Wearable Sensors for Remote Health Monitoring: Potential Applications for Early Diagnosis of Covid-19

Emerging Multifunctional Wearable Sensors: Integrating Multimodal Sweat Analysis and Advanced Material Technologies for Next-Generation Health Monitoring

Sweat, a noninvasive and readily accessible biofluid, offers significant potential in health monitoring through its diverse biomarker profile, including electrolytes, metabolites, and hormones, which reflect physiological states in real time. Multimodal wearable sensors, integrating chemical, physical, and thermal sensing capabilities, have emerged as transformative tools for capturing these biomarkers alongside additional physiological signals. By combining advanced materials such as hydrogels, MXenes, and graphene with innovative structural designs, these sensors enable simultaneous monitoring of biomarkers (e.g., glucose, sodium, and cortisol) alongside parameters like movement and temperature. This Review systematically explores the classification and design of multimodal sensors, emphasizing their ability to address health monitoring challenges across applications including metabolic health management, stress detection, and hydration assessment. Key innovations in functional materials, such as conductive hydrogels and biomimetic structures, are discussed alongside challenges in signal integration, data processing, and power management. Additionally, advancements in self-powered systems and energy harvesting technologies have been highlighted as critical enablers for continuous, real-time monitoring. The Review concludes with a perspective on future directions, emphasizing the need for scalable manufacturing techniques, artificial intelligence integration, and standardized frameworks to enhance sensor functionality and adoption. Multimodal wearable sensors, by seamlessly integrating health data into daily life, hold the promise of transforming personalized healthcare, enabling proactive management of health and wellness through noninvasive, comprehensive, and real-time monitoring.

Photonic Nanomaterials for Wearable Health Solutions

This review underscores the transformative potential of photonic nanomaterials in wearable health technologies, driven by increasing demands for personalized health monitoring. Their unique optical and physical properties enable rapid, precise, and sensitive real-time monitoring, outperforming conventional electrical-based sensors. Integrated into ultra-thin, flexible, and stretchable formats, these materials enhance compatibility with the human body, enabling prolonged wear, improved efficiency, and reduced power consumption. A comprehensive exploration is provided of the integration of photonic nanomaterials into wearable devices, addressing material selection, light-matter interaction principles, and device assembly strategies. The review highlights critical elements such as device form factors, sensing modalities, and power and data communication, with representative examples in skin patches and contact lenses. These devices enable precise monitoring and management of biomarkers of diseases or biological responses. Furthermore, advancements in materials and integration approaches have paved the way for continuum of care systems combining multifunctional sensors with therapeutic drug delivery mechanisms. To overcome existing barriers, this review outlines strategies of material design, device engineering, system integration, and machine learning to inspire innovation and accelerate the adoption of photonic nanomaterials for next-generation of wearable health, showcasing their versatility and transformative potential for digital health applications.

Evaluating remote healthcare practices: Experiences and recommendations of healthcare professionals on smart applications

Objective

With the digitalization of objects and spaces, healthcare services are being reshaped globally, creating many potential applications. This study aimed to determine the application potential of remote healthcare services (RHS) in a hospital by considering the experiences, interests, and suggestions of health professionals, and examples of useful applications that can be used, developed, or invented for healthcare systems.

Methods

A semi-structured, face-to-face interview survey was conducted with 176 healthcare professionals working at Bozok University.

Results

Branches with the highest practice experience were internal medicine, cardiac, pediatric, infection, and orthopedics. Experienced participants rated the usability of "Consultation," "Support," and "Monitoring" applications higher than other apps, and indicated that they would prefer to use them for themselves (η²≤0.12). Requirements adequacy was lower for older adults, internal/surgical branches, and physicians than for other groups (η² ≥ 0.05). Application categories showed a significant relationship (0.4 ≤ r ≤ 0.8, p < 0.05). Several variables significantly explained the models (p < 0.001): application-usability (64%), user-demand (64%), and requirements-adequacy (25%). Professionals' demand (r = 0.83) was more strongly correlated to usability than patient demand (r = 0.63). Health professionals (N = 105) from 17 branches provided 57 available, 51 developable, and 19 innovative application recommendations. These were coded according to application type, critical features, presence, integration status, and usefulness.

Conclusion

RHS' application potential in a hospital was revealed considering demographic factors and application categories based on health professionals' experiences, practical interests, and suggestions, with a strong, comprehensive, and up-to-date methodology. The findings have the potential for international application and can contribute to implementing useful and developing original applications.

Ultrasensitive, Fast-Response, and Stretchable Temperature Microsensor Based on a Stable Encapsulated Organohydrogel Film for Wearable Applications

Ionic conductive hydrogel-based temperature sensors have emerged as promising candidates due to their good stretchability and biocompatibility. However, the unsatisfactory sensitivity, sluggish response/recovery speed, and poor environmental stability limit their applications for accurate long-term health monitoring and robot perception, especially in extreme environments. To address these concerns, here, the stretchable temperature sensors based on a double-side elastomer-encapsulated thin-film organohydrogel (DETO) architecture are proposed with impressive performance. It is found that the water-polyol binary solvent, organohydrogel film, and sandwiched device structure play important roles in the temperature sensing performance. By modifying the composition of binary solvent and thicknesses of organohydrogel and elastomer films, the DETO microsensors realize a thickness of only 380 μm, unprecedented temperature sensitivity (37.96%/°C), fast response time (6.01 s) and recovery time (10.53 s), wide detection range (25-95.7 °C), and good stretchability (40% strain), which are superior to those of conventional hydrogel-based sensors. Furthermore, the device displays good environmental stability with negligible dehydration and prolonged operation duration. With these attributes, the wearable sensor is exploited for the real-time monitoring of various physiological signals such as human skin temperature and respiration patterns as well as temperature perception for robots.

