A fully integrated, standalone stretchable device platform with in-sensor adaptive machine learning for rehabilitation

Deep Learning-Assisted 3D Pressure Sensors for Control of Unmanned Aerial Vehicles

Accurately and reliably detecting and recognizing human body movements in real time, relaying appropriate commands to the machine, have substantial implications for virtual reality, remote control, and robotics applications. Nonetheless, most contemporary wearable analysis and control systems attain action recognition by setting sensor thresholds. In routine usage, the stringent trigger conditions facilitate inadvertent contact, resulting in a poorer user experience. Here, we have created a wearable intelligent gesture recognition control system utilizing a multilayer microstructure composite thin film piezoresistive sensing array and deep learning techniques. The system exhibits ultrahigh sensitivity (ranging from 0-6 kPa to 412.2 kPa-1) and rapid response times (loading at 40 ms, recovery at 30 ms). The detected gestures are classified and recognized via a convolutional neural network, achieving a recognition accuracy of 97.5%. Ultimately, the altitude control of an unmanned aerial vehicle is accomplished through wireless signal transmission and reception. To achieve the visualization of the complete gesture-controlled flight process, we developed an intuitive user interface for the real-time display of flight altitude and video surveillance. The implementation of this recognition system introduces a novel control mechanism for human-machine interaction, expands the applications of robotic technology, and offers innovative concepts and practical pathways for virtual reality.

High-sensitivity flexible electrochemical sensor for real-time multi-analyte sweat analysis

Flexible sweat sensors play a crucial role in health monitoring and disease prevention by enabling real-time, non-invasive assessment of human physiological conditions. Sweat contains a variety of biomarkers, offering valuable insights into an individual's health status. In this study, we developed an advanced flexible electrochemical sensor featuring reduced graphene oxide (rGO)-based electrodes, modified with a composite material comprising nitrogen and sulfur co-doped holey graphene (HG) and MXene, with in-situ-grown TiO2 nanoparticles on the MXene. The sensor design leverages the synergistic properties of its components: MXene provides a conductive scaffold, TiO2 enhances electrocatalytic activity, and the porous HG network facilitates efficient ion and electron transfer, with doping increasing the number of active sites. This configuration enables sensitive and simultaneous detection of ascorbic acid (AA), uric acid (UA), and dopamine (DA). Additionally, a potassium-selective (K⁺) film applied to rGO-enhanced electrodes supports concurrent detection of K⁺ in sweat. The sensor array demonstrates a broad detection range, low detection limits, and high sensitivity, while maintaining mechanical flexibility, anti-interference capabilities, and repeatability. Validated through in-situ sweat biomarker detection during exercise, the sensor effectively tracked fluctuations in K⁺, AA, and UA levels in a volunteer. This practical application underscores the sensor's potential for continuous health monitoring, and early disease detection, establishing it as a promising tool in modern healthcare.

Machine Learning-Enabled Emotion Recognition by Multisource Throat Signals

Emotion monitoring plays a crucial role in mental health management. However, traditional methods of emotion recognition predominantly rely on subjective questionnaires or facial expression analyses, which are often inadequate for continuous and highly accurate monitoring. In this study, we propose a high-precision, fine-grained emotion recognition system based on multisource throat physiological signals. The system collects signals through optimized flexible multiporous skin sensors and analyzes them using machine learning models capable of efficiently capturing complex feature interactions. First, we adopt a two-step cross-linking strategy to modulate the porous structure of the sensitive layer to enable accurate detection of the diverse and weak physiological signals in the throat. By extracting four-dimensional features from the input of 7025 samples, the platform based on the Light Gradient Boosting Machine (LightGBM) efficiently captures their nonlinear interactions, ultimately achieving precise classification of five emotional states (relaxation, surprise, disgust, fear, and neutral) with an accuracy of 98.9%. Further validation on an independent data set reveals an average emotion recognition accuracy of 99.3%, demonstrating the system's robustness and reliability in real-world applications. This work provides a viable technological solution for real-time and continuous emotion monitoring, offering significant potential in mental health management and related fields.

Piezotronic Sensor for Bimodal Monitoring of Achilles Tendon Behavior

Bimodal pressure sensors capable of simultaneously detecting static and dynamic forces are essential to medical detection and bio-robotics. However, conventional pressure sensors typically integrate multiple operating mechanisms to achieve bimodal detection, leading to complex device architectures and challenges in signal decoupling. In this work, we address these limitations by leveraging the unique piezotronic effect of Y-ion-doped ZnO to develop a bimodal piezotronic sensor (BPS) with a simplified structure and enhanced sensitivity. Through a combination of finite element simulations and experimental validation, we demonstrate that the BPS can effectively monitor both dynamic and static forces, achieving an on/off ratio of 1029, a gauge factor of 23,439 and a static force response duration of up to 600 s, significantly outperforming the performance of conventional piezoelectric sensors. As a proof-of-concept, the BPS demonstrates the continuous monitoring of Achilles tendon behavior under mixed dynamic and static loading conditions. Aided by deep learning algorithms, the system achieves 96% accuracy in identifying Achilles tendon movement patterns, thus enabling warnings for dangerous movements. This work provides a viable strategy for bimodal force monitoring, highlighting its potential in wearable electronics.

Motion-unrestricted dynamic electrocardiogram system utilizing imperceptible electronics

Electrocardiogram (ECG) plays a vital role in the prevention, diagnosis, and prognosis of cardiovascular diseases (CVDs). However, the lack of a user-friendly and accurate long-term dynamic electrocardiogram (DCG) device in motion has made it challenging to perform many daily cardiovascular risk screenings and assessments, such as sudden cardiac arrest, resulting in additional economic burdens on society. Here, we present a motion-unrestricted dynamic electrocardiogram (MU-DCG) system, which employs skin-conformal, imperceptible electronics for long-term, comfortable, and accurate 12-lead DCG monitoring. To facilitate assembly for use on the skin, the MU-DCG system features a pressure-activated flexible skin socket for stably soft-connecting the on-skin soft module and the off-skin stiff module during dynamic movements. Crucially, blinded cardiologist evaluations confirm minimal motion artifacts in MU-DCG-acquired ECG signals. Our results demonstrate that the MU-DCG system, with large-area, ultra-thin on-skin electrodes/leads, and an off-skin module, accomplishes anti-motion interference acquisition and in-situ analysis while retaining wearing imperceptibility.

