End-to-end design of wearable sensors

Wearable Devices and Explainable Unsupervised Learning for COVID-19 Detection and Monitoring

Despite the declining COVID-19 cases, global healthcare systems still face significant challenges due to ongoing infections, especially among fully vaccinated individuals, including adolescents and young adults (AYA). To tackle this issue, cost-effective alternatives utilizing technologies like Artificial Intelligence (AI) and wearable devices have emerged for disease screening, diagnosis, and monitoring. However, many AI solutions in this context heavily rely on supervised learning techniques, which pose challenges such as human labeling reliability and time-consuming data annotation. In this study, we propose an innovative unsupervised framework that leverages smartwatch data to detect and monitor COVID-19 infections. We utilize longitudinal data, including heart rate (HR), heart rate variability (HRV), and physical activity measured via step count, collected through the continuous monitoring of volunteers. Our goal is to offer effective and affordable solutions for COVID-19 detection and monitoring. Our unsupervised framework employs interpretable clusters of normal and abnormal measures, facilitating disease progression detection. Additionally, we enhance result interpretation by leveraging the language model Davinci GPT-3 to gain deeper insights into the underlying data patterns and relationships. Our results demonstrate the effectiveness of unsupervised learning, achieving a Silhouette score of 0.55. Furthermore, validation using supervised learning techniques yields high accuracy (0.884 ± 0.005), precision (0.80 ± 0.112), and recall (0.817 ± 0.037). These promising findings indicate the potential of unsupervised techniques for identifying inflammatory markers, contributing to the development of efficient and reliable COVID-19 detection and monitoring methods. Our study shows the capabilities of AI and wearables, reflecting the pursuit of low-cost, accessible solutions for addressing health challenges related to inflammatory diseases, thereby opening new avenues for scalable and widely applicable health monitoring solutions.

A Highly Flexible Piezoelectric Ultrasonic Sensor for Wearable Bone Density Testing

Driven by the loss of bone calcium, the elderly are prone to osteoporosis, and regular routine checks on bone status are necessary, which mainly rely on bone testing equipment. Therefore, wearable real-time healthcare devices have become a research hotspot. Herein, we designed a high-performance flexible ultrasonic bone testing system using axial transmission technology based on quantitative ultrasound theory. First, a new rare-earth-element-doped PMN-PZT piezoelectric ceramic was synthesized using a solid-state reaction, and characterized by X-ray diffraction and SEM. Both a high piezoelectric coefficient d33 = 525 pC/N and electromechanical coupling factors of k33 = 0.77, kt = 0.58 and kp = 0.63 were achieved in 1%La/Sm-doped 0.17 PMN-0.47 PZ-0.36 PT ceramics. Combining a flexible PDMS substrate with an ultrasonic array, a flexible hardware circuit was designed which includes a pulse excitation module, ultrasound array module, amplification module, filter module, digital-to-analog conversion module and wireless transmission module, showing high power transfer efficiency and power intensity with values of 35% and 55.4 mW/cm2, respectively. Finally, the humerus, femur and fibula were examined by the flexible device attached to the skin, and the bone condition was displayed in real time on the mobile client, which indicates the potential clinical application of this device in the field of wearable healthcare.

Transparent, High Stretchable, Environmental Tolerance, and Excellent Sensitivity Hydrogel for Flexible Sensors and Capacitive Pens

The prospect of ionic conductive hydrogels in multifunctional sensors has generated widespread scientific interest. The new generation of flexible materials should be combined with superior mechanical properties, high conductivity, transparency, sensitivity, good self-restoring fatigue properties, and other multifunctional characteristics, while the current materials are difficult to meet these requirements. Herein, we prepared poly(acrylamide-acrylic acid) (P(AM-AA))/gelatin/glycerol-Al3+ (PG1G2A) ionic conducting hydrogel by one-pot polymerization under UV light. The prepared PG1G2A ionic conductive hydrogel had high tensile strength (539.18 kPa), excellent tensile property (1412.96%), good fast self-recovery and fatigue resistance, high transparency (>80%), excellent moisturizing, and antifreezing/drying properties. In addition, the ionic conductive hydrogel-based strain sensor can respond to mechanical stimulation and generate accurate, stable, and recyclable electrical signals, with excellent sensitivity (GF 5.81). In addition, the PG1G2A hydrogel could be used as flexible wearable devices for monitoring multiple strain and subtle movements of different body parts at different temperatures. Interestingly, the PG1G2A hydrogel capacitive pen embedded in the mold can be used to write and draw on the screen of a phone or tablet. This new multifunctional ionic conducting hydrogel shows broad application prospects in E-skin, motion monitoring, and human-computer interaction in extreme environments.

Microfluidic Wearable Devices for Sports Applications

This study aimed to systematically review the application and research progress of flexible microfluidic wearable devices in the field of sports. The research team thoroughly investigated the use of life signal-monitoring technology for flexible wearable devices in the domain of sports. In addition, the classification of applications, the current status, and the developmental trends of similar products and equipment were evaluated. Scholars expect the provision of valuable references and guidance for related research and the development of the sports industry. The use of microfluidic detection for collecting biomarkers can mitigate the impact of sweat on movements that are common in sports and can also address the issue of discomfort after prolonged use. Flexible wearable gadgets are normally utilized to monitor athletic performance, rehabilitation, and training. Nevertheless, the research and development of such devices is limited, mostly catering to professional athletes. Devices for those who are inexperienced in sports and disabled populations are lacking. Conclusions: Upgrading microfluidic chip technology can lead to accurate and safe sports monitoring. Moreover, the development of multi-functional and multi-site devices can provide technical support to athletes during their training and competitions while also fostering technological innovation in the field of sports science.