Fabrication of high-resolution, flexible, laser-induced graphene sensors via stencil masking

The advent of wearable sensing platforms capable of continuously monitoring physiological parameters indicative of health status have resulted in a paradigm shift for clinical medicine. The accessibility and adaptability of such portable, unobtrusive devices enables proactive, personalized care based on real-time physiological insights. While wearable sensing platforms exhibit powerful capabilities for continuously monitoring physiological parameters, device fabrication often requires specialized facilities and technical expertise, restricting deployment opportunities and innovation potential. The recent emergence of rapid prototyping approaches to sensor fabrication, such as laser-induced graphene (LIG), provides a pathway for circumventing these barriers through low-cost, scalable fabrication. However, inherent limitations in laser processing restrict the spatial resolution of LIG-based flexible electronic devices to the minimum laser spot size. For a CO2 laser-a commonly reported laser for device production-this corresponds to a feature size of ∼120 μm. Here, we demonstrate a facile, low-cost stencil-masking technique to reduce the minimum resolvable feature size of a LIG-based device from 120 ± 20 μm to 45 ± 3 μm when fabricated by CO2 laser. Characterization of device performance reveals this stencil-masked LIG (s-LIG) method yields a concomitant improvement in electrical properties, which we hypothesize is the result of changes in macrostructure of the patterned LIG. We showcase the performance of this fabrication method via production of common sensors including temperature and multi-electrode electrochemical sensors. We fabricate fine-line microarray electrodes not typically achievable via native CO2 laser processing, demonstrating the potential of the expanded design capabilities. Comparing microarray sensors made with and without the stencil to traditional macro LIG electrodes reveals the s-LIG sensors have significantly reduced capacitance for similar electroactive surface areas. Beyond improving sensor performance, the increased resolution enabled by this metal stencil technique expands capabilities for scalable fabrication of high-performance wearable sensors in low-resource settings without reliance on traditional fabrication pathways.

Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine

Personalized pain medicine aims to tailor pain treatment strategies for the specific needs and characteristics of an individual patient, holding the potential for improving treatment outcomes, reducing side effects, and enhancing patient satisfaction. Despite existing pain markers and treatments, challenges remain in understanding, detecting, and treating complex pain conditions. Here, we review recent engineering efforts in developing various sensors and devices for addressing challenges in the personalized treatment of pain. We summarize the basics of pain pathology and introduce various sensors and devices for pain monitoring, assessment, and relief. We also discuss advancements taking advantage of rapidly developing medical artificial intelligence (AI), such as AI-based analgesia devices, wearable sensors, and healthcare systems. We believe that these innovative technologies may lead to more precise and responsive personalized medicine, greatly improved patient quality of life, increased efficiency of medical systems, and reducing the incidence of addiction and substance use disorders.

Eye of the future: Unlocking the potential utilization of hydrogels in intraocular lenses

Hydrogels are distinguished by their exceptional ability to absorb and retain large volumes of water within their complex three-dimensional polymer networks, which is advantageous for the development of intraocular lenses (IOLs). Their innate hydrophilicity offers an optimal substrate for the fabrication of IOLs that simulate the natural lens' accommodation, thereby reducing irritation and facilitating healing after surgery. The swelling and water retention characteristics of hydrogels contribute to their notable biocompatibility and versatile mechanical properties. However, the clinical application of hydrogels faces challenges, including managing potential adverse postimplantation effects. Rigorous research is essential to ascertain the safety and effectiveness of hydrogels. This review systematically examines the prospects and constraints of hydrogels as innovative materials for IOLs. Our comprehensive analysis examines their inherent properties, various classification strategies, cross-linking processes, and sensitivity to external stimuli. Additionally, we thoroughly evaluate their interactions with ocular tissues, underscoring the potential for hydrogels to be refined into seamless and biologically integrated visual aids. We also discuss the anticipated technological progress and clinical uses of hydrogels in IOL manufacturing. With ongoing technological advancements, the promise of hydrogels is poised to evolve from concept to clinical reality, marking a significant leap forward in ophthalmology characterized by improved patient comfort, enhanced functionality, and reliable safety.

