A Wearable Body Condition Sensor System with Wireless Feedback Alarm Functions

Design of AI-Enhanced and Hardware-Supported Multimodal E-Skin for Environmental Object Recognition and Wireless Toxic Gas Alarm

Post-earthquake rescue missions are full of challenges due to the unstable structure of ruins and successive aftershocks. Most of the current rescue robots lack the ability to interact with environments, leading to low rescue efficiency. The multimodal electronic skin (e-skin) proposed not only reproduces the pressure, temperature, and humidity sensing capabilities of natural skin but also develops sensing functions beyond it-perceiving object proximity and NO2 gas. Its multilayer stacked structure based on Ecoflex and organohydrogel endows the e-skin with mechanical properties similar to natural skin. Rescue robots integrated with multimodal e-skin and artificial intelligence (AI) algorithms show strong environmental perception capabilities and can accurately distinguish objects and identify human limbs through grasping, laying the foundation for automated post-earthquake rescue. Besides, the combination of e-skin and NO2 wireless alarm circuits allows robots to sense toxic gases in the environment in real time, thereby adopting appropriate measures to protect trapped people from the toxic environment. Multimodal e-skin powered by AI algorithms and hardware circuits exhibits powerful environmental perception and information processing capabilities, which, as an interface for interaction with the physical world, dramatically expands intelligent robots' application scenarios.

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S3CNN) for detecting sleep posture. This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S3CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S3CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach.

A Human-Computer Interaction Strategy for An FPGA Platform Boosted Integrated "Perception-Memory" System Based on Electronic Tattoos and Memristors

The integrated "perception-memory" system is receiving increasing attention due to its crucial applications in humanoid robots, as well as in the simulation of the human retina and brain. Here, a Field Programmable Gate Array (FPGA) platform-boosted system that enables the sensing, recognition, and memory for human-computer interaction is reported by the combination of ultra-thin Ag/Al/Paster-based electronic tattoos (AAP) and Tantalum Oxide/Indium Gallium Zinc Oxide (Ta2O5/IGZO)-based memristors. Notably, the AAP demonstrates exceptional capabilities in accommodating the strain caused by skin deformation, thanks to its unique structural design, which ensures a secure fit to the skin and enables the prolonged monitoring of physiological signals. By utilizing Ta2O5/IGZO as the functional layer, a high switching ratio is conferred to the memristor, and an integrated system for sensing, distinguishing, storing, and controlling the machine hand of multiple human physiological signals is constructed together with the AAP. Further, the proposed system implements emergency calls and smart homes using facial electromyogram signals and utilizing logical entailment to realize the control of the music interface. This innovative "perception-memory" integrated system not only serves the disabled, enhancing human-computer interaction but also provides an alternative avenue to enhance the quality of life and autonomy of individuals with disabilities.

Wireless Technologies in Flexible and Wearable Sensing: From Materials Design, System Integration to Applications

Wireless and wearable sensors attract considerable interest in personalized healthcare by providing a unique approach for remote, noncontact, and continuous monitoring of various health-related signals without interference with daily life. Recent advances in wireless technologies and wearable sensors have promoted practical applications due to their significantly improved characteristics, such as reduction in size and thickness, enhancement in flexibility and stretchability, and improved conformability to the human body. Currently, most researches focus on active materials and structural designs for wearable sensors, with just a few exceptions reflecting on the technologies for wireless data transmission. This review provides a comprehensive overview of the state-of-the-art wireless technologies and related studies on empowering wearable sensors. The emerging functional nanomaterials utilized for designing unique wireless modules are highlighted, which include metals, carbons, and MXenes. Additionally, the review outlines the system-level integration of wireless modules with flexible sensors, spanning from novel design strategies for enhanced conformability to efficient transmitting data wirelessly. Furthermore, the review introduces representative applications for remote and noninvasive monitoring of physiological signals through on-skin and implantable wireless flexible sensing systems. Finally, the challenges, perspectives, and unprecedented opportunities for wireless and wearable sensors are discussed.

Designing High Performance Carbon/ZnSn(OH)6-Based Humidity Sensors

In this work, pure phase and carbon/ZnSn(OH)6 samples were synthesized by a hydrothermal method. The composite sample's structure, morphology, and functional groups were investigated by X-ray diffraction, field-emission scanning electron microscopy, transmission electron microscopy, and Fourier transform infrared spectroscopy. Subsequently, ZnSn(OH)6 samples were modified with different carbon contents, and their humidity-sensing properties were investigated. The introduction of carbon increased the specific surface area of pure ZnSn(OH)6 samples, thus significantly improving the sensors' humidity sensing response. The C10-ZnSn(OH)6 sensor exhibited a high response, up to three orders of magnitude, a humidity hysteresisof 13.5%, a fast response time of 3.2 s, and a recovery time of 24.4 s. The humidity sensor's possible humidity sensing mechanism was also analyzed using the AC complex impedance puissance method with a simulated equivalent circuit. These results revealed that ZnSn(OH)6 can effectively detect ambient humidity and that the introduction of carbon significantly improves its humidity-sensing performance. The study provides an effective strategy for understanding and designing ZnSn(OH)6-based humidity sensors.

Somatosensory Electro-Thermal Actuator through the Laser-Induced Graphene Technology

The biological system realizes the unity of action and perception through the muscle tissue and nervous system. Correspondingly, artificial soft actuators realize the unity of sensing and actuating functions in a single functional material, which will have tremendous potential for developing intelligent and bionic soft robotics. This paper reports the design of a laser-induced graphene (LIG) electrothermal actuator with self-sensing capability. LIG, a functional material formed by a one-step direct-write lasing procedure under ambient air, is used as electrothermal conversion materials and piezoresistive sensing materials. By transferring LIG to a flexible silicone substrate, the design ability of the LIG-based actuator unit is enriched, along with an effectively improved sensing sensitivity. Through the integration of different types of well-designed LIG-based actuator units, the transformations from multidimensional precursors to 2D and 3D structures are realized. According to the piezoresistive effect of the LIG units during the deformation process, the visual synchronous deformation state feedback of the LIG-based actuator is proposed. The multimodal crawling soft robotics and the switchable electromagnetic shielding cloak serve as the demonstrations of the self-sensing LIG-based actuator, showing the advantage of the design in remote control of the soft robot without relying on the assistance of visual devices.