Epidermal electronic-tattoo for plant immune response monitoring

Real-time monitoring of plant immune responses is crucial for understanding plant immunity and mitigating economic losses from pathogen and pest attacks. However, current methods relying on molecular-level assessment are destructive and time-consuming. Here, we report an ultrathin, substrate-free, and highly conductive electronic tattoo (e-tattoo) designed for plants, enabling immune response monitoring through non-invasive electrical impedance spectroscopy (EIS). The e-tattoo's biocompatibility, high conductivity, and sub-100 nm thickness allow it to conform to leaf tissue morphology and provide robust impedance data. We demonstrate continuous EIS analysis of live transgenic Arabidopsis thaliana plants for over 24 h, capturing the onset of NLR-mediated acute immune responses within three hours post-induction, prior to visible symptoms. RNA-seq and tissue ion leakage tests validate that EIS data accurately represent the physiological and molecular changes associated with immune activation. This non-invasive tissue-assessment technology has the potential to enhance our comprehension of immune activation mechanisms in plants and paves the way for real-time monitoring for plant health management.

Layer-by-Layer Self-Assembled Honeycomb Structure Flexible Pressure Sensor Array for Gait Analysis and Motion Posture Recognition with the Assistance of the ResNet-50 Neural Network

With the rapid emergence of flexible electronics, flexible pressure sensors are of importance in various fields. In this study, a dopamine-modified melamine sponge (MS) was used to prepare a honeycomb structure of carbon black (CB)/MXene-silicone rubber (SR)@MS flexible pressure sensor (CMSM) through layer-by-layer self-assembly technology. Using SR as a binder to construct the honeycomb structure not only improves the mechanical properties of the sensor but also provides more attachment sites for CB/MXene, enhancing the stability of the conductive network. The honeycomb structure CMSM flexible pressure sensor exhibits high sensitivity (7.44 kPa-1), a wide detection range (0-240 kPa), short response/recovery times (150 ms/180 ms), and exhibits excellent stability. In addition, a flexible smart insole has been developed based on a 6-unit CMSM sensor array, achieving plantar pressure detection. By combination of the ResNet-50 neural network algorithm with plantar pressure data under different postures, the recognition of 16 types of human motion postures has been achieved, with an accuracy rate of 90.63%. This study proposes a flexible sponge pressure sensor with excellent mechanical performance and sensing capabilities, providing new ideas and references for the design of flexible wearable sensor devices.

Design, Fabrication, and Application of Large-Area Flexible Pressure and Strain Sensor Arrays: A Review

The rapid development of flexible sensor technology has made flexible sensor arrays a key research area in various applications due to their exceptional flexibility, wearability, and large-area-sensing capabilities. These arrays can precisely monitor physical parameters like pressure and strain in complex environments, making them highly beneficial for sectors such as smart wearables, robotic tactile sensing, health monitoring, and flexible electronics. This paper reviews the fabrication processes, operational principles, and common materials used in flexible sensors, explores the application of different materials, and outlines two conventional preparation methods. It also presents real-world examples of large-area pressure and strain sensor arrays. Fabrication techniques include 3D printing, screen printing, laser etching, magnetron sputtering, and molding, each influencing sensor performance in different ways. Flexible sensors typically operate based on resistive and capacitive mechanisms, with their structural designs (e.g., sandwich and fork-finger) affecting integration, recovery, and processing complexity. The careful selection of materials-especially substrates, electrodes, and sensing materials-is crucial for sensor efficacy. Despite significant progress in design and application, challenges remain, particularly in mass production, wireless integration, real-time data processing, and long-term stability. To improve mass production feasibility, optimizing fabrication processes, reducing material costs, and incorporating automated production lines are essential for scalability and defect reduction. For wireless integration, enhancing energy efficiency through low-power communication protocols and addressing signal interference and stability are critical for seamless operation. Real-time data processing requires innovative solutions such as edge computing and machine learning algorithms, ensuring low-latency, high-accuracy data interpretation while preserving the flexibility of sensor arrays. Finally, ensuring long-term stability and environmental adaptability demands new materials and protective coatings to withstand harsh conditions. Ongoing research and development are crucial to overcoming these challenges, ensuring that flexible sensor arrays meet the needs of diverse applications while remaining cost-effective and reliable.

Machine learning-assisted wearable sensing systems for speech recognition and interaction

The human voice stands out for its rich information transmission capabilities. However, voice communication is susceptible to interference from noisy environments and obstacles. Here, we propose a wearable wireless flexible skin-attached acoustic sensor (SAAS) capable of capturing the vibrations of vocal organs and skin movements, thereby enabling voice recognition and human-machine interaction (HMI) in harsh acoustic environments. This system utilizes a piezoelectric micromachined ultrasonic transducers (PMUT), which feature high sensitivity (-198 dB), wide bandwidth (10 Hz-20 kHz), and excellent flatness (±0.5 dB). Flexible packaging enhances comfort and adaptability during wear, while integration with the Residual Network (ResNet) architecture significantly improves the classification of laryngeal speech features, achieving an accuracy exceeding 96%. Furthermore, we also demonstrated SAAS's data collection and intelligent classification capabilities in multiple HMI scenarios. Finally, the speech recognition system was able to recognize everyday sentences spoken by participants with an accuracy of 99.8% through a deep learning model. With advantages including a simple fabrication process, stable performance, easy integration, and low cost, SAAS presents a compelling solution for applications in voice control, HMI, and wearable electronics.

Smart Vascular Grafts with Integrated Flow Biosensors for Hemodynamic Real-Time Monitoring and Vascular Healthcare

Real-time monitoring of hemodynamics is crucial for diagnosing disorders within implanted vascular grafts and facilitating timely treatment. Integrating vascular grafts with advanced flexible electronics offers a promising approach to developing smart vascular grafts (SVGs) capable of continuous hemodynamic monitoring. However, most existing SVG devices encounter significant challenges in practical applications, particularly regarding biomechanical compatibility and the effective evaluation of vascular status. Here, we present a state-of-the-art SVG device seamlessly integrated with flow biosensors constructed by encapsulating patterned porous graphene within biocompatible polymers. The innovative use of porous graphene imparts the SVG with exceptional mechanical sensing performance, featuring a low strain detection limit of 0.0034% and dynamic stability exceeding 32,400 cycles, thus enabling precise hemodynamic perception. This high sensitivity allows the SVG to accurately diagnose vascular disorders, such as blockage degree and position, by collecting hemodynamic data from an artificial artery model. In vitro thrombi (blood clot) diagnostics, treatment simulation experiments, and in vivo tests using a rabbit model strongly validate the SVG's outstanding and reliable performance in vascular healthcare. We have also developed a stand-alone and wireless system, demonstrating its capability for remote monitoring and managing vascular health. Our pioneering SVG system showcases great potential in vascular healthcare for precise hemodynamic monitoring of disorders, timely diagnostics, and even drug screening.