Innovative Wearable Sweat Sensor Array for Real-Time Volatile Organic Compound Detection in Noninvasive Diabetes Monitoring

Wearable sweat sensors are reshaping healthcare monitoring, providing real-time data on hydration and electrolyte levels with user-friendly, noninvasive devices. This paper introduces a highly portable two-channel microfluidic device for simultaneous sweat sampling and the real-time detection of volatile organic compound (VOC) biomarkers. This innovative wearable microfluidic system is tailored for monitoring diabetes through the continuous and noninvasive tracking of acetone and ammonia VOCs, and it seamlessly integrates with smartphones for easy data management. The core of this system lies in the utilization of carbon polymer dots (CPDs) and carbon dots (CDs) derived from monomers such as catechol, resorcinol, o-phenylenediamine, urea, and citric acid. These dots are seamlessly integrated into hydrogels made from gelatin and poly(vinyl alcohol), resulting in an advanced solid-state fluorometric sensor coating on a cellulose paper substrate. These sensors exhibit exceptional performance, offering linear detection ranges of 0.05-0.15 ppm for acetone and 0.25-0.37 ppm for ammonia, with notably low detection limits of 0.01 and 0.08 ppm, respectively. Rigorous optimization of operational parameters, encompassing the temperature, sample volume, and assay time, has been undertaken to maximize device performance. Furthermore, these sensors demonstrate impressive selectivity, effectively discerning between biologically similar substances and other potential compounds commonly present in sweat. As this field matures, the prospect of cost-effective, continuous, personalized health monitoring through wearable VOC sensors holds significant potential for overcoming barriers to comprehensive medical care in underserved regions. This highlights the transformative capacity of wearable VOC sweat sensing in ensuring equitable access to advanced healthcare diagnostics, particularly in remote or geographically isolated areas.

Smart Contact Lens for Colorimetric Visualization of Glucose Levels in the Body Fluid

Frequent blood glucose monitoring is a crucial routine for diabetic patients. Traditional invasive methods can cause discomfort and pain and even pose a risk of infection. As a result, researchers have been exploring noninvasive techniques. However, a limited number of products have been developed for the market due to their high cost. In this study, we developed a low-cost, highly accessible, and noninvasive contact lens-based glucose monitoring system. We functionalized the surface of the contact lens with boronic acid, which has a strong but reversible binding affinity to glucose. To achieve facile conjugation of boronic acid, we utilized a functional coating layer called poly(tannic acid). The functionalized contact lens binds to glucose in body fluids (e.g., tear) and releases it when soaked in an enzymatic cocktail, allowing for the glucose level to be quantified through a colorimetric assay. Importantly, the transparency and oxygen permeability of the contact lens, which are crucial for practical use, were maintained after functionalization, and the lenses showed high biocompatibility. Based on the analysis of colorimetric data generated by the smartphone application and ultraviolet-visible (UV-vis) spectra, we believe that this contact lens has a high potential to be used as a smart diagnostic tool for monitoring and managing blood glucose levels.

Age-appropriate design of domestic intelligent medical products: An example of smart blood glucose detector for the elderly with AHP-QFD Joint KE

Background

In response to the rise of intelligent products and the increasing prevalence of urban "empty nesters," we have developed a product specifically tailored for elderly diabetic patients. This product fulfils functional requirements and addresses stylistic preferences, contributing to the age-friendly evolution of home intelligent medical devices, particularly in intelligent blood glucose monitoring.

Methods

Our approach commenced with a comprehensive user experience analysis to ascertain the needs of elderly users regarding home blood glucose meters. This involved constructing a hierarchy of user demands, followed by analysing and prioritising these needs. Utilizing Quality Function Deployment (QFD), we aligned user requirements with design specifications, identifying specific product functionalities and service design elements. Further, we employed Kansei Engineering (KE) to select sample designs that resonate with the concept of sensual imagery, leading to the derivation of specific modelling intentions. Combining these design elements, we proposed product design strategies and conducted practical case studies. The effectiveness of these designs was then assessed through fuzzy evaluation methods, allowing for user feedback.

Results

Employing the Analytic Hierarchy Process for goal analysis, along with Quality Function Deployment theory and Kansei Engineering, we developed a home intelligent blood glucose meter catered to the elderly. This device not only meets its users' physiological and psychological needs but also provides an operationally convenient, health-conscious, and aesthetically pleasing experience.

Conclusions

This methodology enhances the age-appropriate design of home-based smart glucose monitors for the elderly, offering innovative insights and optimization strategies for designing elderly-centric smart medical products.

Cold-resistant, highly stretchable ionic conductive hydrogels for intelligent motion recognition in winter sports

Conductive hydrogels have attracted much attention for their wide application in the field of flexible wearable sensors due to their outstanding flexibility, conductivity and sensing properties. However, the weak mechanical properties, lack of frost resistance and susceptibility to microbial contamination of traditional conductive hydrogels greatly limit their practical application. In this work, multifunctional polyvinyl alcohol (PVA)/carboxymethyl cellulose (CMC)/poly(acrylamide-co-1-vinyl-3-butylimidazolium bromide) (P(AAm-co-VBIMBr)) (PCPAV) ionic conductive hydrogels with high strength and good conductive, transparent, anti-freezing and antibacterial properties were constructed by introducing a network of chemically crosslinked AAm and VBIMBr copolymers into the base material of PVA and CMC by in situ free radical polymerization. Owing to the multiple interactions between the polymers, including covalent crosslinking, multiple hydrogen bonding interactions, and electrostatic interactions, the obtained ionic conductive hydrogels exhibit a high tensile strength (360.6 kPa), a large elongation at break (810.6%), good toughness, and fatigue resistance properties. The introduction of VBIMBr endows the PCPAV hydrogels with excellent transparency (∼92%), a high ionic conductivity (15.2 mS cm-1), antimicrobial activity and good flexibility and conductivity at sub-zero temperatures. Notably, the PCPAV hydrogels exhibit a wide strain range (0-800%), high strain sensitivity (GF = 3.75), fast response, long-term stability, and fantastic durability, which enable them to detect both large joint movements and minute muscle movements. Based on these advantages, it is believed that the PCPAV-based hydrogel sensors would have potential applications in health monitoring, human motion detection, soft robotics, ionic skins, human-machine interfaces, and other flexible electronic devices.