Femtosecond Laser Fabrication of High-Linearity Liquid Metal-Based Flexible Strain Sensor

Liquid metal (LM) is widely used in flexible electronic devices due to its excellent metallic conductivity and ductility. However, the fabrication of LM flexible strain sensors with high sensitivity and linearity is still a huge challenge, since the resistance of LM does not change much with strain. Here, a highly sensitive and linear fully flexible strain sensor with a resistive sensing function is proposed. The sensor comprises an Fe-doped liquid metal (Fe-LM) electrode for enhanced performance. The design and manufacturing of flexible strain sensors are based on the technology of controlling surface wettability by femtosecond laser micro/nano-processing. A supermetalphobic microstructure is constructed on a polydimethylsiloxane (PDMS) substrate to achieve the selection adhesion of Fe-LM on the PDMS substrate. The Fe-LM-based flexible strain sensor has high sensitivity and linearity, a gauge factor (GF) up to 1.18 in the strain range of 0-100%, excellent linearity with an R2 of 0.9978, a fast response time of 358 ms, and an excellent durability of more than 2400 load cycles. Additionally, the successful monitoring of human body signals demonstrates the potential of our developed flexible strain sensor in wearable monitoring applications.

A laser-engraved wearable gait recognition sensor system for exoskeleton robots

As a reinforcement technology that improves load-bearing ability and prevents injuries, assisted exoskeleton robots have extensive applications in freight transport and health care. The perception of gait information by such robots is vital for their control. This information is the basis for motion planning in assistive and collaborative functions. Here, a wearable gait recognition sensor system for exoskeleton robots is presented. Pressure sensor arrays based on laser-induced graphene are developed with flexibility and reliability. Multiple sensor units are integrated into an insole to detect real-time pressure at key plantar positions. In addition, the circuit hardware and the algorithm are designed to reinforce the sensor system with the capability of gait recognition. The experimental results show that the accuracy of gait recognition by the proposed system is 99.85%, and the effectiveness of the system is further verified through testing on an exoskeleton robot.

Nylon Fabric/GO Based Self-Powered Humidity Sensor Based on the Galvanic Cell Principle with High Air Permeability and Rapid-Response

Flexible humidity sensors have received more and more attention in people's lives, and the problems of gas permeability and power supply issues of the device have long been areas in need of improvement. In this work, inspired by the high air permeability of daily wear clothing and galvanic batteries, a self-powered humidity sensor with high air permeability and fast response is designed. A nylon fabric/GO net (as a humidity sensitive layer and solid electrolyte) is obtained by spraying technique. This structure enables the sensor to have fast response/recovery (0.78 s/0.93 s, calculated at 90% of the final value), ultra-high response (0.83 V) and excellent stability (over 150 cycles) at 35 °C. Such sensors are useful for health monitoring, such as non-contact monitoring of human respiratory rate before and after exercise, and monitoring a level of humidity in the palms, arms, and fingers. This research provides an idea for developing a flexible wearable humidity sensor that is both breathable and self-powered and can also be mass-produced similar to wearable clothing.

Scalable and Multifunctional Polyurethane/MXene/Carbon Nanotube-Based Fabric Sensor toward Baby Healthcare

Continuous monitoring of physiological health status and effective protection against external hazards is an indispensable aspect of healthcare management for critically vulnerable populations, particularly for infants or babies. So, the exploration of all-in-one devices remains critical to avoiding their injury and illness. The integration of multiple properties such as sensing, electromagnetic protection, warming/cooling, and water/bacterial repellence into a common fabric is no doubt a promising solution to coping with diverse application scenarios. However, achieving simultaneous integration in an effective and durable fashion faces huge challenges. Herein, multifunctional fabric was achieved by sequentially coating MXene, carbon nanotubes (CNTs), and self-healing polyurethane (PU) onto cotton fabric. The outstanding conductivity of MXene and CNTs as well as the self-healing ability of PU synergistically enable a flexible, breathable, protective, and sensing fabric with a good durability. It could detect the body motions like bending of the finger, elbow, wrist, and knee, with a high gauge factor of 8.78 and fast response. Moreover, this sensing fabric could protect the wearers against electromagnetic waves and bacteria, delivering a minimum reflection loss of -57.6 dB at 7.6 GHz and high bacterial inhibition efficiency due to the incorporation of MXene and polyethylenimine. Besides, the electrothermal performance of carbonaceous materials enables them to act as a heater for body warmth. The synergistic design of this multifunctional textile offers a promising strategy for producing advanced smart textiles, holding great promise in infant or baby healthcare.

Laser-Assisted Structuring of Graphene Films with Biocompatible Liquid Crystal Polymer for Skin/Brain-Interfaced Electrodes

The work presented here introduces a facile strategy for the development of flexible and stretchable electrodes that harness the robust characteristics of carbon nanomaterials through laser processing techniques on a liquid crystal polymer (LCP) film. By utilizing LCP film as a biocompatible electronic substrate, control is demonstrated over the laser irradiation parameters to achieve efficient pattern generation and transfer printing processes, thereby yielding highly conductive laser-induced graphene (LIG) bioelectrodes. To enhance the resolution of the patterned LIG film, shadow masks are employed during laser scanning on the LCP film surface. This approach is compatible with surface-mounted device integration, enabling the circuit writing of LIG/LCP materials in a flexible format. Moreover, kirigami-inspired on-skin bioelectrodes are introduced that exhibit reasonable stretchability, enabling independent connections to healthcare hardware platforms for electrocardiogram (ECG) and electromyography (EMG) measurements. Additionally, a brain-interfaced LIG microelectrode array is proposed that combines mechanically compliant architectures with LCP encapsulation for stimulation and recording purposes, leveraging their advantageous structural features and superior electrochemical properties. This developed approach offers a cost-effective and scalable route for producing patterned arrays of laser-converted graphene as bioelectrodes. These bioelectrodes serve as ideal circuit-enabled flexible substrates with long-term reliability in the ionic environment of the human body.