Comparative Analysis of Machine Learning Algorithms Used for Translating Aptamer-Antigen Binding Kinetic Profiles to Diagnostic Decisions

Current approaches for classifying biosensor data in diagnostics rely on fixed decision thresholds based on receiver operating characteristic (ROC) curves, which can be limited in accuracy for complex and variable signals. To address these limitations, we developed a framework that facilitates the application of machine learning (ML) to diagnostic data for the binary classification of clinical samples, when using real-time electrochemical measurements. The framework was applied to a real-time multimeric aptamer assay (RT-MAp) that captures single-frequency (12.6 Hz) impedance data during the binding of viral protein targets to trimeric aptamers. The impedance data collected from 172 COVID-19 saliva samples were processed through multiple nonlinear regression models to extract nine key features from the transient signals. These features were then used to train three supervised ML algorithms─support vector machine (SVM), artificial neural network (ANN), and random forest (RF)─using a 75:25 training-testing ratio. Traditional ROC-based classification achieved an accuracy of 83.6%, while ML-based models significantly improved performance, with SVM, ANN, and RF achieving accuracies of 86.0%, 100%, and 100%, respectively. The ANN model demonstrated superior performance in handling complex and high-variance biosensor data, providing a robust and scalable solution for improving diagnostic accuracy in point-of-care settings.

Ion Gel Pressure Sensor with High Sensitivity and a Wide Linear Range Enabled by Magnetically Induced Gradient Microstructures

In the field of intelligent sensing, a major challenge pertains to the development of capacitive pressure sensors that can precisely detect minute pressure changes and simultaneously exhibit a wide linear range and high sensitivity. This paper develops a novel capacitive pressure sensor inspired by the gradient microstructure of tree frog toe pads, which is suitable for various applications including texture recognition, motion monitoring, and object grasping recognition. The sensor employs magnetic induction technology to precisely control the gradient microstructure morphology and combines it with ionic gel and conductive nanomaterials. These features enable it to not only detect minute pressures as low as 0.5 Pa but also maintain a high sensitivity of 1.51 kPa-1 and excellent linear response characteristics across a wide pressure range of up to 93.5 kPa. It can accurately capture pulse beats and motion signals, making it suitable for use in human health monitoring. Furthermore, by utilizing the deep learning algorithms, it achieves a 97.39% object recognition accuracy rate in flexible intelligent sorting systems. This work provides a new solution in application fields such as health monitoring and intelligent logistics sorting.

Wearable Photonic Artificial Throat for Silent Communication and Speech Recognition

Advocating for the voices of the disabled, particularly through wearable artificial throats, has garnered significant attention recently. Such devices necessitate sensors with stretchability, high sensitivity, and excellent skin conformability. In this study, an intelligent photonic artificial throat has been developed. It features a sandwich-structured optical fiber sensor encapsulated in Dragon Skin 20, which has an elastic modulus similar to human tissue and is integrated with sensitivity-enhancing rings and fabric for enhanced wearability. With ultrafast response (response time: 10 ms, recovery time: 32 ms) and high sensitivity (1.92 μW/mN), it detects throat area vibrations and muscle contractions, accurately identifying tones in Mandarin, vowels, words, and sentences in English, achieving accurate bilingual detection. It also distinguishes animal sounds (horse neighing and cuckoo's call) and pop songs when mounted on speakers. Furthermore, the artificial throat can accurately detect subtle movements of the head and neck, and by combining nodding actions with the MORSE code, silent communication between individuals has been successfully achieved. Integrated with an advanced artificial intelligence (AI) algorithm, it recognizes tones (97.50%), vowel letters (97.00%), common words (98.00%) and sentences (96.52%), opening prospects for biomedical applications, language education, speech recognition, motion monitoring, and more.

Emerging Wearable Acoustic Sensing Technologies

Sound signals not only serve as the primary communication medium but also find application in fields such as medical diagnosis and fault detection. With public healthcare resources increasingly under pressure, and challenges faced by disabled individuals on a daily basis, solutions that facilitate low-cost private healthcare hold considerable promise. Acoustic methods have been widely studied because of their lower technical complexity compared to other medical solutions, as well as the high safety threshold of the human body to acoustic energy. Furthermore, with the recent development of artificial intelligence technology applied to speech recognition, speech recognition devices, and systems capable of assisting disabled individuals in interacting with scenes are constantly being updated. This review meticulously summarizes the sensing mechanisms, materials, structural design, and multidisciplinary applications of wearable acoustic devices applied to human health and human-computer interaction. Further, the advantages and disadvantages of the different approaches used in flexible acoustic devices in various fields are examined. Finally, the current challenges and a roadmap for future research are analyzed based on existing research progress to achieve more comprehensive and personalized healthcare.

Bionic Recognition Technologies Inspired by Biological Mechanosensory Systems

Mechanical information is a medium for perceptual interaction and health monitoring of organisms or intelligent mechanical equipment, including force, vibration, sound, and flow. Researchers are increasingly deploying mechanical information recognition technologies (MIRT) that integrate information acquisition, pre-processing, and processing functions and are expected to enable advanced applications. However, this also poses significant challenges to information acquisition performance and information processing efficiency. The novel and exciting mechanosensory systems of organisms in nature have inspired us to develop superior mechanical information bionic recognition technologies (MIBRT) based on novel bionic materials, structures, and devices to address these challenges. Herein, first bionic strategies for information pre-processing are presented and their importance for high-performance information acquisition is highlighted. Subsequently, design strategies and considerations for high-performance sensors inspired by mechanoreceptors of organisms are described. Then, the design concepts of the neuromorphic devices are summarized in order to replicate the information processing functions of a biological nervous system. Additionally, the ability of MIBRT is investigated to recognize basic mechanical information. Furthermore, further potential applications of MIBRT in intelligent robots, healthcare, and virtual reality are explored with a view to solve a range of complex tasks. Finally, potential future challenges and opportunities for MIBRT are identified from multiple perspectives.