Nanoparticle-Assisted Liquid Crystal Droplet Sensors Enable Analysis of Low-Concentration Species in Aqueous Medium

We introduce nanoparticle-assisted liquid crystal (LC) droplet-based sensors that allow determination of low-level concentrations of aqueous soluble species. The silica nanoparticles functionalized with mixed monolayers composed of two distinct groups, hydrophobic alkane tail- and charged group-terminated silanes, facilitated ternary physical interactions between the model analytes (methylene blue (MB) or methyl orange (MO)) and the nematic mesogens 5CB (4-cyano-4'-pentylbiphenyl), and the interfacial species of the nanoparticle. The response of the LC droplets was measured upon nanoparticle adsorption as a function of analyte concentration, which was characterized by the optical determination of the configuration distributions of the LC droplets. We highlight the importance of the charging and the composition of the nanoparticle interfaces for analytical purposes that allow accurate determination of the concentration of the analytes on the order of 0.01 ppb. Such a low concentration corresponds to a low interfacial coverage of nanoparticles, indicating the promisingly high sensitivity of the sensor platform to target analytes. Distinct from the past examples of the LC-based sensors, the nanoparticle-assisted LC sensors allow detection of the species that do not directly cause an ordering transition at the LC-water interfaces, which allow a broader range of analytical targets. The sensor platform that we report herein can be easily tunable for a range of target molecules and will find use in the determination of a wide range of micropollutants in aqueous environments.

A theoretical framework based on the needs of smart aged care for Chinese community-dwelling older adults: A grounded theory study

PURPOSE

With technological progress, the integration of aged care with technology is a new challenge. This study developed a theoretical model of smart aged care in the community to meet the diverse needs of community-dwelling older adults.

METHODS

This qualitative study recruited 22 participants from three communities in Chongqing, China. Through semi-structured interviews and grounded theory, this study analyzed the needs of community-dwelling older adults for smart aged care and identified strategies.

RESULTS

Nine categories were identified, including five need categories, three important factors, and one outcome objective. Furthermore, four health provider topics were proposed.

CONCLUSIONS

Although the application of information technology has enhanced convenience and possibilities, its popularity and satisfaction are low. Information technology can be successfully introduced into the lives of community-dwelling older adults only by truly understanding their needs.

IMPLICATIONS OF NURSING PRACTICE

Smart aged care in the community has positive effects on nursing outcomes for older adults. This study's findings can help caregivers understand the various dimensions of the needs of community-dwelling older adults and relevant influencing factors under smart aged care to increase its popularity and satisfaction. Furthermore, this can promote the integration of intelligent technology and manual services in nursing practice.

Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics

This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to analyzing acoustic signals for quantifying coughing episodes. The research integrates diverse data capture technologies to analyze them collectively, considering their temporal evolution and physical attributes, aiming to extract statistically significant relationships among various variables for valuable insights. The study delineates two distinct aspects: cough detection employing a microphone and a neural network, and thermal sensors employing a calibration curve to refine their output values, reducing errors within a specified temperature range. Regarding control units, the initial implementation with an ESP32 transitioned to a Raspberry Pi model 3B+ due to neural network integration issues. A comprehensive testing is conducted for both fever and cough detection, ensuring robustness and accuracy in each scenario. The subsequent work involves practical experimentation and interoperability tests, validating the proof of concept for each system component. Furthermore, this work assesses the technical specifications of the prototype developed in the preceding tasks. Real-time testing is performed for each symptom to evaluate the system's effectiveness. This research contributes to the advancement of non-invasive sensor technologies, with implications for healthcare applications such as remote health monitoring and early disease detection.

Wearable Nano-Based Gas Sensors for Environmental Monitoring and Encountered Challenges in Optimization

With a rising emphasis on public safety and quality of life, there is an urgent need to ensure optimal air quality, both indoors and outdoors. Detecting toxic gaseous compounds plays a pivotal role in shaping our sustainable future. This review aims to elucidate the advancements in smart wearable (nano)sensors for monitoring harmful gaseous pollutants, such as ammonia (NH3), nitric oxide (NO), nitrous oxide (N2O), nitrogen dioxide (NO2), carbon monoxide (CO), carbon dioxide (CO2), hydrogen sulfide (H2S), sulfur dioxide (SO2), ozone (O3), hydrocarbons (CxHy), and hydrogen fluoride (HF). Differentiating this review from its predecessors, we shed light on the challenges faced in enhancing sensor performance and offer a deep dive into the evolution of sensing materials, wearable substrates, electrodes, and types of sensors. Noteworthy materials for robust detection systems encompass 2D nanostructures, carbon nanomaterials, conducting polymers, nanohybrids, and metal oxide semiconductors. A dedicated section dissects the significance of circuit integration, miniaturization, real-time sensing, repeatability, reusability, power efficiency, gas-sensitive material deposition, selectivity, sensitivity, stability, and response/recovery time, pinpointing gaps in the current knowledge and offering avenues for further research. To conclude, we provide insights and suggestions for the prospective trajectory of smart wearable nanosensors in addressing the extant challenges.

