Stretchable respiration sensors: Advanced designs and multifunctional platforms for wearable physiological monitoring

Advances in Soft Strain and Pressure Sensors

Soft strain and pressure sensors represent a breakthrough in material engineering and nanotechnology, providing accurate and reliable signal detection for applications in health monitoring, sports management, human-machine interface, or soft robotics, when compared to traditional rigid sensors. However, their performance is often compromised by environmental interference and off-axis mechanical deformations, which lead to nonspecific responses, as well as unstable and inaccurate measurements. These challenges can be effectively addressed by enhancing the sensors' specificity, making them responsive only to the desired stimulus while remaining insensitive to unwanted stimuli. This review systematically examines various materials and design strategies for developing strain and pressure sensors with high specificity for target physical signals, such as tactility, pressure distribution, body motions, or artery pulse. This review highlights approaches in materials engineering that impart special properties to the sensors to suppress interference from factors such as temperature, humidity, and liquid contact. Additionally, it details structural designs that improve sensor performance under different types of off-axis mechanical deformations. This review concludes by discussing the ongoing challenges and opportunities for inspiring the future development of highly specific electromechanical sensors.

An intelligent humidity sensing system for human behavior recognition

An intelligent humidity sensing system has been developed for real-time monitoring of human behaviors through respiration detection. The key component of this system is a humidity sensor that integrates a thermistor and a micro-heater. This sensor employs porous nanoforests as its sensing material, achieving a sensitivity of 0.56 pF/%RH within a range of 60-90% RH, along with excellent long-term stability and superior gas selectivity. The micro-heater in the device provides a high operating temperature, enhancing sensitivity by 5.8 times. This significant improvement enables the capture of weak humidity variations in exhaled gases, while the thermistor continuously monitors the sensor's temperature during use and provides crucial temperature information related to respiration. With the assistance of a machine learning algorithm, a behavior recognition system based on the humidity sensor has been constructed, enabling behavior states to be classified and identified with an accuracy of up to 96.2%. This simple yet intelligent method holds great potential for widespread applications in medical assistance analysis and daily health monitoring.

Recent Advances in Nanomaterial-Based Biosignal Sensors

Recent research for medical fields, robotics, and wearable electronics aims to utilize biosignal sensors to gather bio-originated information and generate new values such as evaluating user well-being, predicting behavioral patterns, and supporting disease diagnosis and prevention. Notably, most biosignal sensors are designed for body placement to directly acquire signals, and the incorporation of nanomaterials such as metal-based nanoparticles or nanowires, carbon-based or polymer-based nanomaterials-offering stretchability, high surface-to-volume ratio, and tunability for various properties-enhances their adaptability for such applications. This review categorizes nanomaterial-based biosignal sensors into three types and analyzes them: 1) biophysical sensors that detect deformation such as folding, stretching, and even pulse, 2) bioelectric sensors that capture electric signal originating from human body such as heart and nerves, and 3) biochemical sensors that catch signals from bio-originated fluids such as sweat, saliva and blood. Then, limitations and improvements to nanomaterial-based biosignal sensors is depicted. Lastly, it is highlighted on deep learning-based signal processing and human-machine interface applications, which can enhance the potential of biosignal sensors. Through this paper, it is aim to provide an understanding of nanomaterial-based biosignal sensors, outline the current state of the technology, discuss the challenges that be addressed, and suggest directions for development.

Recent progress of black phosphorene from preparation to diversified bio-/chemo-nanosensors and their challenges and opportunities for comprehensive health

The introduction of comprehensive health, related to human living environment and mental state, helps people to improve human health literacy and accept scientific health guidance. The unique structure and properties of black phosphorene (BP) provide potential opportunities for rapid development and versatile applications of high-performance sensors serving comprehensive health. The review begins with the preparation from bulk black phosphorous crystals via transforming requirements of phosphorous allotropes and BP nanosheets via preparative strategies using both "top-down" and "bottom-up" methods. Then the diversified modification of BP and versatile fabrication of diversified bio-/chemo-nanosensors for sensitive detection of analytes are discussed. Besides, the challenges including the preparation of BP, diversified modification, devices for improving performance defects and chemo-/bio-nanosensors for enhancing performance are outlined together with potential opportunities for the BP preparation and applications in comprehensive health from agricultural environments, food safety, personal life, physical and mental life, and finally to medical care.

Surface-Enhanced Raman Scattering-Based Multimodal Techniques: Advances and Perspectives

Surface-enhanced Raman scattering (SERS) spectroscopy is a versatile molecular fingerprinting technique with rapid signal readout, high aqueous compatibility, and portability. To translate SERS for real-world applications, it is pertinent to overcome inherent challenges, including high sample variability and heterogeneity, matrix effects, and nonlinear SERS signal responses of different analytes in complex (bio)chemical matrices with numerous interfering species. In this perspective, we highlight emerging SERS-based multimodal techniques to address the key roadblocks to improving the sensitivity, specificity, and reliability of (bio)chemical detection, bioimaging, theragnosis, and theragnostic. SERS-based multimodal techniques can be broadly categorized into two categories: (1) complementary methods or systems that work together to achieve a common goal where each method compensates for the weaknesses of the other to culminate in a single enhanced outcome or (2) orthogonal techniques that are independent and provide separate but corroborating results simultaneously without interfering with each other. These multimodal techniques maximize information gained from a single experiment to achieve enhanced qualitative or quantitative analysis and broaden the range of detectable analytes from small molecules to tissues. Finally, we discuss emerging directions in multimodal platform design, instrument integration, and data analytics that aim to push the analytical limits of holistic detection.