Thermoelectric porous laser-induced graphene-based strain-temperature decoupling and self-powered sensing

Despite rapid developments of wearable self-powered sensors, it is still elusive to decouple the simultaneously applied multiple input signals. Herein, we report the design and demonstration of stretchable thermoelectric porous graphene foam-based materials via facile laser scribing for self-powered decoupled strain and temperature sensing. The resulting sensor can accurately detect temperature with a resolution of 0.5°C and strain with a gauge factor of 1401.5. The design of the nanocomposites also explores the synergistic effect between the porous graphene and thermoelectric components to greatly enhance the Seebeck coefficient by almost four times (from 9.703 to 37.33 μV/°C). Combined with the stretchability of 45%, the self-powered sensor platform allows for early fire detection in remote settings and accurate and decoupled monitoring of temperature and strain during the wound healing process in situ. The design concepts from this study could also be leveraged to prepare multimodal sensors with decoupled sensing capability for accurate multi-parameter detection towards health monitoring.

Multimodal Flexible Sensor for the Detection of Pressing-Bending-Twisting Mechanical Deformations

Flexible sensors are increasingly significant in applications such as smart wearables and human-computer interactions. However, typical flexible sensors are spatially limited and can generally detect only one deformation mode. This study presents a novel multimodal flexible sensor that combines three sensing units: optoelectronics, ionic liquids, and conductive fabrics. It employs a sophisticated superposition and combination of the three sensing methods to achieve up to eight mechanical deformations, including pressing, bending, twisting, and combinations thereof, all within a very small sensor space. This sensor has excellent detection performance, high sensitivity (optoelectronics 4.312, ionic liquid 8.186, conductive fabric 2.438), a wide measurement range (pressing 0-75 kPa, bending 0-90°, and twisting 0-180°), and good consistency and repeatability. To address the signal coupling problem in multimode sensors, a deep learning method based on the Transformer is combined to provide precise decoupling of multimode signals and high-precision characterization of each mechanical deformation. Finally, the wrist joint experiments demonstrate the sensor's versatile uses in human-computer interaction.

Multimodal deep ensemble classification system with wearable vibration sensor for detecting throat-related events

Dysphagia, a swallowing disorder, requires continuous monitoring of throat-related events to obtain comprehensive insights into the patient's pharyngeal and laryngeal functions. However, conventional assessments were performed by medical professionals in clinical settings, limiting persistent monitoring. We demonstrate feasibility of a ubiquitous monitoring system for autonomously detecting throat-related events utilizing a soft skin-attachable throat vibration sensor (STVS). The STVS accurately records throat vibrations without interference from surrounding noise, enabling measurement of subtle sounds such as swallowing. Out of the continuous data stream, we automatically classify events of interest using an ensemble-based deep learning model. The proposed model integrates multiple deep neural networks based on multi-modal acoustic features of throat-related events to enhance robustness and accuracy of classification. The performance of our model outperforms previous studies with a classification accuracy of 95.96%. These results show the potential of wearable solutions for improving dysphagia management and patient outcomes outside of clinical environments.

Traditional Chinese Medicine (TCM)-Inspired Fully Printed Soft Pressure Sensor Array with Self-Adaptive Pressurization for Highly Reliable Individualized Long-Term Pulse Diagnostics

Reliable, non-invasive, continuous monitoring of pulse and blood pressure is essential for the prevention and diagnosis of cardiovascular diseases. However, the pulse wave varies drastically among individuals or even over time in the same individual, presenting significant challenges for the existing pulse sensing systems. Inspired by pulse diagnosis methods in traditional Chinese medicine (TCM), this work reports a self-adaptive pressure sensing platform (PSP) that combines the fully printed flexible pressure sensor array with an adaptive wristband-style pressure system can identify the optimal pulse signal. Besides the detected pulse rate/width/length, "Cun, Guan, Chi" position, and "floating, moderate, sinking" pulse features, the PSP combined with a machine learning-based linear regression model can also accurately predict blood pressure such as systolic, diastolic, and mean arterial pressure values. The developed diagnostic platform is demonstrated for highly reliable long-term monitoring and analysis of pulse and blood pressure across multiple human subjects over time. The design concept and proof-of-the-concept demonstrations also pave the way for the future developments of flexible sensing devices/systems for adaptive individualized monitoring in the complex practical environments for personalized medicine, along with the support for the development of digital TCM.

A Fully Integrated Orthodontic Aligner With Force Sensing Ability for Machine Learning-Assisted Diagnosis

Currently, the diagnosis of malocclusion is a highly demanding process involving complicated examinations of the dental occlusion, which increases the demand for innovative tools for occlusal data monitoring. Nevertheless, continuous wireless monitoring within the oral cavity is challenging due to limitations in sampling and device size. In this study, by embedding high-performance piezoelectric sensors into the occlusal surfaces using flexible printed circuits, a fully integrated, flexible, and self-contained transparent aligner is developed. This aligner exhibits excellent sensitivity for occlusal force detection, with a broad detection threshold and continuous pressure monitoring ability at eight distinct sites. Integrated with machine learning algorithm, this fully integrated aligner can also identify and track adverse oral habits that can cause/exacerbate malocclusion, such as lip biting, thumb sucking, and teeth grinding. This system achieved 95% accuracy in determining malocclusion types by analyzing occlusal data from over 1400 malocclusion models. This fully-integrated sensing system, with wireless monitoring and machine learning processing, marks a significant advancement in the development of intraoral wearable sensors. Moreover, it can also facilitate remote orthodontic monitoring and evaluation, offering a new avenue for effective orthodontic care.

Progress of AI assisted synthesis of polysaccharides-based hydrogel and their applications in biomedical field

Polymeric hydrogels, characterized by their highly hydrophilic three-dimensional network structures, boast exceptional physical and chemical properties alongside high biocompatibility and biodegradability. These attributes make them indispensable in various biomedical applications such as drug delivery, tissue engineering, wound dressings and sensor technologies. With the integration of artificial intelligence (AI), hydrogels are undergoing significant transformations in design, leveraging human-machine interaction, machine learning, neural networks, and 3D/4D printing technology. This article provides a concise yet comprehensive overview of polysaccharide-based hydrogels, exploring their intrinsic properties, functionalities, preparation techniques, and classifications, alongside their progress in biomedical research. Special emphasis is placed on AI-enhanced hydrogels, underscoring their transformative potential in redefining hydrogel performance and functionality. By integrating AI technologies, these intelligent hydrogels open unprecedented opportunities in precision medicine, adaptive biomaterials, and smart healthcare systems, highlighting promising directions for future research.