A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care

BACKGROUND

e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICTs) to support health and health-related fields. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments. As a result of the COVID-19 pandemic, ICT has improved the quality of healthcare, the exchange of information, training of healthcare professionals and patients, and facilitated the relationship between patients and healthcare providers. This study systematically reviews the literature on ICT-based automatic and remote monitoring methods, as well as different ICT techniques used in the care of COVID-19-infected patients.

OBJECTIVE

The purpose of this systematic literature review is to identify the e-Health methods, associated ICTs, method implementation strategies, information collection techniques, advantages, and disadvantages of remote and automatic patient monitoring and care in COVID-19 pandemic.

METHODS

The search included primary studies that were published between January 2020 and June 2022 in scientific and electronic databases, such as EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MEDLINE, Google Scholar, JMIR, Web of Science, Science Direct, and PubMed. In this review, the findings from the included publications are presented and elaborated according to the identified research questions. Evidence-based systematic reviews and meta-analyses were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Additionally, we improved the review process using the Rayyan tool and the Scale for the Assessment of Narrative Review Articles (SANRA). Among the eligibility criteria were methodological rigor, conceptual clarity, and useful implementation of ICTs in e-Health for remote and automatic monitoring of COVID-19 patients.

RESULTS

Our initial search identified 664 potential studies; 102 were assessed for eligibility in the pre-final stage and 65 articles were used in the final review with the inclusion and exclusion criteria. The review identified the following eHealth methods-Telemedicine, Mobile Health (mHealth), and Telehealth. The associated ICTs are Wearable Body Sensors, Artificial Intelligence (AI) algorithms, Internet-of-Things, or Internet-of-Medical-Things (IoT or IoMT), Biometric Monitoring Technologies (BioMeTs), and Bluetooth-enabled (BLE) home health monitoring devices. Spatial or positional data, personal and individual health, and wellness data, including vital signs, symptoms, biomedical images and signals, and lifestyle data are examples of information that is managed by ICTs. Different AI and IoT methods have opened new possibilities for automatic and remote patient monitoring with associated advantages and weaknesses. Our findings were represented in a structured manner using a semantic knowledge graph (e.g., ontology model).

CONCLUSIONS

Various e-Health methods, related remote monitoring technologies, different approaches, information categories, the adoption of ICT tools for an automatic remote patient monitoring (RPM), advantages and limitations of RMTs in the COVID-19 case are discussed in this review. The use of e-Health during the COVID-19 pandemic illustrates the constraints and possibilities of using ICTs. ICTs are not merely an external tool to achieve definite remote and automatic health monitoring goals; instead, they are embedded in contexts. Therefore, the importance of the mutual design process between ICT and society during the global health crisis has been observed from a social informatics perspective. A global health crisis can be observed as an information crisis (e.g., insufficient information, unreliable information, and inaccessible information); however, this review shows the influence of ICTs on COVID-19 patients' health monitoring and related information collection techniques.

Biomaterial-Based Biomimetic Visual Sensors: Inkjet Patterning of Bacteriorhodopsin

Biomimetic visual sensors utilizing bacteriorhodopsin (bR) were fabricated by using an inkjet method. The inkjet printer facilitated the jetting of the bR suspension, allowing for the deposition of bR films. The resulting inkjet-printed bR film exhibited time-differential photocurrent response characteristics similar to those of a dip-coated bR film. By adjusting the number of printed bR film layers, the intensity of the photocurrent could be easily controlled. Moreover, the inkjet printing technique enabled unconstrained patterning, facilitating the design of various visual information processing functions, such as visual filters. In this study, we successfully fabricated two visual filters, namely, a two-dimensional Difference of Gaussian (DOG) filter and a Gabor filter. The printed DOG filter demonstrated edge detection capabilities corresponding to contour recognition in visual receptive fields. On the other hand, the printed Gabor filter proved effective in detecting objects of specific sizes as well as their motion and orientation. The integration of bR and the inkjet method holds significant potential for the widespread implementation of highly functional biomaterial-based visual sensors. These sensors have the capability to provide real-time visual information while operating in an energy-efficient manner.

Enhanced Piezoelectricity of PVDF-TrFE Nanofibers by Intercalating with Electrosprayed BaTiO3

Over the past few decades, flexible piezoelectric devices have gained increasing interest due to their wide applications as wearable sensors and energy harvesters. Poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE), as one of piezoelectric polymers, has caught considerable attention because of its high flexibility, high thermal stability, and biocompatibility. However, its relatively lower piezoelectricity limits its broader applications. Herein, we present a new approach to improving the piezoelectricity of PVDF-TrFE nanofibers by integrating barium titanate (BTO) nanoparticles. Instead of being directly dispersed into PVDF-TrFE nanofibers, the BTO nanoparticles were electrosprayed between the nanofiber layers to create a sandwich structure. The results showed that the sample with BTO sandwiched between PVDF-TrFE nanofibers showed a much higher piezoelectric output compared to the sample with BTO uniformly dispersed in the nanofibers, with a maximum of ∼ 457% enhancement. Simulation results suggested that the enhanced piezoelectricity is due to the larger strain induced in the BTO nanoparticles in the sandwich structure. Additionally, BTO might be better poled during electrospraying with higher field strength, which is also believed to contribute to enhanced piezoelectricity. The potential of the piezoelectric nanofiber mats as a sensor for measuring biting force and as a sensor array for pressure mapping was demonstrated.