Bioinspired Photoluminescent "Spider Web" as Ultrafast and Ultrasensitive Airflow-Acoustic Bimodal Sensor for Human-Computer Interaction and Intelligent Recognition

Nature provides massive biomimetic design inspiration for constructing structural materials with desired performances. Spider webs can perceive vibrations generated by airflow and acoustic waves from prey and transfer the corresponding information to spiders. Herein, by mimicking the perception capability and structure of a spider web, an ultrafast and ultrasensitive airflow-acoustic bimodal sensor (HOF-TCPB@SF) is developed based on the postfunctionalization of hydrogen-bonded organic framework (HOF-TCPB) on silk film (SF) through hydrogen bonds. The "spider web-like" HOF-TCPB@SF possesses light weight and high elasticity, endowing this airflow sensor with superior properties including an ultralow detection limit (DL, 0.0076 m s-1), and excellent repeatability (480 cycles). As an acoustic sensor, HOF-TCPB@SF exhibits ultrahigh sensitivity (105140.77 cps Pa-1 cm-2) and ultralow DL (0.2980 dB), with the greatest response frequency of 375 Hz and the ability to identify multiple sounds. Moreover, both airflow and acoustic sensing processes show an ultrafast response speed (40 ms) and multiangle recognition response (0-180°). The perception mechanisms of airflow and acoustic stimuli are analyzed through finite element simulation. This bimodal sensor also achieves real-time airflow monitoring, speech recognition, and airflow-acoustic interoperability based on human-computer interaction, which holds great promise for applications in health care, tunnel engineering, weather forecasting, and intelligent textiles.

Application of Biomass-Based Triboelectrification for Particulate Matter Removal

Electrostatic fields are crucial for achieving the highly efficient filtration of airborne pollutants. However, the dissipation of static charges over time, especially under humid conditions, limits their practical application. In this study, we present a self-charging air filter (SAF) powered by a triboelectric nanogenerator (TENG). This SAF is integrated into a commercial mask, termed SAFM, which can effectively capture and degrade airborne pollutants without requiring an external power source. By leveraging the triboelectric effect during breathing, the TENG within the SAFM continuously replenishes static charges, maintaining the triboelectric field. The system employs a cellulose aerogel/Ti3C2Tx composite as the electron donor and an esterified cellulose-based electrospun nanofiber as the electron acceptor. Remarkably, the triboelectric field significantly enhances filtration performance, with the SAF achieving up to 95.7% filtration efficiency for particulate matter as small as 0.3 μm. This work underscores the potential of TENG-powered triboelectric fields in the development of multifunctional, human-machine interactive facemasks.

A Flexible Capacitive Pressure Sensor with Adjustable Detection Range Based on the Inflatable Dielectric Layer for Human-Computer Interaction

As an essential component in wearable electronic devices and intelligent robots, flexible pressure sensors have enormous application value in fields such as healthcare, human-computer interaction, and intelligent perception. However, due to the complex and ever-changing pressure loads borne by sensors in different application scenarios, this also puts great demands on the flexible response and adjustment ability of a sensor's detection range. Therefore, developing a flexible pressure sensor with a wide and adjustable detection range, which can be applied flexibly under different pressure loads, is also a major challenge in current research. In this paper, we propose a flexible pressure sensor with a wide and adjustable detection range based on an inflatable adjustable safety airbag as the dielectric layer. This sensor uses inflatable airbags prepared using 3D printing technology and silicone reverse molding technology as the dielectric layer and achieves high sensitivity (0.6 kPa-1 to 1.19 kPa-1), wide detection range (220-1500 kPa), and flexible performance applicability by adjusting the air pressure inside the dielectric layer. At the same time, its simple production process, convenient production, fast response time (100 ms), and good stability provide the possibility for the flexible application of sensors in different pressure detection. The experimental results indicate that the sensor has enormous potential for applications in wearable devices, healthcare, human-computer interaction, and intelligent perception recognition.

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

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

A multifunctional triboelectric nanogenerator based on PDMS/MXene for bio-mechanical energy harvesting and volleyball training monitoring

Within the domain of wearable devices that are self-powered and sensory, triboelectric nanogenerators (TENGs) have surfaced as a notable solution to meet the growing needs for energy harvesting. This study unveils an innovative wearable and stretchable multifunctional double-layered TENG, based on PDMS/MXene, known as PM-TENG. Furthermore, PM-TENG can also be used as a joint sensor to monitor the movement of athletes' joints during volleyball training. By augmenting the matrix with PDMS/MXene, which possesses dual capabilities-namely, charge capture and charge movement-the intermediary layer is integrated. This leads to a two fold increase in the ability to trap charges and the overall triboelectric performance. With a power density reaching 11.27 mW, it notably exceeds the performance of its counterparts that solely utilize PDMS, by nearly 11 times. This academic effort elucidates the important role of PM-TENG in biomechanical energy capture and autonomous wearable sports motion sensing.

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

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

Flexible Antibacterial Respiratory Monitoring Sensor Based on Controllable Au-Modified Surface of Highly {001} Preferred Anatase Titanium Dioxide Thin Film

Respiratory signals are critical clinical diagnostic criteria for respiratory diseases and health conditions, and respiratory sensors play a crucial role in achieving the desired respiratory monitoring effect. High sensitivity to a single factor can improve the reliability of respiratory monitoring, and maintaining the hygiene of the sensors is also important for daily health monitoring. Herein, we propose a flexible Au-modified anatase titanium dioxide resistive respiratory sensor, which can be mechanically compliantly attached to curved surfaces for respiratory monitoring in different modalities (i.e., respiratory intensity, frequency, and rate). The uniform and preferentially oriented anatase titanium dioxide films gained by the polymer-assisted deposition technique can be fabricated on flexible substrates through a liquid-assisted transferring process. The Au modification can enhance surface plasmon resonance to facilitate the photocatalytic activity of titanium dioxide, and the optimized distribution of Au on the surface of titanium dioxide film made the sensor have an excellent antibacterial effect. The uniquely designed encapsulation can effectively control the contact between the surface of titanium dioxide films and electrodes, allowing the flexible sensor to exhibit fast response time (0.71 s) and recovery time (1.06 s) to respiratory as well as insensitivity or low sensitivity to other factors (i.e., gas composition, humidity, temperature, stress, and strain). This work provided an effective strategy for flexible wearable respiratory sensors and has great potential in daily respiratory monitoring for health management and pandemic control.