MEMS Acoustic Sensors: Charting the Path from Research to Real-World Applications

MEMS acoustic sensors are a type of physical quantity sensor based on MEMS manufacturing technology for detecting sound waves. They utilize various sensitive structures such as thin films, cantilever beams, or cilia to collect acoustic energy, and use certain transduction principles to read out the generated strain, thereby obtaining the targeted acoustic signal's information, such as its intensity, direction, and distribution. Due to their advantages in miniaturization, low power consumption, high precision, high consistency, high repeatability, high reliability, and ease of integration, MEMS acoustic sensors are widely applied in many areas, such as consumer electronics, industrial perception, military equipment, and health monitoring. Through different sensing mechanisms, they can be used to detect sound energy density, acoustic pressure distribution, and sound wave direction. This article focuses on piezoelectric, piezoresistive, capacitive, and optical MEMS acoustic sensors, showcasing their development in recent years, as well as innovations in their structure, process, and design methods. Then, this review compares the performance of devices with similar working principles. MEMS acoustic sensors have been increasingly widely applied in various fields, including traditional advantage areas such as microphones, stethoscopes, hydrophones, and ultrasound imaging, and cutting-edge fields such as biomedical wearable and implantable devices.

Customized surface adhesive and wettability properties of conformal electronic devices

Conformal and body-adaptive electronics have revolutionized the way we interact with technology, ushering in a new era of wearable devices that can seamlessly integrate with our daily lives. However, the inherent mismatch between artificially synthesized materials and biological tissues (caused by irregular skin fold, skin hair, sweat, and skin grease) needs to be addressed, which can be realized using body-adaptive electronics by rational design of their surface adhesive and wettability properties. Over the past few decades, various approaches have been developed to enhance the conformability and adaptability of bioelectronics by (i) increasing flexibility and reducing device thickness, (ii) improving the adhesion and wettability between bioelectronics and biological interfaces, and (iii) refining the integration process with biological systems. Successful development of a conformal and body-adaptive electronic device requires comprehensive consideration of all three aspects. This review starts with the design strategies of conformal electronics with different surface adhesive and wettability properties. A series of conformal and body-adaptive electronics used in the human body under both dry and wet conditions are systematically discussed. Finally, the current challenges and critical perspectives are summarized, focusing on promising directions such as telemedicine, mobile health, point-of-care diagnostics, and human-machine interface applications.

Compact Vital-Sensing Band with Uninterrupted Power Supply for Core Body Temperature and Pulse Rate Monitoring

Although wearable devices for continuous monitoring of vital signs have undergone significant advancements, their need for frequent recharging precludes continuous operation, potentially leading to adverse outcomes being overlooked. Additionally, the scattered locations of the sensors hamper wearability. Herein, we present a compact vital-sensing band with uninterrupted power supply designed for continuous monitoring of core body temperature (CBT) and pulse rate. The band─which comprises two sensors, a power source (i.e., a flexible thermoelectric generator (TEG) and a battery), and a flexible circuit─is worn on the forearm. The CBT is calculated by measuring the skin temperature and heat flux, while a triboelectric nanogenerator-based self-powered pressure sensor is utilized for pulse rate monitoring. The TEG is a flexible unit that converts body heat into electricity, accumulating a total energy of 314 mJ (100%). Out of this total energy, only 43.2 mJ (7.2%) is utilized for CBT measurements, while the remaining 270.80 mJ (92.8%) is stored in the battery. This enables reliable and continuous operation of the vital-sensing band, highlighting its potential for use in healthcare applications.

A Flexible Smart Healthcare Platform Conjugated with Artificial Epidermis Assembled by Three-Dimensionally Conductive MOF Network for Gas and Pressure Sensing

The rising flexible and intelligent electronics greatly facilitate the noninvasive and timely tracking of physiological information in telemedicine healthcare. Meticulously building bionic-sensitive moieties is vital for designing efficient electronic skin with advanced cognitive functionalities to pluralistically capture external stimuli. However, realistic mimesis, both in the skin's three-dimensional interlocked hierarchical structures and synchronous encoding multistimuli information capacities, remains a challenging yet vital need for simplifying the design of flexible logic circuits. Herein, we construct an artificial epidermal device by in situ growing Cu3(HHTP)2 particles onto the hollow spherical Ti3C2Tx surface, aiming to concurrently emulate the spinous and granular layers of the skin's epidermis. The bionic Ti3C2Tx@Cu3(HHTP)2 exhibits independent NO2 and pressure response, as well as novel functionalities such as acoustic signature perception and Morse code-encrypted message communication. Ultimately, a wearable alarming system with a mobile application terminal is self-developed by integrating the bimodular senor into flexible printed circuits. This system can assess risk factors related with asthmatic, such as stimulation of external NO2 gas, abnormal expiratory behavior and exertion degrees of fingers, achieving a recognition accuracy of 97.6% as assisted by a machine learning algorithm. Our work provides a feasible routine to develop intelligent multifunctional healthcare equipment for burgeoning transformative telemedicine diagnosis.

Directional Characteristic Enhancement of an Omnidirectional Detection Sensor Enabled by Strain Partitioning Effects in a Periodic Composite Hole Substrate

An omnidirectional stretchable strain sensor with high resolution is a critical component for motion detection and human-machine interaction. It is the current dominant solution to integrate several consistent units into the omnidirectional sensor based on a certain geometric structure. However, the excessive similarity in orientation characteristics among sensing units restricts orientation recognition due to their closely matched strain sensitivity. In this study, based on strain partition modulation (SPM), a sensitivity anisotropic amplification strategy is proposed for resistive strain sensors. The stress distribution of a sensitive conductive network is modulated by structural parameters of the customized periodic hole array introduced underneath the elastomer substrate. Meanwhile, the strain isolation structures are designed on both sides of the sensing unit for stress interference immune. The optimized sensors exhibit excellent sensitivity (19 for 0-80%; 109 for 80%-140%; 368 for 140%-200%), with nearly a 7-fold improvement in the 140%-200% interval compared to bare elastomer sensors. More importantly, a sensing array composed of multiple units with different hole configurations can highlight orientation characteristics with amplitude difference between channels reaching up to 29 times. For the 48-class strain-orientation decoupling task, the recognition rate of the sensitivity-differentiated layout sensor with the lightweight deep learning network is as high as 96.01%, superior to that of 85.7% for the sensitivity-consistent layout. Furthermore, the application of the sensor to the fitness field demonstrates an accurate recognition of the wrist flexion direction (98.4%) and spinal bending angle (83.4%). Looking forward, this methodology provides unique prospects for broader applications such as tactile sensors, soft robotics, and health monitoring technologies.