3D-printed epifluidic electronic skin for machine learning-powered multimodal health surveillance

The amalgamation of wearable technologies with physiochemical sensing capabilities promises to create powerful interpretive and predictive platforms for real-time health surveillance. However, the construction of such multimodal devices is difficult to be implemented wholly by traditional manufacturing techniques for at-home personalized applications. Here, we present a universal semisolid extrusion-based three-dimensional printing technology to fabricate an epifluidic elastic electronic skin (e3-skin) with high-performance multimodal physiochemical sensing capabilities. We demonstrate that the e3-skin can serve as a sustainable surveillance platform to capture the real-time physiological state of individuals during regular daily activities. We also show that by coupling the information collected from the e3-skin with machine learning, we were able to predict an individual's degree of behavior impairments (i.e., reaction time and inhibitory control) after alcohol consumption. The e3-skin paves the path for future autonomous manufacturing of customizable wearable systems that will enable widespread utility for regular health monitoring and clinical applications.

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.

A Computationally Assisted Approach for Designing Wearable Biosensors toward Non-Invasive Personalized Molecular Analysis

Wearable sweat sensors have the potential to revolutionize precision medicine as they can non-invasively collect molecular information closely associated with an individual's health status. However, the majority of clinically relevant biomarkers cannot be continuously detected in situ using existing wearable approaches. Molecularly imprinted polymers (MIPs) are a promising candidate to address this challenge but haven't yet gained widespread use due to their complex design and optimization process yielding variable selectivity. Here, QuantumDock is introduced, an automated computational framework for universal MIP development toward wearable applications. QuantumDock utilizes density functional theory to probe molecular interactions between monomers and the target/interferent molecules to optimize selectivity, a fundamentally limiting factor for MIP development toward wearable sensing. A molecular docking approach is employed to explore a wide range of known and unknown monomers, and to identify the optimal monomer/cross-linker choice for subsequent MIP fabrication. Using an essential amino acid phenylalanine as the exemplar, experimental validation of QuantumDock is performed successfully using solution-synthesized MIP nanoparticles coupled with ultraviolet-visible spectroscopy. Moreover, a QuantumDock-optimized graphene-based wearable device is designed that can perform autonomous sweat induction, sampling, and sensing. For the first time, wearable non-invasive phenylalanine monitoring is demonstrated in human subjects toward personalized healthcare applications.

Wearable sweat analysis to determine biological age

A real-time, noninvasive, and clinically applicable aging test in humans has yet to be established. Herein we propose a sweat- and wearable-based test to determine biological age. This test would empower users to monitor their aging process and take an active role in managing their lifestyle and health.

Fibrous wearable and implantable bioelectronics

Fibrous wearable and implantable devices have emerged as a promising technology, offering a range of new solutions for minimally invasive monitoring of human health. Compared to traditional biomedical devices, fibers offer a possibility for a modular design compatible with large-scale manufacturing and a plethora of advantages including mechanical compliance, breathability, and biocompatibility. The new generation of fibrous biomedical devices can revolutionize easy-to-use and accessible health monitoring systems by serving as building blocks for most common wearables such as fabrics and clothes. Despite significant progress in the fabrication, materials, and application of fibrous biomedical devices, there is still a notable absence of a comprehensive and systematic review on the subject. This review paper provides an overview of recent advancements in the development of fibrous wearable and implantable electronics. We categorized these advancements into three main areas: manufacturing processes, platforms, and applications, outlining their respective merits and limitations. The paper concludes by discussing the outlook and challenges that lie ahead for fiber bioelectronics, providing a holistic view of its current stage of development.

Artificial intelligence enabled smart digital eye wearables

PURPOSE OF REVIEW

Smart eyewear is a head-worn wearable device that is evolving as the next phase of ubiquitous wearables. Although their applications in healthcare are being explored, they have the potential to revolutionize teleophthalmology care. This review highlights their applications in ophthalmology care and discusses future scope.

RECENT FINDINGS

Smart eyewear equips advanced sensors, optical displays, and processing capabilities in a wearable form factor. Rapid technological developments and the integration of artificial intelligence are expanding their reach from consumer space to healthcare applications. This review systematically presents their applications in treating and managing eye-related conditions. This includes remote assessments, real-time monitoring, telehealth consultations, and the facilitation of personalized interventions. They also serve as low-vision assistive devices to help visually impaired, and can aid physicians with operational and surgical tasks.

SUMMARY

Wearables such as smart eyewear collects rich, continuous, objective, individual-specific data, which is difficult to obtain in a clinical setting. By leveraging sophisticated data processing and artificial intelligence based algorithms, these data can identify at-risk patients, recognize behavioral patterns, and make timely interventions. They promise cost-effective and personalized treatment for vision impairments in an effort to mitigate the global burden of eye-related conditions and aging.

Flexible and Stretchable Electrochemical Sensors for Biological Monitoring

The rise of flexible and stretchable electronics has revolutionized biosensor techniques for probing biological systems. Particularly, flexible and stretchable electrochemical sensors (FSECSs) enable the in situ quantification of numerous biochemical molecules in different biological entities owing to their exceptional sensitivity, fast response, and easy miniaturization. Over the past decade, the fabrication and application of FSECSs have significantly progressed. This review highlights key developments in electrode fabrication and FSECSs functionalization. It delves into the electrochemical sensing of various biomarkers, including metabolites, electrolytes, signaling molecules, and neurotransmitters from biological systems, encompassing the outer epidermis, tissues/organs in vitro and in vivo, and living cells. Finally, considering electrode preparation and biological applications, current challenges and future opportunities for FSECSs are discussed.

Flexible thermoelectric generator and energy management electronics powered by body heat

Uninterrupted, efficient power supplies have posed a significant hurdle to the ubiquitous adoption of wearable devices, despite their potential for revolutionizing human‒machine interactions. This challenge is further compounded by the requirement of these devices to supply dependable energy for data-intensive sensing and transmission. Traditional thermoelectric solutions fail to deliver satisfactory performance under conditions of extremely low voltages. Here, we present a novel solution of a wearable thermoelectric generator integrated with an energy management system, which is capable of powering sensors and Bluetooth by harnessing body heat. Distinct from previous works, our innovation lies in its ability to consistently operate even with a minimal temperature difference (i.e., 4 K) between the human skin and the ambient environment, ensuring reliable data transmission within a time as short as 1.6 s. Furthermore, our system can recharge utilizing body heat under ultralow voltage conditions (30 mV). Our developed system provides a novel pathway for the continuous, reliable monitoring of self-contained wearable devices without depending on batteries.