Cellulose Nanofiber-Based Triboelectric Nanogenerators for Efficient Air Filtration in Harsh Environments

Advanced portable healthcare devices with high efficiencies, small pressure drops, and high-temperature resistance are urgently desired in harsh environments with high temperatures, high humidities, and high levels of atmospheric pollution. Triboelectric nanogenerators (TENGs), which serve as energy converters in a revolutionary self-powered sensor device, present a sustainable solution for meeting these requirements. In this work, we developed a porous negative triboelectric material by synthesizing ZIF-8 on the surface of a cellulose/graphene oxide aerogel, grafting it with trimethoxy(1H,1H,2H,2H-heptadecafluorodecyl)silane, and adding a negative corona treatment, and it was combined with a positive triboelectric material to create a cellulose nanofiber-based TENG self-powered filter. The devices achieved a balance between a small pressure drop (53 Pa) and high filtration efficiency (98.97%, 99.65%, and 99.93% for PM0.3, PM0.5, and PM1, respectively), demonstrating robust filtration properties at high temperatures and high humidities. Our work provides a new approach for developing self-powered wearable healthcare devices with excellent air filtration properties.

A Soft Bioelectronic Patch for Simultaneous Respiratory and Cardiovascular Monitoring

Sleep is critical to maintaining physical and mental health. Measuring physiological parameters to quantify sleep quality without uncomfortable user experience remains highly desired but a challenge. Here, this work develops a soft bioelectronic patch to perform simultaneous respiration and cardiovascular monitoring during sleep in a wearable and non-invasive manner. The soft bioelectronic patch system is mainly composed of a pressure sensor, a flexible printed circuit for signal processing, and a soft thermoplastic urethane mold for assembling different functional modules. The soft bioelectronic patch holds a sensitivity of >0.12 V kPa-1 and a remarkable low-frequency response from 0.5 to 15 Hz. It is demonstrated to continuously monitor respiration and heartbeat during the whole night, which could be harnessed for sleep monitoring and obstructive sleep apnea-hypopnea syndrome diagnosis. The reported soft bioelectronic patch represents a simple and convenient platform technology for sleep study.

Progress of Research on Conductive Hydrogels in Flexible Wearable Sensors

Conductive hydrogels, characterized by their excellent conductivity and flexibility, have attracted widespread attention and research in the field of flexible wearable sensors. This paper reviews the application progress, related challenges, and future prospects of conductive hydrogels in flexible wearable sensors. Initially, the basic properties and classifications of conductive hydrogels are introduced. Subsequently, this paper discusses in detail the specific applications of conductive hydrogels in different sensor applications, such as motion detection, medical diagnostics, electronic skin, and human-computer interactions. Finally, the application prospects and challenges are summarized. Overall, the exceptional performance and multifunctionality of conductive hydrogels make them one of the most important materials for future wearable technologies. However, further research and innovation are needed to overcome the challenges faced and to realize the wider application of conductive hydrogels in flexible sensors.

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

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

Stretch Sensor: Development of Biodegradable Film

This article presents research on biodegradable stretch sensors produced using biological material. This sensor uses a piezoresistive effect to indicate stretch, which can be used for force measurement. In this work, an attempt was made to develop the composition of a sensitive material and to design a sensor. The biodegradable base was made from a κ-carrageenan compound mixed with Fe2O3 microparticles and glycerol. The influence of the weight fraction and iron oxide microparticles on the tensile strength and Young's modulus was experimentally investigated. Tensile test specimens consisted of 10-25% iron oxide microparticles of various sizes. The results showed that increasing the mass fraction of the reinforcement improved the Young's modulus compared to the pure sample and decreased the elongation percentage. The GF of the developed films varies from 0.67 to 10.47 depending on composition. In this paper, it was shown that the incorporation of appropriate amounts of Fe2O3 microparticles into κ-carrageenan can achieve dramatic improvements in mechanical properties, resulting in elongation of up to 10%. The developed sensors were experimentally tested, and their sensitivity, stability, and range were determined. Finally, conclusions were drawn on the results obtained.

Facile Fabrication of "Tacky", Stretchable, and Aligned Carbon Nanotube Sheet-Based Electronics for On-Skin Health Monitoring

Point-of-care monitoring of physiological signals such as electrocardiogram, electromyogram, and electroencephalogram is essential for prompt disease diagnosis and quick treatment, which can be realized through advanced skin-worn electronics. However, it is still challenging to design an intimate and nonrestrictive skin-contact device for physiological measurements with high fidelity and artifact tolerance. This research presents a facile method using a "tacky" surface to produce a tight interface between the ACNT skin-like electronic and the skin. The method provides the skin-worn electronic with a stretchability of up to 70% strain, greater than that of most common epidermal electrodes. Low-density ACNT bundles facilitate the infiltration of adhesive and improve the conformal contact between the ACNT sheet and the skin, while dense ACNT bundles lessen this effect. The stretchability and conformal contact allow the ACNT sheet-based electronics to create a tight interface with the skin, which enables the high-fidelity measurement of physiological signals (the Pearson's coefficient of 0.98) and tolerance for motion artifacts. In addition, our method allows the use of degradable substrates to enable reusability and degradability of the electronics based on ACNT sheets, integrating "green" properties into on-skin electronics.