An Invisible Dermal Nanotattoo-Based Smart Wearable Sensor for eDiagnostics of Jaundice

Despite substantial progress in the diagnosis of jaundice/hyperbilirubinemia as the most common disease and cause of hospitalization of newborns, on the eve of Industry/Healthcare 5.0, the development of accurate and reliable wearable diagnostic sensors for noninvasive smart monitoring of bilirubin (BIL) is still in high demand. Aiming to fabricate a smart wearable sensor for early diagnosis of neonatal jaundice and its therapeutic monitoring, we here report a fluorescent dermal nanotattoo that further coupled with an IoT-integrated wearable optoelectronic reader for minimally invasive, continuous, and real-time monitoring of BIL in interstitial fluid. Selective recovery of quenched fluorescence of the dermal tattoo sensor, composed of biocompatible dissolving/hydrogel microneedles loaded with fluorescent carbon quantum dots, upon blue light exposure used for jaundice phototherapy was utilized for highly selective BIL sensing. The fascinating features of our developed smart wearable tattoo sensor and its successful results with high correlation with blood BIL results make it a highly promising sensor for easy, minimally invasive, reliable, and smart eDiagnostics and continuous therapeutic eMonitoring of jaundice and other BIL-induced diseases at the point of care. We envision that the developed nanotattoo sensing bioplatform will inspire the development of future smart tattoo sensors in various diagnostic and monitoring scenarios.

Multifunctional Human-Computer Interaction System Based on Deep Learning-Assisted Strain Sensing Array

Continuous and reliable monitoring of gait is crucial for health monitoring, such as postoperative recovery of bone joint surgery and early diagnosis of disease. However, existing gait analysis systems often suffer from large volumes and the requirement of special space for setting motion capture systems, limiting their application in daily life. Here, we develop an intelligent gait monitoring and analysis prediction system based on flexible piezoelectric sensors and deep learning neural networks with high sensitivity (241.29 mV/N), quick response (66 ms loading, 87 ms recovery), and excellent stability (R2 = 0.9946). The theoretical simulations and experiments confirm that the sensor provides exceptional signal feedback, which can easily acquire accurate gait data when fitted to shoe soles. By integrating high-quality gait data with a custom-built deep learning model, the system can detect and infer human motion states in real time (the recognition accuracy reaches 94.7%). To further validate the sensor's application in real life, we constructed a flexible wearable recognition system with human-computer interaction interface and a simple operation process for long-term and continuous tracking of athletes' gait, potentially aiding personalized health management, early detection of disease, and remote medical care.

Recent advances in flexible hydrogel sensors: Enhancing data processing and machine learning for intelligent perception

With the advent of flexible electronics and sensing technology, hydrogel-based flexible sensors have exhibited considerable potential across a diverse range of applications, including wearable electronics and soft robotics. Recently, advanced machine learning (ML) algorithms have been integrated into flexible hydrogel sensing technology to enhance their data processing capabilities and to achieve intelligent perception. However, there are no reviews specifically focusing on the data processing steps and analysis based on the raw sensing data obtained by flexible hydrogel sensors. Here we provide a comprehensive review of the latest advancements and breakthroughs in intelligent perception achieved through the fusion of ML algorithms with flexible hydrogel sensors, across various applications. Moreover, this review thoroughly examines the data processing techniques employed in flexible hydrogel sensors, offering valuable perspectives expected to drive future data-driven applications in this field.

Advances in Machine Learning for Wearable Sensors

Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare. To this end, supervised learning and unsupervised learning algorithms have emerged as powerful tools, allowing for the detection of complex patterns and relationships in large, high-dimensional data sets. In this Review, we aim to delineate the latest advancements in machine learning for wearable sensors, focusing on key developments in algorithmic techniques, applications, and the challenges intrinsic to this evolving landscape. Additionally, we highlight the potential of machine-learning approaches to enhance the accuracy, reliability, and interpretability of wearable sensor data and discuss the opportunities and limitations of this emerging field. Ultimately, our work aims to provide a roadmap for future research endeavors in this exciting and rapidly evolving area.

Materials, Structure, and Interface of Stretchable Interconnects for Wearable Bioelectronics

Since wearable technologies for telemedicine have emerged to tackle global health concerns, the demand for well-attested wearable healthcare devices with high user comfort also arises. Skin-wearables for health monitoring require mechanical flexibility and stretchability for not only high compatibility with the skin's dynamic nature but also a robust collection of fine health signals from within. Stretchable electrical interconnects, which determine the device's overall integrity, are one of the fundamental units being understated in wearable bioelectronics. In this review, a broad class of materials and engineering methodologies recently researched and developed are presented, and their respective attributes, limitations, and opportunities in designing stretchable interconnects for wearable bioelectronics are offered. Specifically, the electrical and mechanical characteristics of various materials (metals, polymers, carbons, and their composites) are highlighted, along with their compatibility with diverse geometric configurations. Detailed insights into fabrication techniques that are compatible with soft substrates are also provided. Importantly, successful examples of establishing reliable interfacial connections between soft and rigid elements using novel interconnects are reviewed. Lastly, some perspectives and prospects of remaining research challenges and potential pathways for practical utilization of interconnects in wearables are laid out.

Designs and Applications for the Multimodal Flexible Hybrid Epidermal Electronic Systems

Research on the flexible hybrid epidermal electronic system (FHEES) has attracted considerable attention due to its potential applications in human-machine interaction and healthcare. Through material and structural innovations, FHEES combines the advantages of traditional stiff electronic devices and flexible electronic technology, enabling it to be worn conformally on the skin while retaining complex system functionality. FHEESs use multimodal sensing to enhance the identification accuracy of the wearer's motion modes, intentions, or health status, thus realizing more comprehensive physiological signal acquisition. However, the heterogeneous integration of soft and stiff components makes balancing comfort and performance in designing and implementing multimodal FHEESs challenging. Herein, multimodal FHEESs are first introduced in 2 types based on their different system structure: all-in-one and assembled, reflecting totally different heterogeneous integration strategies. Characteristics and the key design issues (such as interconnect design, interface strategy, substrate selection, etc.) of the 2 multimodal FHEESs are emphasized. Besides, the applications and advantages of the 2 multimodal FHEESs in recent research have been presented, with a focus on the control and medical fields. Finally, the prospects and challenges of the multimodal FHEES are discussed.