Laser Direct Writing of Flexible Thermal Flow Sensors

Thin film-based thermal flow sensors afford applications in healthcare and industries owing to their merits in preserving initial flow distributions. However, traditional thermal flow sensors are primarily applied to track flow intensities based on hot-wire or hot-film sensing mechanisms due to their relatively facile device configurations and fabrication strategies. Herein, a calorimetric thermal flow sensor is proposed based on laser direct writing to form laser-induced graphene as heaters and temperature sensors, resulting in monitoring both flow intensities and orientations. Via homogeneously surrounding spiral heaters with multiple temperature sensors, the device exhibits high sensitivity (∼162 K·s/m) at small flows with an extended flow detection range (∼25 m/s). Integrating the device with a data-acquisition board and a dual-mode graphical user interface enables wirelessly and dynamically monitoring respiration and the motion of robotic arms. This versatile flow sensor with facile manufacturing affords potentials in health inspection, remote monitoring, and studying hydrodynamics.

Machine Learning-Enabled Tactile Sensor Design for Dynamic Touch Decoding

Skin-like flexible sensors play vital roles in healthcare and human-machine interactions. However, general goals focus on pursuing intrinsic static and dynamic performance of skin-like sensors themselves accompanied with diverse trial-and-error attempts. Such a forward strategy almost isolates the design of sensors from resulting applications. Here, a machine learning (ML)-guided design of flexible tactile sensor system is reported, enabling a high classification accuracy (≈99.58%) of tactile perception in six dynamic touch modalities. Different from the intuition-driven sensor design, such ML-guided performance optimization is realized by introducing a support vector machine-based ML algorithm along with specific statistical criteria for fabrication parameters selection to excavate features deeply concealed in raw sensing data. This inverse design merges the statistical learning criteria into the design phase of sensing hardware, bridging the gap between the device structures and algorithms. Using the optimized tactile sensor, the high-quality recognizable signals in handwriting applications are obtained. Besides, with the additional data processing, a robot hand assembled with the sensor is able to complete real-time touch-decoding of an 11-digit braille phone number with high accuracy.

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.

Rational Design of Conductive Pathways in Flexible Tactile Sensors via Indirect 3D-Printing of Liquid Metal for High-Precision Monitoring and Recognition

Highly sensitive and conformal sensors are essential for the implementation of human-machine interfaces, health monitoring, and rehabilitation prostheses. The proper adjustment of conductive pathways in the sensing materials is essential for their sensitive transduction of mechanical stimuli into electrical signals. However, the rational, precise, and wide-range control of electrical networks within traditional conductive composites is difficult due to the randomly distributed fillers. Herein, we adopt an indirect 3D-printing method to fabricate pressure sensors with various microchannels for liquid metal (LM) to form consistent and tunable conductive pathways. LM is highly conductive, fluidic, and incompressible at ambient conditions, which guarantees the reliable regulation and function of our pressure sensor. Additive manufacturing provides a facile way to construct complicated microchannels with different lengths, different orientations, cross-sectional sizes, depth-width ratios, and shapes, which can effectively modulate the sensitivity and the sensing range. Under the optimized structural configurations, our sensor achieves a high sensitivity of 1.139 kPa-1, a detection range of 0-68 kPa (loading process), and stability of over 5000 cycles, whose sensing performance is better than most microchannel-filled LM sensors. It can achieve high-accuracy monitoring of pulse, speaking and gestures, and exhibit a full recognition of objects under the assistance of machine learning. This work can provide new ideas on the design of conductive pathways in flexible electronics and expand the application of recyclable LM in human-machine interfaces.

An End-to-End Dynamic Posture Perception Method for Soft Actuators Based on Distributed Thin Flexible Porous Piezoresistive Sensors

This paper proposes a method for accurate 3D posture sensing of the soft actuators, which could be applied to the closed-loop control of soft robots. To achieve this, the method employs an array of miniaturized sponge resistive materials along the soft actuator, which uses long short-term memory (LSTM) neural networks to solve the end-to-end 3D posture for the soft actuators. The method takes into account the hysteresis of the soft robot and non-linear sensing signals from the flexible bending sensors. The proposed approach uses a flexible bending sensor made from a thin layer of conductive sponge material designed for posture sensing. The LSTM network is used to model the posture of the soft actuator. The effectiveness of the method has been demonstrated on a finger-size 3 degree of freedom (DOF) pneumatic bellow-shaped actuator, with nine flexible sponge resistive sensors placed on the soft actuator's outer surface. The sensor-characterizing results show that the maximum bending torque of the sensor installed on the actuator is 4.7 Nm, which has an insignificant impact on the actuator motion based on the working space test of the actuator. Moreover, the sensors exhibit a relatively low error rate in predicting the actuator tip position, with error percentages of 0.37%, 2.38%, and 1.58% along the x-, y-, and z-axes, respectively. This work is expected to contribute to the advancement of soft robot dynamic posture perception by using thin sponge sensors and LSTM or other machine learning methods for control.

Wafer-Level, High-Performance, Flexible Sensors Based on Organic Nanoforests for Human-Machine Interactions

High-performance flexible sensors are essential for real-time information analysis and constructing noncontact communication modules for emerging human-machine interactions. In these applications, batch fabrication of sensors that exhibit high performance at the wafer level is in high demand. Here, we present organic nanoforest-based humidity sensor (NFHS) arrays on a 6 in. flexible substrate prepared via a facile, cost-effective manufacturing approach. Such an NFHS achieves state-of-the-art overall performance: high sensitivity and fast recovery time; the best properties are at a small device footprint. The high sensitivity (8.84 pF/% RH) and fast response time (5 s) of the as-fabricated organic nanoforests are attributed to the abundant hydrophilic groups, the ultra-large surface area with a huge number of nanopores, and the vertically distributed structures beneficial to the transfer of molecules up and down. The NFHS also exhibits excellent long-term stability (90 days), superior mechanical flexibility, and good performance repeatability after bending. With these superiorities, the NFHS is further applied as a smart noncontact switch, and the NFHS array is used as the motion trajectory tracker. The wafer-level batch fabrication capability of our NFHS provides a potential strategy for developing practical applications of such humidity sensors.