Fully Integrated Patch Based on Lamellar Porous Film Assisted GaN Optopairs for Wireless Intelligent Respiratory Monitoring

Respiratory pattern is one of the most crucial indicators for accessing human health, but there has been limited success in implementing fast-responsive, affordable, and miniaturized platforms with the capability for smart recognition. Herein, a fully integrated and flexible patch for wireless intelligent respiratory monitoring based on a lamellar porous film functionalized GaN optoelectronic chip with a desirable response to relative humidity (RH) variation is reported. The submillimeter-sized GaN device exhibits a high sensitivity of 13.2 nA/%RH at 2-70%RH and 61.5 nA/%RH at 70-90%RH, and a fast response/recovery time of 12.5 s/6 s. With the integration of a wireless data transmission module and the assistance of machine learning based on 1-D convolutional neural networks, seven breathing patterns are identified with an overall classification accuracy of >96%. This integrated and flexible on-mask sensing platform successfully demonstrates real-time and intelligent respiratory monitoring capability, showing great promise for practical healthcare applications.

Evaluation of blood pressure using a flexible and wearable capacitive pressure sensor

In recent times, the high demand for flexible and wearable pressure sensors to monitor human health, particularly for patients afflicted with hypertension or high blood pressure (BP), has captured the keen interest of researchers. Capacitance-based flexible sensing devices offer real-time metrics regarding vital physiological parameters of the human body, such as BP and pulse rate (PR), thereby enabling the identification of cardiovascular complications. In this regard, we have developed a capacitive pressure sensor using polydimethylsiloxane (PDMS) and deionized water (DIW) and improved its key parameters by adding baking powder to PDMS-DIW. The sensor demonstrated excellent performance in static pressure measurements with a sensitivity of 0.021 Pa-1, detection limit of 1 Pa, and response time of 100 ms. We further investigated its application in human BP monitoring. The sensor successfully captured the oscillometric waveform (OMW) for all 160 participants and demonstrated excellent performance in accurately measuring BP, meeting all criteria outlined as the universal standard when compared with the reference devices: OMRON BP device and the gold-standard mercury-based sphygmomanometer. Furthermore, the sensor accurately provided the PR and agreed well with the reference BP device. Therefore, the developed BP sensor can be a viable alternative to replace the pressure sensors in existing BP devices.

Ultrathin hierarchical hydrogel-carbon nanocomposite for highly stretchable fast-response water-proof wearable humidity sensors

Wearable humidity sensors play an important role in human health monitoring. However, challenges persist in realizing high performance wearable humidity sensors with fast response and good stretchability and durability. Here we report wearable humidity sensors employing an ultrathin micro-nano hierarchical hydrogel-carbon nanocomposite. The nanocomposite is synthesized on polydimethylsiloxane (PDMS) films via a facile two-step solvent-free approach, which creates a hierarchical architecture consisting of periodic microscale wrinkles and vapor-deposited nanoporous hydrogel-candle-soot nanocoating. The hierarchical surface topography results in a significantly enlarged specific surface area (>107 times that of planar hydrogel), which along with the ultrathin hydrogel endow the sensor with high sensitivity and a fast response/recovery (13/0.48 s) over a wide humidity range (11-96%). Owing to the wrinkle structure and interpenetrating network between the hydrogel and PDMS, the sensor is stable and durable against repeated 180° bending, 100% strain, and even scratching. Furthermore, encapsulation of the sensor imparts excellent resistance to water, sweat, and bacteria without influencing its performance. The sensor is then successfully used to monitor different human respiratory behaviors and skin humidity in real time. The reported method is convenient and cost-effective, which could bring exciting new opportunities in the fabrication of next-generation wearable humidity sensors.

Smart Detecting and Versatile Wearable Electrical Sensing Mediums for Healthcare

A rapidly expanding global population and a sizeable portion of it that is aging are the main causes of the significant increase in healthcare costs. Healthcare in terms of monitoring systems is undergoing radical changes, making it possible to gauge or monitor the health conditions of people constantly, while also removing some minor possibilities of going to the hospital. The development of automated devices that are either attached to organs or the skin, continually monitoring human activity, has been made feasible by advancements in sensor technologies, embedded systems, wireless communication technologies, nanotechnologies, and miniaturization being ultra-thin, lightweight, highly flexible, and stretchable. Wearable sensors track physiological signs together with other symptoms such as respiration, pulse, and gait pattern, etc., to spot unusual or unexpected events. Help may therefore be provided when it is required. In this study, wearable sensor-based activity-monitoring systems for people are reviewed, along with the problems that need to be overcome. In this review, we have shown smart detecting and versatile wearable electrical sensing mediums in healthcare. We have compiled piezoelectric-, electrostatic-, and thermoelectric-based wearable sensors and their working mechanisms, along with their principles, while keeping in view the different medical and healthcare conditions and a discussion on the application of these biosensors in human health. A comparison is also made between the three types of wearable energy-harvesting sensors: piezoelectric-, electrostatic-, and thermoelectric-based on their output performance. Finally, we provide a future outlook on the current challenges and opportunities.

Wearable Graphene-based smart face mask for Real-Time human respiration monitoring

After the pandemic of SARS-CoV-2, the use of face-masks is considered the most effective way to prevent the spread of virus-containing respiratory fluid. As the virus targets the lungs directly, causing shortness of breath, continuous respiratory monitoring is crucial for evaluating health status. Therefore, the need for a smart face mask (SFM) capable of wirelessly monitoring human respiration in real-time has gained enormous attention. However, some challenges in developing these devices should be solved to make practical use of them possible. One key issue is to design a wearable SFM that is biocompatible and has fast responsivity for non-invasive and real-time tracking of respiration signals. Herein, we present a cost-effective and straightforward solution to produce innovative SFMs by depositing graphene-based coatings over commercial surgical masks. In particular, graphene nanoplatelets (GNPs) are integrated into a polycaprolactone (PCL) polymeric matrix. The resulting SFMs are characterized morphologically, and their electrical, electromechanical, and sensing properties are fully assessed. The proposed SFM exhibits remarkable durability (greater than1000 cycles) and excellent fast response time (∼42 ms), providing simultaneously normal and abnormal breath signals with clear differentiation. Finally, a developed mobile application monitors the mask wearer's breathing pattern wirelessly and provides alerts without compromising user-friendliness and comfort.