Transparent Electronics for Wearable Electronics Application

Recent advancements in wearable electronics offer seamless integration with the human body for extracting various biophysical and biochemical information for real-time health monitoring, clinical diagnostics, and augmented reality. Enormous efforts have been dedicated to imparting stretchability/flexibility and softness to electronic devices through materials science and structural modifications that enable stable and comfortable integration of these devices with the curvilinear and soft human body. However, the optical properties of these devices are still in the early stages of consideration. By incorporating transparency, visual information from interfacing biological systems can be preserved and utilized for comprehensive clinical diagnosis with image analysis techniques. Additionally, transparency provides optical imperceptibility, alleviating reluctance to wear the device on exposed skin. This review discusses the recent advancement of transparent wearable electronics in a comprehensive way that includes materials, processing, devices, and applications. Materials for transparent wearable electronics are discussed regarding their characteristics, synthesis, and engineering strategies for property enhancements. We also examine bridging techniques for stable integration with the soft human body. Building blocks for wearable electronic systems, including sensors, energy devices, actuators, and displays, are discussed with their mechanisms and performances. Lastly, we summarize the potential applications and conclude with the remaining challenges and prospects.

Multilayered Functional Triboelectric Polymers for Self-Powered Wearable Applications: A Review

Multifunctional wearable devices detect electric signals responsive to various biological stimuli and monitor present body motions or conditions, necessitating flexible materials with high sensitivity and sustainable operation. Although various dielectric polymers have been utilized in self-powered wearable applications in response to multiple external stimuli, their intrinsic limitations hinder further device performance enhancement. Because triboelectric devices comprising dielectric polymers are based on triboelectrification and electrostatic induction, multilayer-stacking structures of dielectric polymers enable significant improvements in device performance owing to enhanced interfacial polarization through dissimilar permittivity and conductivity between each layer, resulting in self-powered high-performance wearable devices. Moreover, novel triboelectric polymers with unique chemical structures or nano-additives can control interfacial polarization, allowing wearable devices to respond to multiple external stimuli. This review summarizes the recent insights into multilayered functional triboelectric polymers, including their fundamental dielectric principles and diverse applications.

A Hybrid Transistor with Transcriptionally Controlled Computation and Plasticity

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Feasibility of a kinect-based system in assessing physical function of the elderly for home-based care

BACKGROUND

With concerns about accurate diagnosis through telehealth, the Kinect sensor offers a reliable solution for movement analysis. However, there is a lack of practical research investigating the suitability of a Kinect-based system as a functional fitness assessment tool in homecare settings. Hence, the objective of this study was to evaluate the feasibility of using a Kinect-based system to assess physical function changes in the elderly.

METHODS

The study consisted of two phases. Phase one involved 35 young healthy adults, evaluating the reliability and validity of a Kinect-based fitness evaluation compared to traditional physical examination using the intraclass correlation coefficient (ICC). Phase two involved 665 elderly subjects, examining the correlation between the Kinect-based fitness evaluation and physical examination through Pearson's correlation coefficients. A Kinect sensor (Microsoft Xbox One Kinect V2) with customized software was employed to capture and compute the movement of joint centers. Both groups performed seven functional assessments simultaneously monitored by a physical therapist and the Kinect system. System usability and user satisfaction were assessed using the System Usability Scale (SUS) and Questionnaire for User Interface Satisfaction (QUIS), respectively.

RESULTS

Kinect-based system showed overall moderate to excellent within-day reliability (ICC = 0.633-1.0) and between-day reliability (ICC = 0.686-1.0). The overall agreement between the two devices was highly correlated (r ≧ 0.7) for all functional assessment tests in young healthy adults. The Kinect-based system also showed a high correlation with physical examination for the functional assessments (r = 0.858-0.988) except functional reach (r = 0.484) and walking speed(r = 0.493). The users' satisfaction with the system was excellent (SUS score = 84.4 ± 18.5; QUIS score = 6.5-6.7).

CONCLUSIONS

The reliability and validity of Kinect for assessing functional performance are generally favorable. Nonetheless, caution is advised when employing Kinect for tasks involving depth changes, such as functional reach and walking speed tests for their moderate validity. However, Kinect's fundamental motion detection capabilities demonstrate its potential for future applications in telerehabilitation in different healthcare settings.

Advances in Wearable Photoplethysmography Applications in Health Monitoring

In the last few years, interest in wearable technology for physiological signal monitoring is rapidly growing, especially during and after the COVID-19 pandemic [...].

Towards a Secure Technology-Driven Architecture for Smart Health Insurance Systems: An Empirical Study

Health insurance has become a crucial component of people's lives as the occurrence of health problems rises. Unaffordable healthcare problems for individuals with little income might be a problem. In the case of a medical emergency, health insurance assists individuals in affording the costs of healthcare services and protects them financially against the possibility of debt. Security, privacy, and fraud risks may impact the numerous benefits of health insurance. In recent years, health insurance fraud has been a contentious topic due to the substantial losses it causes for individuals, commercial enterprises, and governments. Therefore, there is a need to develop mechanisms for identifying health insurance fraud incidents. Furthermore, a large quantity of highly sensitive electronic health insurance data are generated on a daily basis, which attracts fraudulent users. Motivated by these facts, we propose a smart healthcare insurance framework for fraud detection and prevention (SHINFDP) that leverages the capabilities of cutting-edge technologies including blockchain, 5G, cloud, and machine learning (ML) to enhance the health insurance process. The proposed framework is evaluated using mathematical modeling and an industrial focus group. In addition, a case study was demonstrated to illustrate the SHINFDP's applicability in enhancing the security and effectiveness of health insurance. The findings indicate that the SHINFDP aids in the detection of healthcare fraud at early stages. Furthermore, the results of the focus group show that SHINFDP is adaptable and simple to comprehend. The case study further strengthens the findings and also describes the implications of the proposed solution in a real setting.

Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform

The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.

Sustainable Solutions for Oral Health Monitoring: Biowaste-Derived Triboelectric Nanogenerator

Oral healthcare monitoring is a vital aspect of identifying and addressing oral dental problems including tooth decay, gum pain, and oral cancer. Day by day, healthcare facilities and regular checkups are becoming more costly and time-consuming. In this context, consumers are moving toward advanced technology, such as bite sensors, to obtain regular data about their occlusal chewing patterns and strength. The triboelectric nanogenerator (TENG) can potentially eliminate the need for a battery by simply converting abundant vibrations from nature or human motion into electrical energy. In this work, biomaterials are obtained from biowastes such as cellulose from wood waste, chitosan from crab shells, and gelatin from fish scales. All wastes are biodegradable, and our work aims at sustainability and waste hierarchy. The single electrode mode-based TENG was designed and fabricated using biodegradable poly(vinyl alcohol) (PVA)-biomaterial composites, rice paper as a substrate, and edible silver leaf as an electrode. The highest electrical output was obtained for PVA/chitosan 10 wt % composite-based TENG (PC10) of about 20 V, 200 nA, and 12 nC. The biomechanical energy harvesting was measured, and powering of LED was demonstrated using a PC10 TENG device. A biocompatible bite sensor based on the TENG was used to measure the biting force of a dummy teeth model to demonstrate its potential use in dental health applications. It indicates the promising future value of disposable oral medication devices without any invasive surgery or injection.

Opportunities and challenges for sweat-based monitoring of metabolic syndrome via wearable technologies

Metabolic syndrome is a prevalent condition in adults over the age of 65 and is a risk factor for developing cardiovascular disease and type II diabetes. Thus, methods to track the condition, prevent complications and assess symptoms and risk factors are needed. Here we discuss sweat-based wearable technologies as a potential monitoring tool for patients with metabolic syndrome. We describe several key symptoms that can be evaluated that could employ sweat patches to assess inflammatory markers, glucose, sodium, and cortisol. We then discuss the challenges with material property, sensor integration, and sensor placement and provide feasible solutions to optimize them. Together with a list of recommendations, we propose a pathway toward successfully developing and implementing reliable sweat-based technologies to monitor metabolic syndrome.

Metabolic syndrome is a risk factor for developing cardiovascular disease and type II diabetes. Lyzwinski, Elgendi and colleagues discuss the potential role of sweat-based wearable technologies for monitoring metabolic syndrome along with engineering challenges towards implementation and optimization

Physically defined long-term and short-term synapses for the development of reconfigurable analog-type operators capable of performing health care tasks

Extracting valuable information from the overflowing data is a critical yet challenging task. Dealing with high volumes of biometric data, which are often unstructured, nonstatic, and ambiguous, requires extensive computer resources and data specialists. Emerging neuromorphic computing technologies that mimic the data processing properties of biological neural networks offer a promising solution for handling overflowing data. Here, the development of an electrolyte-gated organic transistor featuring a selective transition from short-term to long-term plasticity of the biological synapse is presented. The memory behaviors of the synaptic device were precisely modulated by restricting ion penetration through an organic channel via photochemical reactions of the cross-linking molecules. Furthermore, the applicability of the memory-controlled synaptic device was verified by constructing a reconfigurable synaptic logic gate for implementing a medical algorithm without further weight-update process. Last, the presented neuromorphic device demonstrated feasibility to handle biometric information with various update periods and perform health care tasks.

Flexible, stretchable, and single-molecule-sensitive SERS-active sensor for wearable biosensing applications

The development of wearable sensors for remote patient monitoring and personalized medicine has led to a revolution in biomedical technology. Plasmonic metasurfaces that enhance Raman scattering signals have recently gained attention as wearable sensors. However, finding a flexible, sensitive, and easy-to-fabricate metasurface has been a challenge for decades. In this paper, a novel wearable device, the flexible, stretchable, and single-molecule-sensetive SERS-active sensor, is proposed. This device offers an unprecedented SERS enhancement factor in the order of 1011, along with other long-desired characteristics for SERS applications such as a high scattering to absorption ratio (∼2.5) and a large hotspot volume (40 nm × 40 nm × 5 nm). To achieve flexibility, we use polydimethylsiloxane (PDMS) as the substrate, which is stable, transparent, and biologically compatible. Our numerical calculations show that the proposed sensor offers reliable SERS performance even under bending (up to 100° angles) or stretching (up to 50% stretch). The easy-to-fabricate and flexible nature of our sensor offers a promising avenue for developing highly sensitive wearable sensors for a range of applications, particularly in the field of personalized medicine and remote patient monitoring.

Wearable Sensor: An Emerging Data Collection Tool for Plant Phenotyping

The advancement of plant phenomics by using optical imaging-based phenotyping techniques has markedly improved breeding and crop management. However, there remains a challenge in increasing the spatial resolution and accuracy due to their noncontact measurement mode. Wearable sensors, an emerging data collection tool, present a promising solution to address these challenges. By using a contact measurement mode, wearable sensors enable in-situ monitoring of plant phenotypes and their surrounding environments. Although a few pioneering works have been reported in monitoring plant growth and microclimate, the utilization of wearable sensors in plant phenotyping has yet reach its full potential. This review aims to systematically examine the progress of wearable sensors in monitoring plant phenotypes and the environment from an interdisciplinary perspective, including materials science, signal communication, manufacturing technology, and plant physiology. Additionally, this review discusses the challenges and future directions of wearable sensors in the field of plant phenotyping.