An Integrated Analog Front-End System on Flexible Substrate for the Acquisition of Bio-Potential Signals

The application of a versatile, low-temperature thin-film transistor (TFT) technology is presently described as the implementation on a flexible substrate of an analog front-end (AFE) system for the acquisition of bio-potential signals. The technology is based on semiconducting amorphous indium-gallium-zinc oxide (IGZO). The AFE system consists of three monolithically integrated constituent components: a bias-filter circuit with a bio-compatible low cut-off frequency of ≈1 Hz, a 4-stage differential amplifier offering a large gain-bandwidth product of ≈955 kHz, and an additional notch filter exhibiting over 30 dB suppression of the power-line noise. Respectively built using conductive IGZO electrodes with thermally induced donor agents and enhancement-mode fluorinated IGZO TFTs with exceptionally low leakage current, both capacitors and resistors with significantly reduced footprints are realized. Defined as the ratio of the gain-bandwidth product of an AFE system to its area, a record-setting figure-of-merit of ≈86 kHz mm^-2 is achieved. This is about an order of magnitude larger than the < 10 kHz mm^-2 of the nearest benchmark. Requiring no supplementary off-substrate signal-conditioning components and occupying an area of ≈11 mm² , the stand-alone AFE system is successfully applied to both electromyography and electrocardiography (ECG).

High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population

Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.

Direct Conversion of Liquid Organic Precursor into 3D Laser-Induced Graphene Materials

Among the different states of matter, liquids have particular advantages in terms of easy handling and recycling, which has been manifested in various chemosynthetic reactions, but remains underexplored in graphene synthesis. This work reports the direct conversion of liquid organic precursor into versatile 3D graphene materials using rapid laser irradiation. The liquid precursor allows for easy fabrication of graphene with significant 3D architectures, including powders, patterned composite structures, and substrate-free films. Taking advantage of the high compatibility of liquid precursor with a wide range of dopants, the 3D graphene can be further engineered together with various functional components, especially the high loading (≈15 wt.%) and well-dispersed (an average diameter of less than 50 nm) high-entropy alloy nanoparticles. Furthermore, combined with the 3D printing strategy, the rapid construction of graphene with complex and accurate 3D shapes is demonstrated via a selective in situ laser transforming (SLT) strategy. With the high structural integrity unachievable by traditional 3D printing methods, the obtained objects show an electrical conductivity of 4380 S m^-1 and a compressive modulus of 31.8 MPa. The results reported in this work will open up a new way for the fabrication of functional carbon materials with customizable shapes and components.

Carbon-Based Flexible Devices for Comprehensive Health Monitoring

Traditional public health systems suffer from incomprehensive, delayed, and inefficient medical services. Convenient and comprehensive health monitoring has been highly sought after recently. Flexible and wearable devices are attracting wide attention due to their potential applications in wearable human health monitoring and care systems. Using carbon materials with overall superiorities can facilitate the development of wearable and flexible devices with various functions and excellent performance, which can comprehensively and real-time monitor human health status and prevent diseases. Herein, the latest advances in the rational design and controlled fabrication of carbon materials for applications in health-related flexible and wearable electronics are reviewed. The fabrication strategies, working mechanism, performance, and applications in health monitoring of carbon-based flexible devices, including electromechanical sensors, temperature/humidity sensors, chemical sensors, and flexible conductive wires/electrodes, are reviewed. Furthermore, integrating multiple carbon-based devices into multifunctional wearable systems is discussed. Finally, the existing challenges and future opportunities in this field are also proposed.

Intelligent wearable devices based on nanomaterials and nanostructures for healthcare

Emerging classes of flexible electronic sensors as alternatives to conventional rigid sensors offer a powerful set of capabilities for detecting and quantifying physiological and physical signals from human skin in personal healthcare. Unfortunately, the practical applications and commercialization of flexible sensors are generally limited by certain unsatisfactory aspects of their performance, such as biocompatibility, low sensing range, power supply, or single sensory function. This review intends to provide up-to-date literature on wearable devices for smart healthcare. A systematic review is provided, from sensors based on nanomaterials and nanostructures, algorithms, to multifunctional integrated devices with stretchability, self-powered performance, and biocompatibility. Typical electromechanical sensors are investigated with a specific focus on the strategies for constructing high-performance sensors based on nanomaterials and nanostructures. Then, the review emphasizes the importance of tailoring the fabrication techniques in order to improve stretchability, biocompatibility, and self-powered performance. The construction of wearable devices with high integration, high performance, and multi-functionalization for multiparameter healthcare is discussed in depth. Integrating wearable devices with appropriate machine learning algorithms is summarized. After interpretation of the algorithms, intelligent predictions are produced to give instructions or predictions for smart implementations. It is desired that this review will offer guidance for future excellence in flexible wearable sensing technologies and provide insight into commercial wearable sensors.

Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring: Part 1 wearable technology

With the development of Artificial Intelligence techniques, smart health monitoring is becoming more popular. In this study, we investigate the trend of wearable sensors being adopted and developed in neonatal cardiorespiratory monitoring. We performed a search of papers published from the year 2000 onwards. We then reviewed the advances in sensor technologies and wearable modalities for this application. Common wearable modalities included clothing (39%); chest/abdominal belts (25%); and adhesive patches (15%). Popular singular physiological information from sensors included electrocardiogram (15%), breathing (24%), oxygen saturation and photoplethysmography (13%). Many studies (46%) incorporated a combination of these signals. There has been extensive research in neonatal cardiorespiratory monitoring using both single and multi-parameter systems. Poor data quality is a common issue and further research into combining multi-sensor information to alleviate this should be investigated. IMPACT STATEMENT: State-of-the-art review of sensor technology for wearable neonatal cardiorespiratory monitoring. Review of the designs for wearable neonatal cardiorespiratory monitoring. The use of multi-sensor information to improve physiological data quality has been limited in past research. Several sensor technologies have been implemented and tested on adults that have yet to be explored in the newborn population.