Soft Fiber Electronics Based on Semiconducting Polymer

Fibers, originating from nature and mastered by human, have woven their way throughout the entire history of human civilization. Recent developments in semiconducting polymer materials have further endowed fibers and textiles with various electronic functions, which are attractive in applications such as information interfacing, personalized medicine, and clean energy. Owing to their ability to be easily integrated into daily life, soft fiber electronics based on semiconducting polymers have gained popularity recently for wearable and implantable applications. Herein, we present a review of the previous and current progress in semiconducting polymer-based fiber electronics, particularly focusing on smart-wearable and implantable areas. First, we provide a brief overview of semiconducting polymers from the viewpoint of materials based on the basic concepts and functionality requirements of different devices. Then we analyze the existing applications and associated devices such as information interfaces, healthcare and medicine, and energy conversion and storage. The working principle and performance of semiconducting polymer-based fiber devices are summarized. Furthermore, we focus on the fabrication techniques of fiber devices. Based on the continuous fabrication of one-dimensional fiber and yarn, we introduce two- and three-dimensional fabric fabricating methods. Finally, we review challenges and relevant perspectives and potential solutions to address the related problems.

Radial artery pulse wave velocity: a new characterization technique and the instabilities associated with the respiratory phase and breath-holding

Objective. Pulse wave velocity (PWV) is a key diagnostic parameter of the cardiovascular system's state. However, approaches aimed at PWV characterization often suffer from inevitable drawbacks. Statistical results demonstrating how closely PWV in the radial artery (RA) and the respiration phase correlate, as well as RA PWV evolution during breath-holding (BH), have not yet been presented in the literature. The aims of this study are (a) to propose a simple robust technique for measuring RA PWV, (b) to reveal the phase relation between the RA PWV and spontaneous breathing, and (c) to disclose the influence of BH on the RA PWV.Approach.The high-resolution remote breathing monitoring method Sorption-Enhanced Infrared Thermography (SEIRT) and the new technique aimed at measuring RA PWV described in this paper were used synchronously, and their measurement data were processed simultaneously.Main results. Spontaneous breathing leaves a synchronous 'trace' on the RA PWV. The close linear correlation of the respiration phase and the phase of concomitant RA PWV changes is statistically confirmed in five tested people (Pearson's r is of the order of 0.5-0.8, P < 0.05). The BH appreciably affects the RA PWV. A phenomenon showing that the RA PWV is not indifferent to hypoxia is observed for the first time.Significance.The proposed technique for RA PWV characterization has high prospects in biomedical diagnostics. The presented pilot study deserves attention in the context of the mutual interplay between respiratory and cardiovascular systems. It may also be useful in cases where peripheral pulse wave propagation helps assess respiratory function.

From Piezoelectric Nanogenerator to Non-Invasive Medical Sensor: A Review

Piezoelectric nanogenerators (PENGs) not only are able to harvest mechanical energy from the ambient environment or body and convert mechanical signals into electricity but can also inform us about pathophysiological changes and communicate this information using electrical signals, thus acting as medical sensors to provide personalized medical solutions to patients. In this review, we aim to present the latest advances in PENG-based non-invasive sensors for clinical diagnosis and medical treatment. While we begin with the basic principles of PENGs and their applications in energy harvesting, this review focuses on the medical sensing applications of PENGs, including detection mechanisms, material selection, and adaptive design, which are oriented toward disease diagnosis. Considering the non-invasive in vitro application scenario, discussions about the individualized designs that are intended to balance a high performance, durability, comfortability, and skin-friendliness are mainly divided into two types: mechanical sensors and biosensors, according to the key role of piezoelectric effects in disease diagnosis. The shortcomings, challenges, and possible corresponding solutions of PENG-based medical sensing devices are also highlighted, promoting the development of robust, reliable, scalable, and cost-effective medical systems that are helpful for the public.

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.

Wearable respiratory sensors for COVID-19 monitoring

Since its outbreak in 2019, COVID-19 becomes a pandemic, severely burdening the public healthcare systems and causing an economic burden. Thus, societies around the world are prioritizing a return to normal. However, fighting the recession could rekindle the pandemic owing to the lightning-fast transmission rate of SARS-CoV-2. Furthermore, many of those who are infected remain asymptomatic for several days, leading to the increased possibility of unintended transmission of the virus. Thus, developing rigorous and universal testing technologies to continuously detect COVID-19 for entire populations remains a critical challenge that needs to be overcome. Wearable respiratory sensors can monitor biomechanical signals such as the abnormities in respiratory rate and cough frequency caused by COVID-19, as well as biochemical signals such as viral biomarkers from exhaled breaths. The point-of-care system enabled by advanced respiratory sensors is expected to promote better control of the pandemic by providing an accessible, continuous, widespread, noninvasive, and reliable solution for COVID-19 diagnosis, monitoring, and management.

Quantitative and Real-Time Evaluation of Human Respiration Signals with a Shape-Conformal Wireless Sensing System

Respiration signals reflect many underlying health conditions, including cardiopulmonary functions, autonomic disorders and respiratory distress, therefore continuous measurement of respiration is needed in various cases. Unfortunately, there is still a lack of effective portable electronic devices that meet the demands for medical and daily respiration monitoring. This work showcases a soft, wireless, and non-invasive device for quantitative and real-time evaluation of human respiration. This device simultaneously captures respiration and temperature signatures using customized capacitive and resistive sensors, encapsulated by a breathable layer, and does not limit the user's daily life. Further a machine learning-based respiration classification algorithm with a set of carefully studied features as inputs is proposed and it is deployed into mobile clients. The body status of users, such as being quiet, active and coughing, can be accurately recognized by the algorithm and displayed on clients. Moreover, multiple devices can be linked to a server network to monitor a group of users and provide each user with the statistical duration of physiological activities, coughing alerts, and body health advice. With these devices, individual and group respiratory health status can be quantitatively collected, analyzed, and stored for daily physiological signal detections as well as medical assistance.

Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis

Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respiratory diseases. In this work, a smart face mask is proposed with an air-permeable and biodegradable self-powered breath sensor as the key component. This smart face mask is easily fabricated, comfortable to use, eco-friendly, and has sensitive and stable output performances in real wearable conditions. To verify the practicability, we use smart face masks to record respiratory signals of patients with chronic respiratory diseases when the patients do not have obvious symptoms. With the assistance of the machine learning algorithm of the bagged decision tree, the accuracy for distinguishing the healthy group and three groups of chronic respiratory diseases (asthma, bronchitis, and chronic obstructive pulmonary disease) is up to 95.5%. These results indicate that the strategy of this work is feasible and may promote the development of wearable health monitoring systems.

Flexible pressure and temperature dual-mode sensor based on buckling carbon nanofibers for respiration pattern recognition

Breathing condition is an essential physiological indicator closely related to human health. Wearable flexible breath sensors for respiration pattern recognition have attracted much attention as they can provide physiological signal details for personal medical diagnosis, health monitoring, etc. However, present smart mask based on flexible breath sensors using single-mode detection can only detect a relatively small number of respiration patterns, especially lacking the ability to accurately distinguish mouth breath from nasal one. Herein, a smart face mask incorporated with a dual-sensing mode breathing sensor that can recognize up to eight human respiration patterns is fabricated. The breathing sensor uses novel three dimensional (3D) buckling carbon nanofiber mats as active materials to realize the function of pressure and temperature sensing simultaneously. The pressure model of the sensors shows a high sensitivity that are able to precisely detect pressure generated by respiratory airflow, while the temperature model can realize non-contact temperature variation caused by breath. Benefit from the capacity of real-time recognition and accurate distinguishing between mouth breath and nasal breath, the face mask is further developed to monitor the development of mouth breathing syndrome. The dual-sensing mode sensor has great potential applications in health monitoring.

Breath monitoring, sleep disorder detection, and tracking using thin-film acoustic waves and open-source electronics

Apnoea, a major sleep disorder, affects many adults and causes several issues, such as fatigue, high blood pressure, liver conditions, increased risk of type II diabetes, and heart problems. Therefore, advanced monitoring and diagnosing tools of apnoea disorders are needed to facilitate better treatment, with advantages such as accuracy, comfort of use, cost effectiveness, and embedded computation capabilities to recognise, store, process, and transmit time series data. In this work we present an adaptation of our apnoea-Pi open-source surface acoustic wave (SAW) platform (Apnoea-Pi) to monitor and recognise apnoea in patients. The platform is based on a thin-film SAW device using bimorph ZnO and Al structures, including those fabricated as Al foils or plates, to achieve breath tracking based on humidity and temperature changes. We applied open-source electronics and provided embedded computing characteristics for signal processing, data recognition, storage, and transmission of breath signals. We show that the thin-film SAW device out-performed standard and off-the-shelf capacitive electronic sensors in terms of their response and accuracy for human breath-tracking purposes. This in combination with embedded electronics makes a suitable platform for human breath monitoring and sleep disorder recognition.

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

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

A remote consultation system for sports injury based on wireless sensor network

INTRODUCTION: Although current research methods can realize the effective collection of human physiological signals in the health monitoring system, they cannot obtain the ideal detection effect due to the influence of the communication performance in the health monitoring system. OBJECTIVES: In order to improve the monitoring performance of remote consultation, a sports injury remote consultation system based on wireless sensor network is designed. METHODS: The wearable sensors is used in the body area network to collect human physiological signals. Through the wireless sensor network of the wireless communication module, the collected human physiological signals are transmitted to the remote consultation module. The wireless communication module selects CC2530 chip as the core chip of the wireless communication module. A fixed partition routing algorithm based on energy balance is used to stably transmit human physiological signals. RESULTS: The consultation personnel of the remote consultation module make a sports injury consultation judgment based on the received physiological signal results of the human body. The system test results show that the designed system can accurately monitor various physiological indicators of the human body. The wireless sensor network energy consumption of the system in this paper is all less than 500J, the energy consumption variance of the cluster head is less than 4×10-3, and the number of surviving nodes can be guaranteed to be higher than 130. It has high communication performance of wireless sensor network. CONCLUSION: The system can accurately judge whether there is a sports injury according to the monitoring results of physiological indicators, and realize the effective consultation of sports injury.

A Deep-Learning-Assisted On-Mask Sensor Network for Adaptive Respiratory Monitoring

Wearable respiratory monitoring is a fast, non-invasive, and convenient approach to provide early recognition of human health abnormalities like restrictive and obstructive lung diseases. Here, a computational fluid dynamics assisted on-mask sensor network is reported, which can overcome different user facial contours and environmental interferences to collect highly accurate respiratory signals. Inspired by cribellate silk, Rayleigh-instability-induced spindle-knot fibers are knitted for the fabrication of permeable and moisture-proof textile triboelectric sensors that hold a decent signal-to-noise ratio of 51.2 dB, a response time of 0.28 s, and a sensitivity of 0.46 V kPa^-1 . With the assistance of deep learning, the on-mask sensor network can realize the respiration pattern recognition with a classification accuracy up to 100%, showing great improvement over a single respiratory sensor. Additionally, a customized user-friendly cellphone application is developed to connect the processed respiratory signals for real-time data-driven diagnosis and one-click health data sharing with the clinicians. The deep-learning-assisted on-mask sensor network opens a new avenue for personalized respiration management in the era of the Internet of Things.