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

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

Self-Repairing and Energy-Harvesting Triboelectric Sensor for Tracking Limb Motion and Identifying Breathing Patterns

The increasing prevalence of health problems stemming from sedentary lifestyles and evolving workplace cultures has placed a substantial burden on healthcare systems. Consequently, remote health wearable monitoring systems have emerged as essential tools to track individuals' health and well-being. Self-powered triboelectric nanogenerators (TENGs) have exhibited significant potential for use as emerging detection devices capable of recognizing body movements and monitoring breathing patterns. However, several challenges remain to be addressed in order to fulfill the requirements for self-healing ability, air permeability, energy harvesting, and suitable sensing materials. These materials must possess high flexibility, be lightweight, and have excellent triboelectric charging effects in both electropositive and electronegative layers. In this work, we investigated self-healable electrospun polybutadiene-based urethane (PBU) as a positive triboelectric layer and titanium carbide (Ti3C2Tx) MXene as a negative triboelectric layer for the fabrication of an energy-harvesting TENG device. PBU consists of maleimide and furfuryl components as well as hydrogen bonds that trigger the Diels-Alder reaction, contributing to its self-healing properties. Moreover, this urethane incorporates a multitude of carbonyl and amine groups, which create dipole moments in both the stiff and the flexible segments of the polymer. This characteristic positively influences the triboelectric qualities of PBU by facilitating electron transfer between contacting materials, ultimately resulting in high output performance. We employed this device for sensing applications to monitor human motion and breathing pattern recognition. The soft and fibrous-structured TENG generates a high and stable open-circuit voltage of up to 30 V and a short-circuit current of 4 μA at an operation frequency of 4.0 Hz, demonstrating remarkable cyclic stability. A significant feature of our TENG is its self-healing ability, which allows for the restoration of its functionality and performance after sustaining damage. This characteristic has been achieved through the utilization of the self-healable PBU fibers, which can be repaired via a simple vapor solvent method. This innovative approach enables the TENG device to maintain optimal performance and continue functioning effectively even after multiple uses. After integration with a rectifier, the TENG can charge various capacitors and power 120 LEDs. Moreover, we employed the TENG as a self-powered active motion sensor, attaching it to the human body to monitor various body movements for energy-harvesting and sensing purposes. Additionally, the device demonstrates the capability to recognize breathing patterns in real time, offering valuable insights into an individual's respiratory health.

Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time

With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care and reducing healthcare costs. The Internet of Things (IoT) has recently drawn much interest as a potential remote health monitoring remedy. IoT-based systems can gather and analyze a wide range of physiological data, including blood oxygen levels, heart rates, body temperatures, and ECG signals, and then provide real-time feedback to medical professionals so they may take appropriate action. This paper proposes an IoT-based system for remote monitoring and early detection of health problems in home clinical settings. The system comprises three sensor types: MAX30100 for measuring blood oxygen level and heart rate; AD8232 ECG sensor module for ECG signal data; and MLX90614 non-contact infrared sensor for body temperature. The collected data is transmitted to a server using the MQTT protocol. A pre-trained deep learning model based on a convolutional neural network with an attention layer is used on the server to classify potential diseases. The system can detect five different categories of heartbeats: Normal Beat, Supraventricular premature beat, Premature ventricular contraction, Fusion of ventricular, and Unclassifiable beat from ECG sensor data and fever or non-fever from body temperature. Furthermore, the system provides a report on the patient's heart rate and oxygen level, indicating whether they are within normal ranges or not. The system automatically connects the user to the nearest doctor for further diagnosis if any critical abnormalities are detected.

Empowering Patients and Transforming Healthcare in the Post-COVID-19 Era: The Role of Digital and Wearable Technologies

The COVID-19 pandemic has dramatically impacted the global healthcare system, revealing critical gaps in our capacity to provide efficient and effective care to patients, particularly those with chronic diseases [...].

Electronic textiles: New age of wearable technology for healthcare and fitness solutions

Sedentary lifestyles and evolving work environments have created challenges for global health and cause huge burdens on healthcare and fitness systems. Physical immobility and functional losses due to aging are two main reasons for noncommunicable disease mortality. Smart electronic textiles (e-textiles) have attracted considerable attention because of their potential uses in health monitoring, rehabilitation, and training assessment applications. Interactive textiles integrated with electronic devices and algorithms can be used to gather, process, and digitize data on human body motion in real time for purposes such as electrotherapy, improving blood circulation, and promoting wound healing. This review summarizes research advances on e-textiles designed for wearable healthcare and fitness systems. The significance of e-textiles, key applications, and future demand expectations are addressed in this review. Various health conditions and fitness problems and possible solutions involving the use of multifunctional interactive garments are discussed. A brief discussion of essential materials and basic procedures used to fabricate wearable e-textiles are included. Finally, the current challenges, possible solutions, opportunities, and future perspectives in the area of smart textiles are discussed.