Graphene and Liquid Metal Integrated Multifunctional Wearable Platform for Monitoring Motion and Human-Machine Interfacing

Motion sensors are an essential component of many electronic systems. However, the development of inertial motion sensors based on fatigue-free soft proof mass has not been explored extensively in the field of soft electronics. Nontoxic gallium-based liquid metals are an emerging class of material that exhibit attractive electromechanical properties, making them excellent proof mass materials for inertial sensors. Here, we propose and demonstrate a fully soft laser-induced graphene (LIG) and liquid metal-based inertial sensor integrated with temperature, humidity, and breathing sensors. The inertial sensor design confines a graphene-coated liquid metal droplet inside a fluidic channel, rolling over LIG resistive electrode. The proposed sensor architecture and material realize a highly mobile proof mass and a vibrational space for its oscillation. The inertial sensor exhibits a high sensitivity of 6.52% m^-1 s² and excellent repeatability (over 12 500 cycles). The platform is fabricated using a scalable, rapid laser writing technique and integrated with a programmable system on a chip (PSoC) to function as a stand-alone system for real-time wireless monitoring of movement patterns and the control of a robotic arm. The developed printed inertial platform is an excellent candidate for the next-generation of wearables motion tracking platforms and soft human-machine interfaces.

Governance mechanisms for chronic disease diagnosis and treatment systems in the post-pandemic era

"Re-visits and drug renewal" is difficult for chronic disease patients during COVID-19 and will continue in the post-pandemic era. To overcome this dilemma, the scenario of chronic disease diagnosis and treatment systems was set, and an evolutionary game model participated by four stakeholder groups including physical medical institutions, medical service platforms, intelligent medical device providers, and chronic disease patients, was established. Ten possible evolutionary stabilization strategies (ESSs) with their mandatory conditions were found based on Lyapunov's first method. Taking cardiovascular and cerebrovascular diseases, the top 1 prevalent chronic disease, as a specific case context, and resorting to the MATLAB simulation, it is confirmed that several dual ESSs and four unique ESS circumstances exist, respectively, and the evolution direction is determined by initial conditions, while the evolution speed is determined by the values of the conditions based on the quantitative relations of benefits, costs, etc. Accordingly, four governance mechanisms were proposed. By their adjustment, the conditions along with their values can be interfered, and then the chronic disease diagnosis and treatment systems can be guided toward the desired direction, that is, toward the direction of countermeasure against the pandemic, government guidance, global trends of medical industry development, social welfare, and lifestyle innovation. The dilemma of "Re-visits and drug renewal" actually reflects the uneven distribution problem of qualified medical resources and the poor impact resistance capability of social medical service systems under mass public emergency. Human lifestyle even the way of working all over the world will get a spiral upgrade after experiencing COVID-19, such as consumption, and meeting, while medical habits react not so rapidly, especially for mid or aged chronic disease patients. We believe that telemedicine empowered by intelligent medical devices can benefit them and will be a global trend, governments and the four key stakeholders should act according to the governance mechanisms suggested here simultaneously toward novel social medical ecosystems for the post-pandemic era.

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.

High-Precision and Low-Damage Microchannel Construction via Magnetically Assisted Laser-Induced Plasma Ablation for Micro-Thermoelectric Devices

Thermoelectric devices are developing toward high density and miniaturization with a large filling factor for new applications in chip thermal management and microenergy harvesting. Pulsed laser etching has become one of the most effective tools for the patterning construction of highly integrated micro-thermoelectric devices. However, the laser spot size and Gaussian laser energy distribution restrict the processing size and accuracy of microchannels. Moreover, the rapid temperature rise caused by laser energy injection would also raise serious problems such as element volatilization, cracks, and recast layers. Herein, a liquid-assisted nanosecond laser ablation technology with magnetically controlled plasma is proposed to etch microchannels on thermoelectric thick films. By evaluating the size and shape of microchannels, we theoretically investigated the influence of cavitation bubbles on the laser optical path and surface roughness in laser-induced plasma ablation. In addition, the energy criterion for high-precision ablation is revealed, and the effect of magnetic field on ablation threshold is explained by magnetic constraint on energy and kinetic properties of the laser-induced charged plasma plume. Finally, the high-precision and low-damage microchannels are achieved on Bi₂Te₃ thermoelectric thick films with a minimum line width of 19.12 μm and a small sidewall inclination degree of tan θ = 0.085. This work provides a promising alternative for the fabrication of high-density three-dimensional (3D) patterning in semiconductor microdevices.

Flexible biochemical sensors for point-of-care management of diseases: a review

Health problems have been widely concerned by all mankind. Real-time monitoring of disease-related biomarkers can feedback the physiological status of human body in time, which is very helpful to the diseases management of healthcare. However, conventional non-flexible/rigid biochemical sensors possess low fit and comfort with the human body, hence hindering the accurate and comfortable long-time health monitoring. Flexible and stretchable materials make it possible for sensors to be continuously attached to the human body with good fit, and more precise and higher quality results can be obtained. Thus, tremendous attention has been paid to flexible biochemical sensors in point-of-care (POC) for real-time monitoring the entire disease process. Here, recent progress on flexible biochemical sensors for management of various diseases, focusing on chronic and communicable diseases, is reviewed, and the detection principle and performance of these flexible biochemical sensors are discussed. Finally, some directions and challenges are proposed for further development of flexible biochemical sensors.

A Machine-Learning-Enhanced Simultaneous and Multimodal Sensor Based on Moist-Electric Powered Graphene Oxide

Simultaneous multimodal monitoring can greatly perceive intricately multiple stimuli, which is important for the understanding and development of a future human-machine fusion world. However, the integrated multisensor networks with cumbersome structure, huge power consumption, and complex preparation process have heavily restricted practical applications. Herein, a graphene oxide single-component multimodal sensor (GO-MS) is developed, which enables simultaneous monitoring of multiple environmental stimuli by a single unit with unique moist-electric self-power supply. This GO-MS can generate a sustainable moist-electric potential by spontaneously adsorbing water molecules in air, which has a characteristic response behavior when exposed to different stimuli. As a result, the simultaneous monitoring and decoupling of the changes of temperature, humidity, pressure, and light intensity are achieved by this single GO-MS with machine-learning (ML) assistance. Of practical importance, a moist-electric-powered human-machine interaction wristband based on GO-MS is constructed to monitor pulse signals, body temperature, and sweating in a multidimensional manner, as well as gestures and sign language commanding communication. This ML-empowered moist-electric GO-MS provides a new platform for the development of self-powered single-component multimodal sensors, showing great potential for applications in the fields of health detection, artificial electronic skin, and the Internet-of-Things.