Assessing Respiratory Activity by Using IMUs: Modeling and Validation

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

Helical Fiber Strain Sensors Based on Triboelectric Nanogenerators for Self-Powered Human Respiratory Monitoring

Respiration is a major vital sign, which can be used for early illness diagnosis and physiological monitoring. Wearable respiratory sensors present an exciting opportunity to monitor human respiratory behaviors in a real-time, noninvasive, and comfortable way. Among them, fiber-shaped triboelectric nanogenerators (FS-TENGs) are attractive for their comfort and high degree of freedom. However, the single-electrode FS-TENGs cannot respond to their own tensile strains, and the coaxial double-electrode FS-TENGs show low sensitivity to strain due to structural limitations. Here, a type of helical fiber strain sensor (HFSS) is developed, which can respond to tiny tensile strains. In addition, a smart wearable real-time respiratory monitoring system is developed based on the HFSSs, which can measure some key breathing parameters for disease prevention and medical diagnosis. An intelligent alarm can automatically call a preset mobile phone for help in response to respiratory behavior changes. This work provides an effective helical structure for fabricating highly sensitive strain sensors based on FS-TENGs and develops wearable self-powered real-time respiratory monitoring systems.

Simultaneous Biomechanical and Biochemical Monitoring for Self-Powered Breath Analysis

The high moisture level of exhaled gases unavoidably limits the sensitivity of breath analysis via wearable bioelectronics. Inspired by pulmonary lobe expansion/contraction observed during respiration, a respiration-driven triboelectric sensor (RTS) was devised for simultaneous respiratory biomechanical monitoring and exhaled acetone concentration analysis. A tin oxide-doped polyethyleneimine membrane was devised to play a dual role as both a triboelectric layer and an acetone sensing material. The prepared RTS exhibited excellent ability in measuring respiratory flow rate (2-8 L/min) and breath frequency (0.33-0.8 Hz). Furthermore, the RTS presented good performance in biochemical acetone sensing (2-10 ppm range at high moisture levels), which was validated via finite element analysis. This work has led to the development of a novel real-time active respiratory monitoring system and strengthened triboelectric-chemisorption coupling sensing mechanism.

Recent Advances in Multiresponsive Flexible Sensors towards E-skin: A Delicate Design for Versatile Sensing

Multiresponsive flexile sensors with strain, temperature, humidity, and other sensing abilities serving as real electronic skin (e-skin) have manifested great application potential in flexible electronics, artificial intelligence (AI), and Internet of Things (IoT). Although numerous flexible sensors with sole sensing function have already been reported since the concept of e-skin, that mimics the sensing features of human skin, was proposed about a decade ago, the ones with more sensing capacities as new emergences are urgently demanded. However, highly integrated and highly sensitive flexible sensors with multiresponsive functions are becoming a big thrust for the detection of human body motions, physiological signals (e.g., skin temperature, blood pressure, electrocardiograms (ECG), electromyograms (EMG), sweat, etc.) and environmental stimuli (e.g., light, magnetic field, volatile organic compounds (VOCs)), which are vital to real-time and all-round human health monitoring and management. Herein, this review summarizes the design, manufacturing, and application of multiresponsive flexible sensors and presents the future challenges of fabricating these sensors for the next-generation e-skin and wearable electronics.

Remote Healthcare for Elderly People Using Wearables: A Review

The growth of health care spending on older adults with chronic diseases faces major concerns that require effective measures to be adopted worldwide. Among the main concerns is whether recent technological advances now offer the possibility of providing remote health care for the aging population. The benefits of suitable prevention and adequate monitoring of chronic diseases by using emerging technological paradigms such as wearable devices and the Internet of Things (IoT) can increase the detection rates of health risks to raise the quality of life for the elderly. Specifically, on the subject of remote health monitoring in older adults, a first approach is required to review devices, sensors, and wearables that serve as tools for obtaining and measuring physiological parameters in order to identify progress, limitations, and areas of opportunity in the development of health monitoring schemes. For these reasons, a review of articles on wearable devices was presented in the first instance to identify whether the selected articles addressed the needs of aged adults. Subsequently, the direct review of commercial and prototype wearable devices with the capability to read physiological parameters was presented to identify whether they are optimal or usable for health monitoring in older adults.

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

Wearable sensors are emerging as a new technology to detect physiological and biochemical markers for remote health monitoring. By measuring vital signs such as respiratory rate, body temperature, and blood oxygen level, wearable sensors offer tremendous potential for the noninvasive and early diagnosis of numerous diseases such as Covid-19. Over the past decade, significant progress has been made to develop wearable sensors with high sensitivity, accuracy, flexibility, and stretchability, bringing to reality a new paradigm of remote health monitoring. In this review paper, the latest advances in wearable sensor systems that can measure vital signs at an accuracy level matching those of point-of-care tests are presented. In particular, the focus of this review is placed on wearable sensors for measuring respiratory behavior, body temperature, and blood oxygen level, which are identified as the critical signals for diagnosing and monitoring Covid-19. Various designs based on different materials and working mechanisms are summarized. This review is concluded by identifying the remaining challenges and future opportunities for this emerging field.

Electronic Textiles for Wearable Point-of-Care Systems

Traditional public health systems are suffering from limited, delayed, and inefficient medical services, especially when confronted with the pandemic and the aging population. Fusing traditional textiles with diagnostic, therapeutic, and protective medical devices can unlock electronic textiles (e-textiles) as point-of-care platform technologies on the human body, continuously monitoring vital signs and implementing round-the-clock treatment protocols in close proximity to the patient. This review comprehensively summarizes the research advances on e-textiles for wearable point-of-care systems. We start with a brief introduction to emphasize the significance of e-textiles in the current healthcare system. Then, we describe textile sensors for diagnosis, textile therapeutic devices for medical treatment, and textile protective devices for prevention, by highlighting their working mechanisms, representative materials, and clinical application scenarios. Afterward, we detail e-textiles' connection technologies as the gateway for real-time data transmission and processing in the context of 5G technologies and Internet of Things. Finally, we provide new insights into the remaining challenges and future directions in the field of e-textiles. Fueled by advances in chemistry and materials science, textile-based diagnostic devices, therapeutic devices, protective medical devices, and communication units are expected to interact synergistically to construct intelligent, wearable point-of-care textile platforms, ultimately illuminating the future of healthcare system in the Internet of Things era.