Shape-position perceptive fusion electronic skin with autonomous learning for gesture interaction

Wearable devices, such as data gloves and electronic skins, can perceive human instructions, behaviors and even emotions by tracking a hand's motion, with the help of knowledge learning. The shape or position single-mode sensor in such devices often lacks comprehensive information to perceive interactive gestures. Meanwhile, the limited computing power of wearable applications restricts the multimode fusion of different sensing data and the deployment of deep learning networks. We propose a perceptive fusion electronic skin (PFES) with a bioinspired hierarchical structure that utilizes the magnetization state of a magnetostrictive alloy film to be sensitive to external strain or magnetic field. Installed at the joints of a hand, the PFES realizes perception of curvature (joint shape) and magnetism (joint position) information by mapping corresponding signals to the two-directional continuous distribution such that the two edges represent the contributions of curvature radius and magnetic field, respectively. By autonomously selecting knowledge closer to the user's hand movement characteristics, the reinforced knowledge distillation method is developed to learn and compress a teacher model for rapid deployment on wearable devices. The PFES integrating the autonomous learning algorithm can fuse curvature-magnetism dual information, ultimately achieving human machine interaction with gesture recognition and haptic feedback for cross-space perception and manipulation.

Wearable multichannel-active pressurized pulse sensing platform

With the modernization of traditional Chinese medicine (TCM), creating devices to digitalize aspects of pulse diagnosis has proved to be challenging. The currently available pulse detection devices usually rely on external pressure devices, which are either bulky or poorly integrated, hindering their practical application. In this work, we propose an innovative wearable active pressure three-channel pulse monitoring device based on TCM pulse diagnosis methods. It combines a flexible pressure sensor array, flexible airbag array, active pressure control unit, advanced machine learning approach, and a companion mobile application for human-computer interaction. Due to the high sensitivity (460.1 kPa-1), high linearity (R 2 > 0.999) and flexibility of the flexible pressure sensors, the device can accurately simulate finger pressure to collect pulse waves (Cun, Guan, and Chi) at different external pressures on the wrist. In addition, by measuring the change in pulse wave amplitude at different pressures, an individual's blood pressure status can be successfully predicted. This enables truly wearable, actively pressurized, continuous wireless dynamic monitoring of wrist pulse health. The innovative and integrated design of this pulse monitoring platform could provide a new paradigm for digitizing aspects of TCM and other smart healthcare systems.

Transient charge-driven 3D conformal printing via pulsed-plasma impingement

Seamless integration of microstructures and circuits on three-dimensional (3D) complex surfaces is of significance and is catalyzing the emergence of many innovative 3D curvy electronic devices. However, patterning fine features on arbitrary 3D targets remains challenging. Here, we propose a facile charge-driven electrohydrodynamic 3D microprinting technique that allows micron- and even submicron-scale patterning of functional inks on a couple of 3D-shaped dielectrics via an atmospheric-pressure cold plasma jet. Relying on the transient charging of exposed sites arising from the weakly ionized gas jet, the specified charge is programmably deposited onto the surface as a virtual electrode with spatial and time spans of ~mm in diameter and ~μs in duration to generate a localized electric field accordantly. Therefore, inks with a wide range of viscosities can be directly drawn out from micro-orifices and deposited on both two-dimensional (2D) planar and 3D curved surfaces with a curvature radius down to ~1 mm and even on the inner wall of narrow cavities via localized electrostatic attraction, exhibiting a printing resolution of ~450 nm. In addition, several conformal electronic devices were successfully printed on 3D dielectric objects. Self-aligned 3D microprinting, with stacking layers up to 1400, is also achieved due to the electrified surfaces. This microplasma-induced printing technique exhibits great advantages such as ultrahigh resolution, excellent compatibility of inks and substrates, antigravity droplet dispersion, and omnidirectional printing on 3D freeform surfaces. It could provide a promising solution for intimately fabricating electronic devices on arbitrary 3D surfaces.

Fully Integrated Multiplexed Wristwatch for Real-Time Monitoring of Electrolyte Ions in Sweat

Considerable progress has already been made in sweat sensors based on electrochemical methods to realize real-time monitoring of biomarkers. However, realizing long-term monitoring of multiple targets at the atomic level remains extremely challenging, in terms of designing stable solid contact (SC) interfaces and fully integrating multiple modules for large-scale applications of sweat sensors. Herein, a fully integrated wristwatch was designed using mass-manufactured sensor arrays based on hierarchical multilayer-pore cross-linked N-doped porous carbon coated by reduced graphene oxide (NPCs@rGO-950) microspheres with high hydrophobicity as core SC, and highly selective monitoring simultaneously for K+, Na+, and Ca2+ ions in human sweat was achieved, exhibiting near-Nernst responses almost without forming an interfacial water layer. Combined with computed tomography, solid-solid interface potential diffusion simulation results reveal extremely low interface diffusion potential and high interface capacitance (598 μF), ensuring the excellent potential stability, reversibility, repeatability, and selectivity of sensor arrays. The developed highly integrated-multiplexed wristwatch with multiple modules, including SC, sensor array, microfluidic chip, signal transduction, signal processing, and data visualization, achieved reliable real-time monitoring for K+, Na+, and Ca2+ ion concentrations in sweat. Ingenious material design, scalable sensor fabrication, and electrical integration of multimodule wearables lay the foundation for developing reliable sweat-sensing systems for health monitoring.

Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor

Nano- and microplastic particles are a global and emerging environmental issue that might pose potential threats to human health. The present work exploits artificial intelligence (AI) to identify nano- and microplastics in water by monitoring the interaction of the sample with a sensitive surface. An estrogen receptor (ER) grafted onto a gold surface, realized on a nonexpensive and easy-to-produce plastic optical fiber (POF) platform in order to excite a surface plasmon resonance (SPR) phenomenon, has been developed in order to carry out a "smart" sensitive interface (ER-SPR-POF interface). The ER-SPR-POF interface offers output data useful for exploiting a machine learning-based approach to achieve nano- and microplastic particle sensors. This work developed a proof-of-concept sensor through a training phase carried out by different particles, in terms of materials and size. The experimental results have demonstrated that the proposed "smart" ER-SPR-POF interface combined with AI can be used to identify the kind of particles in terms of the materials (polystyrene; poly(methyl methacrylate)) and size (20 μm; 100 nm) with an accuracy of 90.3%.