Machine Learning-Assisted Multifunctional Environmental Sensing Based on a Piezoelectric Cantilever

Multifunctional environmental sensing is crucial for various applications in agriculture, pollution monitoring, and disease diagnosis. However, most of these sensing systems consist of multiple sensors, leading to significantly increased dimensions, energy consumption, and structural complexity. They also often suffer from signal interferences among multiple sensing elements. Herein, we report a multifunctional environmental sensor based on one single sensing element. A MoS₂ film was deposited on the surface of a piezoelectric microcantilever (300 × 1000 μm²) and used as both a sensing layer and top electrode to make full use of the changes in multiple properties of MoS₂ after its exposure to various environments. The proposed sensor has been demonstrated for humidity detection and achieved high resolution (0.3% RH), low hysteresis (5.6%), and fast response (1 s) and recovery (2.8 s). Based on the analysis of the magnitude spectra for transmission using machine learning algorithms, the sensor accurately quantifies temperatures and CO₂ concentrations in the interference of humidity with accuracies of 91.9 and 92.1%, respectively. Furthermore, the sensor has been successfully demonstrated for real-time detection of humidity and temperature or CO₂ concentrations for various applications, revealing its great potential in human-machine interactions and health monitoring of plants and human beings.

A flexible metal nano-mesh strain sensor with the characteristic of spontaneous functional recovery after fracture damage

Developing flexible sensors with high sensitivity, a wide sensing range, and good stability is a challenge. By replicating the anodic aluminum oxide (AAO) hole structure, we proposed new strain sensors with Pt nano-mesh films embedded in polydimethylsiloxane (PDMS) films. The nano-mesh strain sensor exhibited high sensitivity (a gauge factor of 4500) and a sensing range as high as 90%. The resistance remained almost completely unchanged after 1500 loading/unloading cycles of 15% strain, demonstrating the high repeatability and stability of the sensor. In addition, even if the nano-mesh experienced an open circuit by overstraining, the sensor can still measure strain within 45% after recovery. The capability of spontaneous functional recovery after fractural damage considerably extends its service life. Finally, the nano-mesh strain sensors were worn on the wrist and neck to monitor wrist movement and throat vibration, respectively. Signals corresponding to swallowing, throat clearing, and letter pronunciation were clearly distinguished from the peak value and signal patterns. These results indicate that the metal nano-mesh strain sensors have great potential for applications in wearable devices, electronic skin, and flexible robotics.

Emerging Development of Auto-Charging Sensors for Respiration Monitoring

In recent years, the development of biomedical monitoring systems, including respiration monitoring systems, has been accelerated. Wearable and implantable medical devices are becoming increasingly important in the diagnosis and management of disease and illness. Respiration can be monitored using a variety of biosensors and systems. Auto-charged sensors have a number of advantages, including low cost, ease of preparation, design flexibility, and a wide range of applications. It is possible to use the auto-charged sensors to directly convert mechanical energy from the airflow into electricity. The ability to monitor and diagnose one's own health is a major goal of auto-charged sensors and systems. Respiratory disease model output signals have not been thoroughly investigated and clearly understood. As a result, figuring out their exact interrelationship is a difficult and important research question. This review summarized recent developments in auto-charged respiratory sensors and systems in terms of their device principle, output property, detecting index, and so on. Researchers with an interest in auto-charged sensors can use the information presented here to better understand the difficulties and opportunities that lie ahead.

Highly Adhesive, Stretchable, and Antifreezing Hydrogel with Excellent Mechanical Properties for Sensitive Motion Sensors and Temperature-/Humidity-Driven Actuators

Conductive hydrogels as flexible wearable devices have attracted considerable attention due to their mechanical flexibility and intelligent sensing. How to endow more and better performance, such as high self-adhesion, stretchability, and wide application temperature range for traditional hydrogels and flexible sensors is a challenge. Herein, a stretchable, self-adhesive, and antifreezing conductive hydrogel with multiple networks and excellent mechanical properties was prepared by a two-step method for its application in sensitive motion sensors and temperature-/humidity-driven actuators. First, quaternary chitosan (QCS) was introduced into the network of an acrylamide (AM) and 1-vinyl imidazole (VI) copolymer initiated by UV-photoinitiated radical polymerization. Then, the double-network hydrogel was immersed in a FeCl₃ solution to fabricate the P(AAm-co-VI)/QCS-Fe³⁺ ionic hydrogel with multiple physical networks. The properties of the hydrogel were controllable and adjustable. The toughness of the ionic hydrogel could reach up to 654.4 kJ/m³, the fracture strength could reach 253.1 kPa, and the compressive strength reached 8.4 MPa at an 80% compression strain. The multiple physical networks improved the mechanical properties and the quick resilience of the hydrogel. A large amount of FeCl₃ in the network greatly enhanced the ionic conductivity. Meanwhile, hydrogen bonds with water molecules inhibit the formation of ice crystals between zero water molecules and enhance the freezing resistance of P(Aam-co-VI)/QCS hydrogels. The active group on the QCS chain provided adhesiveness to various substrates for hydrogels. The P(AAm-co-VI)/QCS-Fe³⁺ hydrogel-based sensor showed high sensitivity, which can detect human movement and pulse, with a gauge factor of 2.37. Finally, due to the different dehydration rates of the P(AAm-co-VI)/QCS-Fe³⁺ and P(AAm-co-VI)/QCS hydrogel, a double-layer temperature/humidity-driven actuator was fabricated, expanding the application of conductive hydrogels.

Strain-ultrasensitive surface wrinkles for visual optical sensors

Wearable tactile sensors have found widespread applications in human health monitoring, motion monitoring, human-machine interactions, and artificial prostheses. Herein, we demonstrate a new and feasible strategy for wearable optical sensors based on surface wrinkles that are ultrasensitive to strain using a bilayer wrinkling system, in which the relevance between strain and the optical signal can be founded on surface wrinkles. The strain (S⃑(ε, θ_S)), the wrinkled topography (W⃑(A, θ_W)), and the reflected optical signal (O⃑(δ, θ_O)) are correlated with each other, allowing simultaneous measurement of the strain magnitude and direction due to the vector property of optical signals. In addition, interactively visualized detection of slight strain has been achieved by a conspicuous structural color change, successfully amplifying the strain signal owing to the ultra-sensitivity of wrinkles and the nonlinearity of the optical signal. The sensor also exhibits electrical safety and immunity to electromagnetic interference and thus may find potential applications in detecting various complex slight strains, such as subtle human motion or object deformation.