Digital health for aging populations

Growing life expectancy poses important societal challenges, placing an increasing burden on ever more strained health systems. Digital technologies offer tremendous potential for shifting from traditional medical routines to remote medicine and transforming our ability to manage health and independence in aging populations. In this Perspective, we summarize the current progress toward, and challenges and future opportunities of, harnessing digital technologies for effective geriatric care. Special attention is given to the role of wearables in assisting older adults to monitor their health and maintain independence at home. Challenges to the widespread future use of digital technologies in this population will be discussed, along with a vision for how such technologies will shape the future of healthy aging.

High-Throughput Single-Molecule Sensors: How Can the Signals Be Analyzed in Real Time for Achieving Real-Time Continuous Biosensing?

Single-molecule sensors collect statistics of single-molecule interactions, and the resulting data can be used to determine concentrations of analyte molecules. The assays are generally end-point assays and are not designed for continuous biosensing. For continuous biosensing, a single-molecule sensor needs to be reversible, and the signals should be analyzed in real time in order to continuously report output signals, with a well-controlled time delay and measurement precision. Here, we describe a signal processing architecture for real-time continuous biosensing based on high-throughput single-molecule sensors. The key aspect of the architecture is the parallel computation of multiple measurement blocks that enables continuous measurements over an endless time span. Continuous biosensing is demonstrated for a single-molecule sensor with 10,000 individual particles that are tracked as a function of time. The continuous analysis includes particle identification, particle tracking, drift correction, and detection of the discrete timepoints where individual particles switch between bound and unbound states, yielding state transition statistics that relate to the analyte concentration in solution. The continuous real-time sensing and computation were studied for a reversible cortisol competitive immunosensor, showing how the precision and time delay of cortisol monitoring are controlled by the number of analyzed particles and the size of the measurement blocks. Finally, we discuss how the presented signal processing architecture can be applied to various single-molecule measurement methods, allowing these to be developed into continuous biosensors.

Microneedle-Integrated Sensors for Extraction of Skin Interstitial Fluid and Metabolic Analysis

Skin interstitial fluid (ISF) has emerged as a fungible biofluid sample for blood serum and plasma for disease diagnosis and therapy. The sampling of skin ISF is highly desirable considering its easy accessibility, no damage to blood vessels, and reduced risk of infection. Particularly, skin ISF can be sampled using microneedle (MN)-based platforms in the skin tissues, which exhibit multiple advantages including minimal invasion of the skin tissues, less pain, ease of carrying, capacity for continuous monitoring, etc. In this review, we focus on the current development of microneedle-integrated transdermal sensors for collecting ISF and detecting specific disease biomarkers. Firstly, we discussed and classified microneedles according to their structural design, including solid MNs, hollow MNs, porous MNs, and coated MNs. Subsequently, we elaborate on the construction of MN-integrated sensors for metabolic analysis with highlights on the electrochemical, fluorescent, chemical chromogenic, immunodiagnostic, and molecular diagnostic MN-integrated sensors. Finally, we discuss the current challenges and future direction for developing MN-based platforms for ISF extraction and sensing applications.

Flexible and Wearable Biosensors for Monitoring Health Conditions

Flexible and wearable biosensors have received tremendous attention over the past decade owing to their great potential applications in the field of health and medicine. Wearable biosensors serve as an ideal platform for real-time and continuous health monitoring, which exhibit unique properties such as self-powered, lightweight, low cost, high flexibility, detection convenience, and great conformability. This review introduces the recent research progress in wearable biosensors. First of all, the biological fluids often detected by wearable biosensors are proposed. Then, the existing micro-nanofabrication technologies and basic characteristics of wearable biosensors are summarized. Then, their application manners and information processing are also highlighted in the paper. Massive cutting-edge research examples are introduced such as wearable physiological pressure sensors, wearable sweat sensors, and wearable self-powered biosensors. As a significant content, the detection mechanism of these sensors was detailed with examples to help readers understand this area. Finally, the current challenges and future perspectives are proposed to push this research area forward and expand practical applications in the future.

Recent Advances in Two-Dimensional MXene-Based Electrochemical Biosensors for Sweat Analysis

Sweat, a biofluid secreted naturally from the eccrine glands of the human body, is rich in several electrolytes, metabolites, biomolecules, and even xenobiotics that enter the body through other means. Recent studies indicate a high correlation between the analytes' concentrations in the sweat and the blood, opening up sweat as a medium for disease diagnosis and other general health monitoring applications. However, low concentration of analytes in sweat is a significant limitation, requiring high-performing sensors for this application. Electrochemical sensors, due to their high sensitivity, low cost, and miniaturization, play a crucial role in realizing the potential of sweat as a key sensing medium. MXenes, recently developed anisotropic two-dimensional atomic-layered nanomaterials composed of early transition metal carbides or nitrides, are currently being explored as a material of choice for electrochemical sensors. Their large surface area, tunable electrical properties, excellent mechanical strength, good dispersibility, and biocompatibility make them attractive for bio-electrochemical sensing platforms. This review presents the recent progress made in MXene-based bio-electrochemical sensors such as wearable, implantable, and microfluidic sensors and their applications in disease diagnosis and developing point-of-care sensing platforms. Finally, the paper discusses the challenges and limitations of MXenes as a material of choice in bio-electrochemical sensors and future perspectives on this exciting material for sweat-sensing applications.

Multiplexed electrochemical assays for clinical applications

Rapid, accurate diagnoses are central to future efficient healthcare to identify diseases at early stages, avoid unnecessary treatment, and improve outcomes. Electrochemical techniques have been applied in many ways to support clinical applications by enabling the analysis of relevant disease biomarkers in user-friendly, sensitive, low-cost assays. Electrochemistry offers a launchpad for multiplexed biomarker assays that offer more accurate and precise diagnostics compared to single biomarker assays. In this short review, we underpin the importance of multiplexed analyses and provide a universal overview of current electrochemical assay strategies for multiple biomarkers. We highlight relevant examples of electrochemical methods that successfully quantify important disease biomarkers. Finally, we offer a future outlook on possible strategies that can be employed to increase throughput, sensitivity, and specificity of multiplexed electrochemical assays.