Advancements and Challenges in Non-Invasive Sensor Technologies for Swallowing Assessment: A Review

Dysphagia is a pervasive health issue that impacts diverse demographic groups worldwide, particularly the elderly, stroke survivors, and those suffering from neurological disorders. This condition poses substantial health risks, including malnutrition, respiratory complications, and increased mortality. Additionally, it exacerbates economic burdens by extending hospital stays and escalating healthcare costs. Given that this disorder is frequently underestimated in vulnerable populations, there is an urgent need for enhanced diagnostic and therapeutic strategies. Traditional diagnostic tools such as the videofluoroscopic swallowing study (VFSS) and flexible endoscopic evaluation of swallowing (FEES) require interpretation by clinical experts and may lead to complications. In contrast, non-invasive sensors offer a more comfortable and convenient approach for assessing swallowing function. This review systematically examines recent advancements in non-invasive swallowing function detection devices, focusing on the validation of the device designs and their implementation in clinical practice. Moreover, this review discusses the swallowing process and the associated biomechanics, providing a theoretical foundation for the technologies discussed. It is hoped that this comprehensive overview will facilitate a paradigm shift in swallowing assessments, steering the development of technologies towards more accessible and accurate diagnostic tools, thereby improving patient care and treatment outcomes.

A wearable flexible electrochemical biosensor with CuNi-MOF@rGO modification for simultaneous detection of uric acid and dopamine in sweat

BACKGROUND

In health assessment and personalized medical services, accurate detection of biological markers such as dopamine (DA) and uric acid (UA) in sweat is crucial for providing valuable physiological information. However, there are challenges in detecting sweat biomarkers due to their low concentrations, variations in sweat yield among individuals, and the need for efficient sweat collection.

RESULTS

We synthesized CuNi-MOF@rGO as a high-activity electrocatalyst and investigated its feasibility and electrochemical mechanism for simultaneously detecting low-concentration biomarkers UA and DA. Interaction between the non-coordinating carboxylate group and the sample produces effective separation signals for DA and UA. The wearable biomimetic biosensor has a wide linear range of 1-500 μM, with a detection limit of 9.41 μM and sensitivity of 0.019 μA μM-1 cm-2 for DA, and 10-1000 μM, with a detection limit of 9.09 μM and sensitivity of 0.026 μA μM-1 cm-2 for UA. Thus, our sensor performs excellently in detecting low-concentration biomarkers. To improve sweat collection, we designed a microfluidic-controlled device with hydrophilic modification in the microchannel. Experimental results show optimal ink flow at 2% concentration. Overall, we developed an innovative and highly active electrocatalyst, successfully enabling simultaneous detection of low-concentration biomarkers UA and DA.

SIGNIFICANCE

This study provides a strategy for sweat analysis and health monitoring. Moreover, the sensor also showed good performance in detecting real sweat samples. This study has shown great potential in future advances in sweat analysis and health monitoring.

Fully printed minimum port flexible interdigital electrode sensor arrays

Screen-printed interdigital electrode-based flexible pressure sensor arrays play a crucial role in human-computer interaction and health monitoring due to their simplicity of fabrication. However, the long-standing challenge of how to reduce the number of electrical output ports of interdigital electrodes to facilitate integration with back-end circuits is still commonly ignored. Here, we propose a screen-printing strategy to avoid wire cross-planes for rapid fabrication of flexible pressure sensor arrays. By innovatively introducing an insulating ink to realize electrical insulation and three-dimensional interconnection of wire crossings, the improved sensor array (4 × 4) successfully reduces the number of output ports from 17 to 8. In addition, we further constructed microstructures on the laser-etched electrode surfaces and the sensitive layer, which enabled the sensor to achieve a sensitivity as high as 17 567.5 kPa-1 in the range of 0-50 kPa. Moreover, we integrated the sensors with back-end circuits for the precise detection of tactile and physiological information. This provides a reliable method for preparing high-performance flexible sensor arrays and large-scale integration of microsensors.

A Laparoscopically Compatible Rapid-Adhesion Bioadhesive for Asymmetric Adhesion, Non-Pressing Hemostasis, and Seamless Seal

Bioadhesive hydrogels offer unprecedented opportunities in hemostatic agents and tissue sealing; however, the application of existing bioadhesive hydrogels through narrow spaces to achieve strong adhesion in fluid-rich physiological environments is challenged either by undesired indiscriminate adhesion or weak wet tissue adhesion. Here, a laparoscopically compatible asymmetric adhesive hydrogel (aAH) composed of sprayable adhesive hydrogel powders and injectable anti-adhesive glue is proposed for hemostasis and to seal the bloody tissues in a non-pressing way, allowing for preventing postoperative adhesion. The powders can seed on the irregular bloody wound to rapidly absorb interfacial fluid, crosslink, and form an adhesive hydrogel to hemostatic seal (blood clotting time and tissue sealing in 10 s, ≈200 mm Hg of burst pressure in sealed porcine tissues). The aAH can be simply formed by crosslinking the upper powder with injectable glue to prevent postoperative adhesion (adhesive strength as low as 1 kPa). The aAH outperforms commercial hemostatic agents and sealants in the sealing of bleeding organs in live rats, demonstrating superior anti-adhesive efficiency. Further, the hemostatic seamless sealing by aAH succeeds in shortening the time of warm ischemia, decreasing the blood loss, and reducing the possibility of rebleeding in the porcine laparoscopic partial nephrectomy model.

A Dual-Mode Pressure and Temperature Sensor

The emerging field of flexible tactile sensing systems, equipped with multi-physical tactile sensing capabilities, holds vast potential across diverse domains such as medical monitoring, robotics, and human-computer interaction. In response to the prevailing challenges associated with the limited integration and sensitivity of flexible tactile sensors, this paper introduces a versatile tactile sensing system capable of concurrently monitoring temperature and pressure. The temperature sensor employs carbon nanotube/graphene conductive paste as its sensitive material, while the pressure sensor integrates an ionic gel containing boron nitride as its sensitive layer. Through the application of cost-effective screen printing technology, we have successfully manufactured a flexible dual-mode sensor with exceptional performance, featuring high sensitivity (804.27 kPa-1), a broad response range (50 kPa), rapid response time (17 ms), and relaxation time (34 ms), alongside exceptional durability over 5000 cycles. Furthermore, the resistance temperature coefficient of the sensor within the temperature range of 12.5 °C to 93.7 °C is -0.17% °C-1. The designed flexible dual-mode tactile sensing system enables the real-time detection of pressure and temperature information, presenting an innovative approach to electronic skin with multi-physical tactile sensing capabilities.