Fiber-based quantum-dot pulse oximetry for wearable health monitoring with high wavelength selectivity and photoplethysmogram sensitivity

Increasing demand for real-time healthcare monitoring is leading to advances in thin and flexible optoelectronic device-based wearable pulse oximetry. Most previous studies have used OLEDs for this purpose, but did not consider the side effects of broad full-width half-maximum (FWHM) characteristics and single substrates. In this study, we performed SpO₂ measurement using a fiber-based quantum-dot pulse oximetry (FQPO) system capable of mass production with a transferable encapsulation technique, and a narrow FWHM of about 30 nm. Based on analyses we determined that uniform angular narrow FWHM-based light sources are important for accurate SpO₂ measurements through multi-layer structures and human skin tissues. The FQPO was shown to have improved photoplethysmogram (PPG) signal sensitivity with no waveguide-mode noise signal, as is typically generated when using a single substrate (30-50%). We successfully demonstrate improved SpO₂ measurement accuracy as well as all-in-one clothing-type pulse oximetry with FQPO.

Ultrasensitive Humidity Sensors with Synergy between Superhydrophilic Porous Carbon Electrodes and Phosphorus-Doped Dielectric Electrolyte

Capacitive humidity sensors have been used for human health monitoring, but their performance may be poor in terms of sensitivity and response time, because of limitations in sensing materials and insufficient knowledge about sensing mechanisms. Herein, a new combination of humidity sensing materials to assemble thin-film based capacitive-type sensors is proposed by using PA-doped polybenzimidazole (PA-PBI) as an electrolyte and laser-carbonized PA-PBI as a carbon electrode (PA-PBI-C). Based on PA involved laser scribing, the flexible sensor can reach excellent humidity-sensing performances with an ultrahigh sensitivity (1.16 × 106 pF RH%-1, where RH represents the relative humidity), a superior linearity (R2 = 0.9982), a fast response time (0.72 s), and a low hysteresis in a wide RH range from 1% to 95%. By further studying P-O decorated porous carbon electrode with superhydrophilicity and the solid-state dielectric electrolyte featured by a high dielectric constant, a synergistic sensing mechanism consisting of a "Water reservoir" and a "Bridge" is established to support advanced health-monitoring applications such as the respiration patterns and skin condition where both sensitivity and response time are critical.

Biosensors based detection of novel biomarkers associated with COVID-19: Current progress and future promise

The pandemic situation of COVID-19 has caused global alarm in health care, devastating loss of lives, strangled economy, and paralysis of normal livelihood. The high inter-individual transmission rate created havoc in the global community. Although tremendous efforts are pitching in from across the globe to understand this disease, the clinical features seemed to have a wide range including fever, cough, and fatigue are the prominent features. Congestion, rhinorrhea, sore throat, and diarrhea are other less common features observed. The challenge of this disease lies in the difficulty in maneuvering the clinical course causing severe complications. One of the major causative factors for multi-organ failure in patients with severe COVID-19 complications is systemic vasculitis and cytokine-mediated coagulation disorders. Hence, effective markers trailing the disease severity and disease prognosis are urgently required for prompt medical treatment. In this review article, we have emphasized currently identified inflammatory, hematological, immunological, and biochemical biomarkers of COVID-19. We also discussed currently available biosensors for the detection of COVID-19-associated biomarkers & risk factors and the detection methods as well as their performances. These could be effective tools for rapid and more promising diagnoses in the current pandemic situation. Effective biomarkers and their rapid, scalable, & sensitive detection might be beneficial for the prevention of serious complications and the clinical management of the disease.

Clustered Regularly Interspaced short palindromic repeats-Based Microfluidic System in Infectious Diseases Diagnosis: Current Status, Challenges, and Perspectives

Mitigating the spread of global infectious diseases requires rapid and accurate diagnostic tools. Conventional diagnostic techniques for infectious diseases typically require sophisticated equipment and are time consuming. Emerging clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated proteins (Cas) detection systems have shown remarkable potential as next-generation diagnostic tools to achieve rapid, sensitive, specific, and field-deployable diagnoses of infectious diseases, based on state-of-the-art microfluidic platforms. Therefore, a review of recent advances in CRISPR-based microfluidic systems for infectious diseases diagnosis is urgently required. This review highlights the mechanisms of CRISPR/Cas biosensing and cutting-edge microfluidic devices including paper, digital, and integrated wearable platforms. Strategies to simplify sample pretreatment, improve diagnostic performance, and achieve integrated detection are discussed. Current challenges and future perspectives contributing to the development of more effective CRISPR-based microfluidic diagnostic systems are also proposed.

Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles

Background

Daily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks.

Objective

Use data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex.

Methods

We used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model.

Results

Although the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females.

Conclusion

Web-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection.

Blood oxygen saturation is lower in persons with pre-diabetes and screen-detected diabetes compared with non-diabetic individuals: A population-based study of the Lolland-Falster Health Study cohort

Aims

Low blood oxygen saturation is associated with increased mortality and persons with diabetes have sub-clinical hypoxemia. We aimed to confirm the presence of sub-clinical hypoxemia in pre-diabetes, screen-detected diabetes and known diabetes.

Methods

Pre-diabetes was defined as hemoglobin A1C (HbA1C) ≥ 42 mmol/mol and <48 mmol/mol; known diabetes as history or treatment of diabetes; screen-detected diabetes as no history or treatment of diabetes and HbA1C ≥ 48 mmol/mol. Blood oxygen saturation was measured with pulse oximetry. Urine albumin-to creatinine ratio (UACR) was measured on a single spot urine.