An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability

Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient's status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.

Mechano-luminescence Behavior of Lanthanide-Doped Fluoride Nanocrystals for Three-Dimensional Stress Imaging

Pervasive mechanical force in nature and human activities is closely related to intriguing physics and widespread applications. However, describing stress distribution timely and precisely in three dimensions to avoid "groping in the dark" is still a formidable challenge, especially for nonplanar structures. Herein, we realize three-dimensional (3D) stress imaging for sharp arbitrary targets via advanced 3D printing, owing to the use of fluoride nanocrystal(NC)-based ink. Notably, a fascinating mechano-luminescence (ML) is observed for the homogeneously dispersed NaLuF₄:Tb³⁺ NCs (∼25 nm) with rationally designed deep traps (at 0.88 and 1.02 eV) via incorporating Cs⁺ ions and using X-ray irradiation. Carriers captured in the corresponding traps are steadily released under mechanical stimulations, which enables a ratio metric luminescence intensity based on the applied force. As a result, a significant mechano-optical conversion and superior optical waveguide of the corresponding transparent printed targets demonstrate stress in 3D with a high spatial and temporal resolution based on stereovision. These results highlight the optical function of the 3D-printed fluoride NCs, which cast light into the black boxes of stress described in space, benefiting us in understanding the ubiquitous force relevant to most natural and engineering processes.

Advancing Patient Care: Innovative Use of Near-Infrared Spectroscopy for Monitoring Urine Volume in Neurogenic Bladder

PURPOSE

Current guidelines recommend clean intermittent catheterization (CIC) at regular time intervals for patients with spinal cord injuries; however, many patients experience difficulties. Performing time-based CIC outside the home is a significant burden for patients. In this study, we aimed to overcome the limitations of the current guidelines by developing a digital device to monitor bladder urine volume in real-time.

METHODS

The optode sensor is a near-infrared spectroscopy (NIRS)-based wearable device intended to be attached to the skin of the lower abdomen where the bladder is located. The sensor's primary function is to detect changes in urine volume within the bladder. An in vitro study was conducted using a bladder phantom that mimicked the optical properties of the lower abdomen. To validate the data in the human body at the proof-of-concept level, one volunteer attached the device to the lower abdomen to measure the light intensity between the first voiding and immediately before the second voiding.

RESULTS

The degree of attenuation at the maximum test volume was equivalent across experiments, and the optode sensor with multiplex measurements demonstrated robust performance for patient diversity. Moreover, the symmetric feature of the matrix was deemed a potential parameter for identifying the accuracy of sensor localization in a deep-learning model. The validated feasibility of the sensor showed almost the same results as an ultrasound scanner, which is routinely used in the clinical field.

CONCLUSION

The optode sensor of the NIRS-based wearable device can measure the urine volume in the bladder in real-time.

Advances in Integration, Wearable Applications, and Artificial Intelligence of Biomedical Microfluidics Systems

Microfluidics attracts much attention due to its multiple advantages such as high throughput, rapid analysis, low sample volume, and high sensitivity. Microfluidics has profoundly influenced many fields including chemistry, biology, medicine, information technology, and other disciplines. However, some stumbling stones (miniaturization, integration, and intelligence) strain the development of industrialization and commercialization of microchips. The miniaturization of microfluidics means fewer samples and reagents, shorter times to results, and less footprint space consumption, enabling a high throughput and parallelism of sample analysis. Additionally, micro-size channels tend to produce laminar flow, which probably permits some creative applications that are not accessible to traditional fluid-processing platforms. The reasonable integration of biomedical/physical biosensors, semiconductor microelectronics, communications, and other cutting-edge technologies should greatly expand the applications of current microfluidic devices and help develop the next generation of lab-on-a-chip (LOC). At the same time, the evolution of artificial intelligence also gives another strong impetus to the rapid development of microfluidics. Biomedical applications based on microfluidics normally bring a large amount of complex data, so it is a big challenge for researchers and technicians to analyze those huge and complicated data accurately and quickly. To address this problem, machine learning is viewed as an indispensable and powerful tool in processing the data collected from micro-devices. In this review, we mainly focus on discussing the integration, miniaturization, portability, and intelligence of microfluidics technology.

Skin-Interfaced Wearable Sweat Sensors for Precision Medicine

Wearable sensors hold great potential in empowering personalized health monitoring, predictive analytics, and timely intervention toward personalized healthcare. Advances in flexible electronics, materials science, and electrochemistry have spurred the development of wearable sweat sensors that enable the continuous and noninvasive screening of analytes indicative of health status. Existing major challenges in wearable sensors include: improving the sweat extraction and sweat sensing capabilities, improving the form factor of the wearable device for minimal discomfort and reliable measurements when worn, and understanding the clinical value of sweat analytes toward biomarker discovery. This review provides a comprehensive review of wearable sweat sensors and outlines state-of-the-art technologies and research that strive to bridge these gaps. The physiology of sweat, materials, biosensing mechanisms and advances, and approaches for sweat induction and sampling are introduced. Additionally, design considerations for the system-level development of wearable sweat sensing devices, spanning from strategies for prolonged sweat extraction to efficient powering of wearables, are discussed. Furthermore, the applications, data analytics, commercialization efforts, challenges, and prospects of wearable sweat sensors for precision medicine are discussed.