Multifunctional Flexible Humidity Sensor Systems Towards Noncontact Wearable Electronics

In the past decade, the global industry and research attentions on intelligent skin-like electronics have boosted their applications in diverse fields including human healthcare, Internet of Things, human-machine interfaces, artificial intelligence and soft robotics. Among them, flexible humidity sensors play a vital role in noncontact measurements relying on the unique property of rapid response to humidity change. This work presents an overview of recent advances in flexible humidity sensors using various active functional materials for contactless monitoring. Four categories of humidity sensors are highlighted based on resistive, capacitive, impedance-type and voltage-type working mechanisms. Furthermore, typical strategies including chemical doping, structural design and Joule heating are introduced to enhance the performance of humidity sensors. Drawing on the noncontact perception capability, human/plant healthcare management, human-machine interactions as well as integrated humidity sensor-based feedback systems are presented. The burgeoning innovations in this research field will benefit human society, especially during the COVID-19 epidemic, where cross-infection should be averted and contactless sensation is highly desired.

A Multitasking Flexible Sensor via Reservoir Computing

Natural disasters are reported globally, and one source of severe damage to cities is flooding caused by locally heavy rain. Sharing of local weather information can save lives. However, it is difficult to collect local weather information in real-time because such data collection requires bulky, expensive sensors. For local, real-time monitoring of heavy rain and wind, a sensor system should be simple and low-cost so that it can be attached to a variety of surfaces, including roofs, vehicles, and umbrellas. To develop simple, low-cost multitasking sensors located on nonplanar surfaces, a flexible rain sensor to monitor waterdrop volume and wind velocity is devised. To monitor both simultaneously, a laser-induced graphene-based superhydrophobic conductive film is introduced. Using the superhydrophobic surface, water dynamics are measured when waterdrops collide with the sensor surface, and obtained time-series data are processed using "reservoir computing" to extract the volume and velocity from a single sensor as multitasking electronics. As a proof-of-concept, it is shown that the sensor measures continuous, long-term volume and wind-change dynamics. The results demonstrate feasibility of multitasking electronics with reservoir computing to reduce sensor integration complexity with low power consumption for both sensor and signal processing.

Developing Wound Moisture Sensors: Opportunities and Challenges for Laser-Induced Graphene-Based Materials

Recent advances in polymer composites have led to new, multifunctional wound dressings that can greatly improve healing processes, but assessing the moisture status of the underlying wound site still requires frequent visual inspection. Moisture is a key mediator in tissue regeneration and it has long been recognised that there is an opportunity for smart systems to provide quantitative information such that dressing selection can be optimised and nursing time prioritised. Composite technologies have a rich history in the development of moisture/humidity sensors but the challenges presented within the clinical context have been considerable. This review aims to train a spotlight on existing barriers and highlight how laser-induced graphene could lead to emerging material design strategies that could allow clinically acceptable systems to emerge.

Self-Stretchable Fiber Liquid Sensors Made with Bacterial Cellulose/Carbon Nanotubes for Smart Diapers

Liquid sensors for detecting water and body fluids are crucial in daily water usage and health monitoring, but it is challenging to combine sensing performance with high tensile deformation and multifunctional applications. Here, a substrate-free, self-stretchable bacterial cellulose (BC)/carbon nanotube (CNT) helical fiber liquid sensor was prepared by the solution spinning and coiling process using BC as the water-sensitive matrix and CNTs as the active sensing materials. The BC/CNT (BCT) fiber sensor has a high stretch ratio of more than 1000% and a rapid response for a current change rate of 10⁴% within 1 s, which is almost unaffected under washing and various stretching or knotting deformations. By combination of the BCT fiber, we can design smart diapers or water level detectors, which rapidly monitor the status of smart diapers or water level, and the monitoring result can be transferred on time through an alarm device or smartphone. In short, the scalable and continuous preparation of the self-stretchable BCT helical fiber will provide a capacious platform for the development of a wearable sensor applied in daily life (such as smart diapers, water level detection, etc.).

A wearable, flexible sensor for real-time, home monitoring of sleep apnea

A flexible sensor that can be attached to the body to collect vital data wirelessly enables real-time human healthcare management. One potential application for home-use healthcare devices is monitoring of sleep conditions to diagnose sleep apnea syndrome. Such data are not readily gathered using conventional tools, owing to the bulk and cost of instrumentation. In order to monitor respiration at home, it is necessary to improve sensing performance and long-term stability of the sensors without sacrificing wearability and comfortability. To build a platform for wireless home-use respiration monitoring, this study develops a mask-borne flexible humidity sensor using ZnIn₂S₄ nanosheets as a humidity-sensitive material with high sensitivity and stability for more than 150 h. As proof-of-concept, long-term wireless respiration monitoring is demonstrated during sleep to identify symptoms of sleep apnea in wearers.

Flexible Electronics and Devices as Human-Machine Interfaces for Medical Robotics

Medical robots are invaluable players in non-pharmaceutical treatment of disabilities. Particularly, using prosthetic and rehabilitation devices with human-machine interfaces can greatly improve the quality of life for impaired patients. In recent years, flexible electronic interfaces and soft robotics have attracted tremendous attention in this field due to their high biocompatibility, functionality, conformability, and low-cost. Flexible human-machine interfaces on soft robotics will make a promising alternative to conventional rigid devices, which can potentially revolutionize the paradigm and future direction of medical robotics in terms of rehabilitation feedback and user experience. In this review, the fundamental components of the materials, structures, and mechanisms in flexible human-machine interfaces are summarized by recent and renowned applications in five primary areas: physical and chemical sensing, physiological recording, information processing and communication, soft robotic actuation, and feedback stimulation. This review further concludes by discussing the outlook and current challenges of these technologies as a human-machine interface in medical robotics.