Results

The study included 829 adults (≥18 years) with diabetes (713 (86%) with known diabetes; 116 (14%) with screen-detected diabetes) and 12,747 without diabetes (11,981 (94%) healthy controls; 766 (6%) with pre-diabetes). Mean (95% CI) blood oxygen saturation was 96.3% (96.3% to 96.4%) in diabetes which was lower than in non-diabetes [97.3% (97.2-97.3%)] after adjustment for age, gender, and smoking (p < 0.001), but significance was lost after adjustment for BMI (p = 0.25). Sub-groups with pre-diabetes and screen-detected diabetes had lower blood oxygen saturations than healthy controls (p-values < 0.01). Lower blood oxygen saturation was associated with higher UACR.

Conclusions

Persons with pre-diabetes and screen-detected diabetes have sub-clinical hypoxemia, which is associated with albuminuria.

Spectroscopic methods for COVID-19 detection and early diagnosis

The coronavirus pandemic is a worldwide hazard that poses a threat to millions of individuals throughout the world. This pandemic is caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which was initially identified in Wuhan, China's Hubei provincial capital, and has since spread throughout the world. According to the World Health Organization's Weekly Epidemiological Update, there were more than 250 million documented cases of coronavirus infections globally, with five million fatalities. Early detection of coronavirus does not only reduce the spread of the virus, but it also increases the chance of curing the infection. Spectroscopic techniques have been widely used in the early detection and diagnosis of COVID-19 using Raman, Infrared, mass spectrometry and fluorescence spectroscopy. In this review, the reported spectroscopic methods for COVID-19 detection were discussed with emphasis on the practical aspects, limitations and applications.

Wearable technology for early detection of COVID-19: A systematic scoping review

Wearable technology is an emerging method for the early detection of coronavirus disease 2019 (COVID-19) infection. This scoping review explored the types, mechanisms, and accuracy of wearable technology for the early detection of COVID-19. This review was conducted according to the five-step framework of Arksey and O'Malley. Studies published between December 31, 2019 and December 15, 2021 were obtained from 10 electronic databases, namely, PubMed, Embase, Cochrane, CINAHL, PsycINFO, ProQuest, Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online. Grey literature, reference lists, and key journals were also searched. All types of articles describing wearable technology for the detection of COVID-19 infection were included. Two reviewers independently screened the articles against the eligibility criteria and extracted the data using a data charting form. A total of 40 articles were included in this review. There are 22 different types of wearable technology used to detect COVID-19 infections early in the existing literature and are categorized as smartwatches or fitness trackers (67%), medical devices (27%), or others (6%). Based on deviations in physiological characteristics, anomaly detection models that can detect COVID-19 infection early were built using artificial intelligence or statistical analysis techniques. Reported area-under-the-curve values ranged from 75% to 94.4%, and sensitivity and specificity values ranged from 36.5% to 100% and 73% to 95.3%, respectively. Further research is necessary to validate the effectiveness and clinical dependability of wearable technology before healthcare policymakers can mandate its use for remote surveillance.

Engineered Living Hydrogels

Living biological systems, ranging from single cells to whole organisms, can sense, process information, and actuate in response to changing environmental conditions. Inspired by living biological systems, engineered living cells and nonliving matrices are brought together, which gives rise to the technology of engineered living materials. By designing the functionalities of living cells and the structures of nonliving matrices, engineered living materials can be created to detect variability in the surrounding environment and to adjust their functions accordingly, thereby enabling applications in health monitoring, disease treatment, and environmental remediation. Hydrogels, a class of soft, wet, and biocompatible materials, have been widely used as matrices for engineered living cells, leading to the nascent field of engineered living hydrogels. Here, the interactions between hydrogel matrices and engineered living cells are described, focusing on how hydrogels influence cell behaviors and how cells affect hydrogel properties. The interactions between engineered living hydrogels and their environments, and how these interactions enable versatile applications, are also discussed. Finally, current challenges facing the field of engineered living hydrogels for their applications in clinical and environmental settings are highlighted.

Lab-on-a-Contact Lens: Recent Advances and Future Opportunities in Diagnostics and Therapeutics

The eye is one of the most complex organs in the human body, containing rich and critical physiological information (e.g., intraocular pressure, corneal temperature, and pH) as well as a library of metabolite biomarkers (e.g., glucose, proteins, and specific ions). Smart contact lenses (SCLs) can serve as a wearable intelligent ocular prosthetic device capable of noninvasive and continuous monitoring of various essential physical/biochemical parameters and drug loading/delivery for the treatment of ocular diseases. Advances in SCL technologies and the growing public interest in personalized health are accelerating SCL research more than ever before. Here, the current status and potential of SCL development through a comprehensive review from fabrication to applications to commercialization are discussed. First, the material, fabrication, and platform designs of the SCLs for the diagnostic and therapeutic applications are discussed. Then, the latest advances in diagnostic and therapeutic SCLs for clinical translation are reviewed. Later, the established techniques for wearable power transfer and wireless data transmission applied to current SCL devices are summarized. An outlook, future opportunities, and challenges for developing next-generation SCL devices are also provided. With the rise in interest of SCL development, this comprehensive and essential review can serve as a new paradigm for the SCL devices.