity of the different tubes. The prepared hybrids can be easily separated magnetically and showed great reusability.

An Active Self-Driven Piezoelectric Sensor Enabling Real-Time Respiration Monitoring

In this work, we report an active respiration monitoring sensor based on a piezoelectric-transducer-gated thin-film transistor (PTGTFT) aiming to measure respiration-induced dynamic force in real time with high sensitivity and robustness. It differs from passive piezoelectric sensors in that the piezoelectric transducer signal is rectified and amplified by the PTGTFT. Thus, a detailed and easy-to-analyze respiration rhythm waveform can be collected with a sufficient time resolution. The respiration rate, three phases of respiration cycle, as well as phase patterns can be further extracted for prognosis and caution of potential apnea and other respiratory abnormalities, making the PTGTFT a great promise for application in long-term real-time respiration monitoring.

Personalized Prediction of Asthma Severity and Asthma Attack for a Personalized Treatment Regimen

Control of asthma is critical for disease management and quality of life. Asthma treatment depends on the patient demographic information (e.g., age), and disease severity, which is determined by: (1) how symptoms affect a patient's daily life, (2) measured lung function, and (3) estimated risk of having an asthma attack. In this paper, we will present the Tensorflow Text Classification (TC) method to classify a patient's asthma severity level. We will also propose a Qlearning method to train an agent through trials and errors to improve the prediction accuracy and create a personalized treatment regimen for asthma patients.

Software and Hardware Requirements and Trade-Offs in Operating Systems for Wearables: A Tool to Improve Devices' Performance

Wearable device requirements currently vary from soft to hard real-time constraints. Frequently, hardware improvements are a way to speed-up the global performance of a solution. However, changing some parts or the whole hardware may increase device complexity, raising the costs and leading to development delays of products or research prototypes. This paper focuses on software improvements, presenting a tool designed to create different versions of operating systems (OSs) fitting the specifications of wearable devices projects. Authors have developed a software tool allowing the end-user to craft a new OS in just a few steps. In order to validate the generated OS, an original wearable prototype for mining environments is outlined. Resulting data presented here allows for measuring the actual impact an OS has in different variables of a solution. Finally, the analysis also allows for evaluating the performance impact associated with each hardware part. Results suggest the viability of using the proposed solution when searching for performance improvements on wearables.

Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses

Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices. This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams. We first cluster the data by physical activity using the accelerometer data, and then fit a group lasso model to each activity cluster. We find the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. We show that using activity state-based contextual information increases accuracy while decreasing power usage. We also show that the reduced feature set can be used in other regression models increasing accuracy and decreasing energy burden. We demonstrate the potential reduction in power usage using a custom-designed multi-modal wearable system prototype.

An Internet-of-Things (IoT) Network System for Connected Safety and Health Monitoring Applications

This paper presents a hybrid wearable sensor network system towards the Internet of Things (IoT) connected safety and health monitoring applications. The system is aimed at improving safety in the outdoor workplace. The proposed system consists of a wearable body area network (WBAN) to collect user data and a low-power wide-area network (LPWAN) to connect the WBAN with the Internet. The wearable sensors in the WBAN are exerted to measure the environmental conditions around the subject using a Safe Node and monitor the vital signs of the subject using a Health Node. A standalone local server (gateway), which can process the raw sensor signals, display the environmental and physiological data, and trigger an alert if any emergency circumstance is detected, is designed within the proposed network. To connect the gateway with the Internet, an IoT cloud server is implemented to provide more functionalities, such as web monitoring and mobile applications.

Wearables and the medical revolution

Wearable sensors are already impacting healthcare and medicine by enabling health monitoring outside of the clinic and prediction of health events. This paper reviews current and prospective wearable technologies and their progress toward clinical application. We describe technologies underlying common, commercially available wearable sensors and early-stage devices and outline research, when available, to support the use of these devices in healthcare. We cover applications in the following health areas: metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. Finally, we discuss challenges associated with the adoption of wearable sensors in the current healthcare ecosystem and discuss areas for future research and development.

Asthma Management in the Era of Smart-Medicine: Devices, Gadgets, Apps and Telemedicine

Asthma is the most common chronic pediatric condition. Effective asthma management requires a proactive and inclusive approach that controls the patient's symptoms and prevents recurrence of exacerbations. Clinicians should encourage patients to become involved in their management since self-management approaches have proven to be an effective means for chronic illness treatment. Novel forms of self-monitoring and management are technological interventions. In the last decade, novel technology has been developed and used to improve asthma control since it is a powerful agent that addresses a variety of challenges in chronic disease management such as education, communication and adherence. A myriad of technology-based strategies are available although many of these are not evidence based and further studies are needed to evaluate their efficacy in specific asthma-control endpoints. Herein, authors present a review of current and future technology-based options for asthma management and a comparison between them.

Prospects and Challenges of Volatile Organic Compound Sensors in Human Healthcare

The chemical signatures of volatile organic compounds (VOCs) in humans can be utilized for point-of-care (POC) diagnosis. Apart from toxic exposure studies, VOCs generated in humans can provide insights into one's healthy and diseased metabolic states, acting as a biomarker for identifying numerous diseases noninvasively. VOC sensors and the technology of e-nose have received significant attention for continuous and selective monitoring of various physiological and pathophysiological conditions of an individual. Noninvasive detection of VOCs is achieved from biomatrices of breath, sweat and saliva. Among these, detection from sweat and saliva can be continuous in real-time. The sensing approaches include optical, chemiresistive and electrochemical techniques. This article provides an overview of such techniques. These, however, have limitations of reliability, precision, selectivity, and stability in continuous monitoring. Such limitations are due to lack of sensor stability and complexity of samples in a multivariate environment, which can lead to false readings. To overcome selectivity barriers, sensor arrays enabling multimodal sensing, have been used with pattern recognition techniques. Stability and precision issues have been addressed through advancements in nanotechnology. The use of various forms of nanomaterial not only enhance sensing performance, but also plays a major role in detection on a miniaturized scale. The rapid growth in medical Internet of Things (IoT) and artificial intelligence paves a pathway for improvements in human theranostics.