Recent Progress in MXene Hydrogel for Wearable Electronics

Recently, hydrogels have attracted great attention because of their unique properties, including stretchability, self-adhesion, transparency, and biocompatibility. They can transmit electrical signals for potential applications in flexible electronics, human-machine interfaces, sensors, actuators, et al. MXene, a newly emerged two-dimensional (2D) nanomaterial, is an ideal candidate for wearable sensors, benefitting from its surface's negatively charged hydrophilic nature, biocompatibility, high specific surface area, facile functionalization, and high metallic conductivity. However, stability has been a limiting factor for MXene-based applications, and fabricating MXene into hydrogels has been proven to significantly improve their stability. The unique and complex gel structure and gelation mechanism of MXene hydrogels require intensive research and engineering at nanoscale. Although the application of MXene-based composites in sensors has been widely studied, the preparation methods and applications of MXene-based hydrogels in wearable electronics is relatively rare. Thus, in order to facilitate the effective evolution of MXene hydrogel sensors, the design strategies, preparation methods, and applications of MXene hydrogels for flexible and wearable electronics are comprehensively discussed and summarized in this work.

A wearable electrochemical fabric for cytokine monitoring

Wearable biosensors monitoring various biomarkers in sweat provide comprehensive and prompt profiling of health states at molecular levels. Cytokines existed in sweat with trace amounts play an important role in cellular activity modulation. Unfortunately, flexible and wearable biosensors for cytokine monitoring have not yet been achieved due to the limitation of membrane-based structure and sensing strategy. Herein, we develop a novel electrochemical fabric based on aptamer-functionalized carbon nanotube/graphene fibers for real-time and in situ monitoring of IL-6, a paramount cytokine biomarker for inflammation and cancer. This fabric system possesses flexibility, anti-fatigue ability and breathability for wearable applications and can apply to different body parts in various forms. Moreover, the electrochemical fabric can track other biomarkers by replacing the coupling aptamer, serving as a universal platform for sweat analysis. This fabric-based platform holds the potential to facilitate an intelligent and personalized health monitoring approach.

Biocompatible Material-Based Flexible Biosensors: From Materials Design to Wearable/Implantable Devices and Integrated Sensing Systems

Human beings have a greater need to pursue life and manage personal or family health in the context of the rapid growth of artificial intelligence, big data, the Internet of Things, and 5G/6G technologies. The application of micro biosensing devices is crucial in connecting technology and personalized medicine. Here, the progress and current status from biocompatible inorganic materials to organic materials and composites are reviewed and the material-to-device processing is described. Next, the operating principles of pressure, chemical, optical, and temperature sensors are dissected and the application of these flexible biosensors in wearable/implantable devices is discussed. Different biosensing systems acting in vivo and in vitro, including signal communication and energy supply are then illustrated. The potential of in-sensor computing for applications in sensing systems is also discussed. Finally, some essential needs for commercial translation are highlighted and future opportunities for flexible biosensors are considered.

Flexible, Permeable, and Recyclable Liquid-Metal-Based Transient Circuit Enables Contact/Noncontact Sensing for Wearable Human-Machine Interaction

The past several years have witnessed a rapid development of intelligent wearable devices. However, despite the splendid advances, the creation of flexible human-machine interfaces that synchronously possess multiple sensing capabilities, wearability, accurate responsivity, sensitive detectivity, and fast recyclability remains a substantial challenge. Herein, a convenient yet robust strategy is reported to craft flexible transient circuits via stencil printing liquid metal conductor on the water-soluble electrospun film for human-machine interaction. Due to the inherent liquid conductor within porous substrate, the circuits feature high-resolution, customized patterning viability, attractive permeability, excellent electroconductivity, and superior mechanical stability. More importantly, such circuits display appealing noncontact proximity capabilities while maintaining compelling tactile sensing performance, which is unattainable by traditional systems with compromised contact sensing. As such, the flexible circuit is utilized as wearable sensors with practical multifunctionality, including information transfer, smart identification, and trajectory monitoring. Furthermore, an intelligent human-machine interface composed of the flexible sensors is fabricated to realize specific goals such as wireless object control and overload alarm. The transient circuits are quickly and efficiently recycled toward high economic and environmental values. This work opens vast possibilities of generating high-quality flexible and transient electronics for advanced applications in soft and intelligent systems.

Dual Structural Design of Platinum-Nickel Hydrogels for Wearable Glucose Biosensing with Ultrahigh Stability

Wearable glucose sensors are of great significance and highly required in mobile health monitoring and management but suffering from limited long-term stability and wearable adaptability. Here a simultaneous component and structure engineering strategy is presented, which involves Pt with abundant Ni to achieve three-dimensional, dual-structural Pt-Ni hydrogels with interconnected networks of PtNi nanowires and Ni(OH)₂ nanosheets, showing prominent electrocatalytic activity and stability in glucose oxidation under neutral condition. Specifically, the PtNi_(1:3) dual hydrogels shows 2.0 and 270.6 times' activity in the glucose electro-oxidation as much as the pure Pt and Ni hydrogels. Thanks to the high activity, structural stability, good flexibility, and self-healing property, the PtNi_(1:3) dual gel-based non-enzymatic glucose sensing chip is endowed with high performance. It features a high sensitivity, an excellent selectivity and flexibility, and particularly an outstanding long-term stability over 2 months. Together with a pH sensor and a wireless circuit, an accurate, real-time, and remote monitoring of sweat glucose is achieved. This facile design of novel dual-structural metallic hydrogels sheds light to rationally develop new functional materials for high-performance wearable biosensors.

Printable Epsilon-Type Structure Transistor Arrays with Highly Reliable Physical Unclonable Functions

Printed electronics promises to drive the future data-intensive technologies, with its potential to fabricate novel devices over a large area with low cost on nontraditional substrates. In these emerging technologies, there exists a large digital information flow, which requires secure communication and authentication. Physical unclonable functions (PUFs) offer a promising built-in hardware-security system comparable to biometrical data, which can be constructed by device-specific intrinsic variations in the additive manufacturing process of active devices. However, printed PUFs typically exploit the inherent variation in layer thickness and roughness of active devices. The current in devices with enough significant changes to increase the robustness to external environment noise is still a challenge. Here, printable epsilon-type-structure indium tin oxide transistor arrays are demonstrated to construct high-reliability PUFs by modifying the coffee-ring structure. The epsilon-type structure improves the printing scalability, film quality, and device reliability. Furthermore, the print-induced uncertainty along the channel thickness and length can lead to changes in the carrier concentration. Notably, the randomly distributed printing droplets in a small area significantly increase this uncertainty. As a result, the PUFs exhibit near-ideal uniformity, uniqueness, randomness, and reliability. Additionally, the PUFs are resilient against machine-learning-based attacks with a prediction accuracy of only 55% without postprocessing.