Reconfigurable, Stretchable Strain Sensor with the Localized Controlling of Substrate Modulus by Two-Phase Liquid Metal Cells

Strain modulation based on the heterogeneous design of soft substrates is an effective method to improve the sensitivity of stretchable resistive strain sensors. In this study, a novel design for reconfigurable strain modulation in the soft substrate with two-phase liquid cells is proposed. The modulatory strain distribution induced by the reversible phase transition of the liquid metal provides reconfigurable strain sensing capabilities with multiple combinations of operating range and sensitivity. The effectiveness of our strategy is validated by theoretical simulations and experiments on a hybrid carbonous film-based resistive strain sensor. The strain sensor can be gradually switched between a highly sensitive one and a wide-range one by selectively controlling the phases of liquid metal in the cell array with a external heating source. The relative change of sensitivity and operating range reaches a maximum of 59% and 44%, respectively. This reversible heterogeneous design shows great potential to facilitate the fabrication of strain sensors and might play a promising role in the future applications of stretchable strain sensors.

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.

Self-Healing, Self-Adhesive and Stable Organohydrogel-Based Stretchable Oxygen Sensor with High Performance at Room Temperature

With the advent of the 5G era and the rise of the Internet of Things, various sensors have received unprecedented attention, especially wearable and stretchable sensors in the healthcare field. Here, a stretchable, self-healable, self-adhesive, and room-temperature oxygen sensor with excellent repeatability, a full concentration detection range (0-100%), low theoretical limit of detection (5.7 ppm), high sensitivity (0.2%/ppm), good linearity, excellent temperature, and humidity tolerances is fabricated by using polyacrylamide-chitosan (PAM-CS) double network (DN) organohydrogel as a novel transducing material. The PAM-CS DN organohydrogel is transformed from the PAM-CS composite hydrogel using a facile soaking and solvent replacement strategy. Compared with the pristine hydrogel, the DN organohydrogel displays greatly enhanced mechanical strength, moisture retention, freezing resistance, and sensitivity to oxygen. Notably, applying the tensile strain improves both the sensitivity and response speed of the organohydrogel-based oxygen sensor. Furthermore, the response to the same concentration of oxygen before and after self-healing is basically the same. Importantly, we propose an electrochemical reaction mechanism to explain the positive current shift of the oxygen sensor and corroborate this sensing mechanism through rationally designed experiments. The organohydrogel oxygen sensor is used to monitor human respiration in real-time, verifying the feasibility of its practical application. This work provides ideas for fabricating more stretchable, self-healable, self-adhesive, and high-performance gas sensors using ion-conducting organohydrogels.

Laser-Induced Graphene Based Flexible Electronic Devices

Since it was reported in 2014, laser-induced graphene (LIG) has received growing attention for its fast speed, non-mask, and low-cost customizable preparation, and has shown its potential in the fields of wearable electronics and biological sensors that require high flexibility and versatility. Laser-induced graphene has been successfully prepared on various substrates with contents from various carbon sources, e.g., from organic films, plants, textiles, and papers. This paper reviews the recent progress on the state-of-the-art preparations and applications of LIG including mechanical sensors, temperature and humidity sensors, electrochemical sensors, electrophysiological sensors, heaters, and actuators. The achievements of LIG based devices for detecting diverse bio-signal, serving as monitoring human motions, energy storage, and heaters are highlighted here, referring to the advantages of LIG in flexible designability, excellent electrical conductivity, and diverse choice of substrates. Finally, we provide some perspectives on the remaining challenges and opportunities of LIG.

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.

Flexible photoelectrochemical biosensor for ultrasensitive microRNA detection based on concatenated multiplex signal amplification

Precise microRNA (miRNA) analysis is significant importance for early disease diagnosis. Herein, a novel flexible photoelectrochemical (PEC) biosensor for miRNA determination was developed by employing CdS NPs-modified carbon cloth (CC) on polyimide (PI) film as photoelectric material to provide the PEC responses and an efficient four-stage reaction system as the target recognition and signal amplification unit to improve the analytical performance. In this PEC biosensor, the presence of target miR-21 would trigger the catalytic hairpin assembly (CHA) and the following hybridization chain reaction (HCR) to produce a long dsDNA labeled with numerous biotins, which would further capture a large amount of alkaline phosphatase (ALP) for catalyzing the generation of ascorbic acid (AA). As an efficient electron donor, AA could be oxidized by the photoelectrode, which would initiate a redox cycling amplification process to regenerate AA, resulting in the enhancement of the photocurrent response. Benefitting from the synergistic nucleic acid-based, enzyme catalytic, and chemical signal amplification strategies, the proposed biosensing strategy enabled ultrasensitive miRNA determination. As expected, the PEC biosensor performed satisfactory analytical performances with a linear range of 1 fM to 1 nM and the detection limit down to 0.41 fM. Furthermore, the PEC biosensing strategy exhibited recommendable selectivity, stability, flexibility, and practical applicability. Therefore, this sensing platform provides promising potential for application in bioassay and early diagnosis of disease.

Constructing a Segregated Magnetic Graphene Network in Rubber Composites for Integrating Electromagnetic Interference Shielding Stability and Multi-Sensing Performance

A flexible, wearable electronic device composed of magnetic iron oxide (Fe₃O₄)/reduced graphene oxide/natural rubber (MGNR) composites with a segregated network was prepared by electrostatic self-assembly, latex mixing, and in situ reduction. The segregated network offers the composites higher electrical conductivity and more reliable sensing properties. Moreover, the addi-tion of Fe₃O₄ provides the composites with better electromagnetic interference shielding effectiveness (EMI SE). The EMI shielding property of MGNR composites is more stable under tensile deformation and long-term cycling conditions and has a higher sensitivity to stretch strain compared with the same structure made from reduced graphene oxide/natural rubber (GNR) composites. The EMI SE value of MGNR composites reduces by no more than 2.9% under different tensile permanent deformation, cyclic stretching, and cyclic bending conditions, while that of GNR composites reduces by approximately 16% in the worst case. Additionally, the MGNR composites have a better sensing performance and can maintain stable signals, even in the case of cyclic stretching with a very small strain (0.05%). Furthermore, they can steadily monitor the changes in resistance signals in various human motions such as finger bending, wrist bending, speaking, smiling, and blinking, indicating that the MGNR composites can be used in future wearable electronic flexibility devices.