Recent progress of self-powered respiration monitoring systems

Wearable and implantable medical devices are playing more and more key roles in disease diagnosis and health management. Various biosensors and systems have been used for respiration monitoring. Among them, self-powered sensors have some special characteristics such as low-cost, easy preparation, highly designable, and diversified. The respiratory airflow can drive the self-powered sensors directly to convert mechanical energy of the airflow into electricity. One of the major goals of the self-powered sensors and systems is realizing health monitoring and diagnosis. The relationship between the output signals and the models of respiratory diseases has not been studied deeply and clearly. Therefore, how to find an accurate relationship between them is a challenging and significant research topic. This review summarized the recent progress of the self-powered respiratory sensors and systems from aspects of device principle, output property, detecting index and so on. The challenges and perspectives have also been discussed for reference to the researchers who are interested in the field of self-powered sensors.

Enabling Longitudinal Respiration Monitoring Using Vapor-Coated Conducting Textiles

Wearable sensors allow for portable, long-term health monitoring in natural environments. Recently, there has been an increase in demand for technology that can reliably monitor respiration, which can be indicative of cardiac diseases, asthma, and infection by respiratory viruses. However, to date, the most reliable respiration monitoring system involves a tightly worn chest belt that is not conducive to longitudinal monitoring. Herein, we report that accurate respiration monitoring can be effected using a fabric-based humidity sensor mounted within a face mask. Our humidity sensor is created using cotton fabrics coated with a persistently p-doped conjugated polymer, poly(3,4-ethylenedioxythiophene):chloride (PEDOT-Cl), using a previously reported chemical vapor deposition process. The vapor-deposited polymer coating displays a stable, rapid, and reversible change in conductivity with an increase in local humidity, such as the humidity changes experienced within a face mask as the wearer breathes. Thus, when integrated into a face mask, the PEDOT-Cl-coated cotton humidity sensor is able to transduce breaths into an electrical signal. The humidity sensor-incorporated face mask is able to differentiate between deep and shallow breathing, as well as breathing versus talking. The sensor-incorporated face mask platform also functions both while walking and sitting, providing equally high signal quality in both indoor and outdoor contexts. Additionally, we show that the face mask can be worn for long periods of time with a negligible decline in the signal quality.

Discovering giant magnetoelasticity in soft matter for electronic textiles

We discovered a giant magnetoelasticity in soft matter with up to 5-fold enhancement of magnetomechanical coupling factors compared to that of rigid metal alloys without an externally applied magnetic field. A wavy chain analytical model based on the magnetic dipole-dipole interaction and demagnetizing field was established, fitting well to the experimental observation. To explore its potentials in electronic textiles, we coupled it with magnetic induction to invent a textile magnetoelastic generator (MEG), a new working mechanism for biomechanical energy conversion, featuring an intrinsic waterproofness, an ultralow internal impedance of approximately 20 Ω, and a high short-circuit current density of 1.37 mA/cm², which is about four orders of magnitude higher than that of other textile generator counterparts. Meanwhile, assisted by machine learning, the textile MEG could continuously monitor the respiratory activities on heavily perspiring skin without any encapsulation, allowing a timely diagnosis of the respiration abnormalities in a self-powered manner. We foresee that this discovery can be extended to wide-range soft-matter systems, emerging as a compelling approach to develop electronic textiles for energy, sensing, and therapeutic applications.

Artificial Intelligence Based Body Sensor Network Framework-Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

With the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality 'big data' in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

Strain sensors fabricated by surface assembly of nanoparticles

Harnessing interparticle spatial properties of surface assembly of nanoparticles (SAN) on flexible substrates is a rapidly emerging front of research in the design and fabrication of highly-sensitive strain sensors. It has recently shown promising potentials for applications in wearable sensors and skin electronics. SANs feature 3D structural tunability of the interparticle spatial properties at both molecular and nanoscale levels, which is transformative for the design of intriguing strain sensors. This review will present a comprehensive overview of the recent research development in exploring SAN-structured strain sensors for wearable applications. It starts from the basic principle governing the strain sensing characteristics of SANs on flexible substrates in terms of thermally-activated interparticle electron tunneling and conductive percolation. This discussion is followed by descriptions of the fabrication of the sensors and the proof-of-concept demonstrations of the strain sensing characteristics. The nanoparticles in the SANs are controllable in terms of size, shape, and composition, whereas the interparticle molecules enable the tunability of the electrical properties in terms of interparticle spatial properties. The design of SAN-derived strain sensors is further highlighted by describing several recent examples in the explorations of their applications in wearable biosensor and bioelectronics. Fundamental understanding of the role of interparticle spatial properties within SANs at both molecular and device levels is the focal point. The future direction of the SAN-derived wearable sensors will also be discussed, shining lights on a potential paradigm shift in materials design in exploring the emerging opportunities in wearable sensors and skin electronics.

Advances in ultrasensitive piezoresistive sensors: from conventional to flexible and stretchable applications

The piezoresistive effect has been a dominant mechanical sensing principle that has been widely employed in a range of sensing applications. This transducing concept still receives great attention because of the huge demand for developing small, low-cost, and high-performance sensing devices. Many researchers have extensively explored new methods to enhance the piezoresistive effect and to make sensors more and more sensitive. Many interesting phenomena and mechanisms to enhance the sensitivity have been discovered. Numerous review papers on the piezoresistive effect have been published; however, there is no comprehensive review article that thoroughly analyses methods and approaches to enhance the piezoresistive effect. This paper comprehensively reviews and presents all the advanced enhancement methods ranging from the quantum physical effect and new materials to nanoscopic and macroscopic structures, and from conventional rigid to flexible, stretchable and wearable applications. In addition, the paper summarises results recently achieved on applying the above-mentioned innovative sensing enhancement techniques in making extremely sensitive piezoresistive transducers.