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.

Advanced Electronic Packaging Technology: From Hard to Soft

Packaging is a pivotal step in electronic device manufacturing, determining the translational performance of bare chips [...].

Strain and Pressure Sensors Based on MWCNT/PDMS for Human Motion/Perception Detection

Flexible wearable devices have attracted wide attention in capacious fields because of their real-time and continuous monitoring of human information. The development of flexible sensors and corresponding integration with wearable devices is of great significance to build smart wearable devices. In this work, multi-walled carbon nanotube/polydimethylsiloxane-based (MWCNT/PDMS) resistive strain sensors and pressure sensors were developed to integrate a smart glove for human motion/perception detection. Firstly, MWCNT/PDMS conductive layers with excellent electrical and mechanical properties (resistivity of 2.897 KΩ · cm, elongation at break of 145%) were fabricated via a facile scraping-coating method. Then, a resistive strain sensor with a stable homogeneous structure was developed due to the similar physicochemical properties of the PDMS encapsulation layer and MWCNT/PDMS sensing layer. The resistance changes of the prepared strain sensor exhibited a great linear relationship with the strain. Moreover, it could output obvious repeatable dynamic response signals. It still had good cyclic stability and durability after 180° bending/restoring cycles and 40% stretching/releasing cycles. Secondly, MWCNT/PDMS layers with bioinspired spinous microstructures were formed by a simple sandpaper retransfer process and then assembled face-to-face into a resistive pressure sensor. The pressure sensor presented a linear relationship of relative resistance change and pressure in the range of 0–31.83 KPa with a sensitivity of 0.026 KPa−1, and a sensitivity of 2.769 × 10−4 KPa−1 over 32 KPa. Furthermore, it responded quickly and kept good cycle stability at 25.78 KPa dynamic loop over 2000 s. Finally, as parts of a wearable device, resistive strain sensors and a pressure sensor were then integrated into different areas of the glove. The cost-effective, multi-functional smart glove can recognize finger bending, gestures, and external mechanical stimuli, which holds great potential in the fields of medical healthcare, human-computer cooperation, and so on.

3D-Printed Microinjection Needle Arrays via a Hybrid DLP-Direct Laser Writing Strategy

Microinjection protocols are ubiquitous throughout biomedical fields, with hollow microneedle arrays (MNAs) offering distinctive benefits in both research and clinical settings. Unfortunately, manufacturing-associated barriers remain a critical impediment to emerging applications that demand high-density arrays of hollow, high-aspect-ratio microneedles. To address such challenges, here, a hybrid additive manufacturing approach that combines digital light processing (DLP) 3D printing with "ex situ direct laser writing (esDLW)" is presented to enable new classes of MNAs for fluidic microinjections. Experimental results for esDLW-based 3D printing of arrays of high-aspect-ratio microneedles-with 30 μm inner diameters, 50 μm outer diameters, and 550 μm heights, and arrayed with 100 μm needle-to-needle spacing-directly onto DLP-printed capillaries reveal uncompromised fluidic integrity at the MNA-capillary interface during microfluidic cyclic burst-pressure testing for input pressures in excess of 250 kPa (n = 100 cycles). Ex vivo experiments perform using excised mouse brains reveal that the MNAs not only physically withstand penetration into and retraction from brain tissue but also yield effective and distributed microinjection of surrogate fluids and nanoparticle suspensions directly into the brains. In combination, the results suggest that the presented strategy for fabricating high-aspect-ratio, high-density, hollow MNAs could hold unique promise for biomedical microinjection applications.

Recent Advances in Nanomaterials Used for Wearable Electronics

In recent decades, thriving Internet of Things (IoT) technology has had a profound impact on people's lifestyles through extensive information interaction between humans and intelligent devices. One promising application of IoT is the continuous, real-time monitoring and analysis of body or environmental information by devices worn on or implanted inside the body. This research area, commonly referred to as wearable electronics or wearables, represents a new and rapidly expanding interdisciplinary field. Wearable electronics are devices with specific electronic functions that must be flexible and stretchable. Various novel materials have been proposed in recent years to meet the technical challenges posed by this field, which exhibit significant potential for use in different wearable applications. This article reviews recent progress in the development of emerging nanomaterial-based wearable electronics, with a specific focus on their flexible substrates, conductors, and transducers. Additionally, we discuss the current state-of-the-art applications of nanomaterial-based wearable electronics and provide an outlook on future research directions in this field.

A Systematic Review on the Advanced Techniques of Wearable Point-of-Care Devices and Their Futuristic Applications

Personalized point-of-care testing (POCT) devices, such as wearable sensors, enable quick access to health monitoring without the use of complex instruments. Wearable sensors are gaining popularity owing to their ability to offer regular and continuous monitoring of physiological data by dynamic, non-invasive assessments of biomarkers in biofluids such as tear, sweat, interstitial fluid and saliva. Current advancements have concentrated on the development of optical and electrochemical wearable sensors as well as advances in non-invasive measurements of biomarkers such as metabolites, hormones and microbes. For enhanced wearability and ease of operation, microfluidic sampling, multiple sensing, and portable systems have been incorporated with materials that are flexible. Although wearable sensors show promise and improved dependability, they still require more knowledge about interaction between the target sample concentrations in blood and non-invasive biofluids. In this review, we have described the importance of wearable sensors for POCT, their design and types of these devices. Following which, we emphasize on the current breakthroughs in the application of wearable sensors in the realm of wearable integrated POCT devices. Lastly, we discuss the present obstacles and forthcoming potentials including the use of Internet of Things (IoT) for offering self-healthcare using wearable POCT.