Electronic skin as wireless human-machine interfaces for robotic VR

A Bionic Textile Sensory System for Humanoid Robots Capable of Intelligent Texture Recognition

Artificial tactile perception systems that emulate the functions of slow adaptive (SA) and fast adaptive (FA) cutaneous mechanoreceptors are essential for developing advanced prosthetics and humanoid robots. However, constructing a high-performance sensory system within a single device capable of simultaneously perceiving both static and dynamic forces for surface-texture recognition remains a critical challenge; this contrasts with common strategies integrating individual SA- and FA-mimicking sensors in multi-layered, multi-circuit configurations. Herein, a textile pressure/tactile (PT) sensor is reported based solely on piezoresistive principle alongside high sensitivity and rapid response to both high-frequency vibrations and static forces. These characteristics are attributed to the sensor's 3D multiscale architecture and the corresponding hierarchical structural deformation of its honeycomb-like sensing fabric. As a proof-of-concept application relevant to humanoid robotics and prosthetics, an automated surface-texture-recognition system is constructed by integrating the PT sensor with machine-learning algorithms, a prosthetic device, an industrial robot arm, and a graphical user interface. This artificial sensory system demonstrates the ability to learn distinct object features, differentiate fine surface textures, and subsequently classify unknown textiles with high recognition accuracy (>98.9%) across a wide range of scanning speeds (50-300 mm s-1). These results show promise for the future development of interactive artificial intelligence.

Hands-Free Human-Machine Interfaces Using Piezoelectric Sensors and Accelerometers for Simulated Wheelchair Control in Older Adults and People with Physical Disabilities

Human-machine interface (HMI) systems are increasingly utilized to develop assistive technologies for individuals with disabilities and older adults. This study proposes two HMI systems using piezoelectric sensors to detect facial muscle activations from eye and tongue movements, and accelerometers to monitor head movements. This system enables hands-free wheelchair control for those with physical disabilities and speech impairments. A prototype wearable sensing device was also designed and implemented. Four commands can be generated using each sensor to steer the wheelchair. We conducted tests in offline and real-time scenarios to assess efficiency and usability among older volunteers. The head-machine interface achieved greater efficiency than the face-machine interface. The simulated wheelchair control tests showed that the head-machine interface typically required twice the time of joystick control, whereas the face-machine interface took approximately four times longer. Participants noted that the head-mounted wearable device was flexible and comfortable. Both modalities can be used for wheelchair control, especially the head-machine interface for patients retaining head movement. In severe cases, the face-machine interface can be used. Moreover, hybrid control can be employed to satisfy specific requirements. Compared to current commercial devices, the proposed HMIs provide lower costs, easier fabrication, and greater adaptability for real-world applications. We will further verify and improve the proposed devices for controlling a powered wheelchair, ensuring practical usability for people with paralysis and speech impairments.

Covalent Graphene-Metal-Organic Polyhedra Hybrids: Triboelectric Nanogenerators for Next Generation of Wearable E-Skin Technologies

The development of stretchable energy-harvesting devices that convert mechanical stimuli into electrical energy is crucial for advancing self-powered electronic skin (e-skin) technologies. Triboelectric nanogenerators (TENGs) show promise but suffer from low stretchability, limited conductivity, and poor mechanical durability. Here, we report a new generation of TENGs designed via the molecular chemistry of metal-organic polyhedra (MOPs) covalently bonded to functionalized 2D nitrogen-doped graphene sheets (NG@MOP). The resulting NG@MOP hybrids, featuring aromatic regions and surface amine groups, link to anionic nickel-based MOPs, [Ni8(HImDC)12]8-, through amide bonds. This hybrid exhibits a large surface area, hierarchical micro-mesoporous channels, and structural defects, improving mechanical resilience. Incorporating NG@MOP into a polyurethane matrix produces a highly stretchable, biocompatible film withstanding over 500% strain. The composite delivers excellent triboelectric output, generating 417 V and 10.8 µA at 5 wt % loading. Applied as a wearable e-skin, the device maintains functionality under extreme deformation, demonstrating a strain sensitivity of 13 mV per degree of motion. It efficiently detects subtle body motions such as bending and stretching, showing strong potential for wearable motion sensing and real-time health monitoring.

Octopus Tentacle-Inspired In-Sensor Adaptive Integral for Edge-Intelligent Touch Intention Recognition

Electronics continue to drive technological innovation and diversified applications. To ensure efficiency and effectiveness across various interactive contexts, the ability to adjust operating functions or parameters according to environmental shifts or user requirements is highly desirable. However, due to the inherent limitations of nonadaptive device structures and materials, the current development of touch electronics faces challenges, e.g., limited hardware resources, poor adaptability, weak deformation stability, and bottlenecks in sensing data processing. Here, a reconfigurable and adaptive intelligent (RAI) touch sensor is proposed, inspired by octopus's tentacle cognitive behavior. It realizes remarkable deformability and highly efficient multitouch interactions. The geometric progression structure of the sensing element equips the RAI touch sensor with a unique integrated-in-sensing mechanism and programmable logic. This greatly compresses sensing data dimensionality at the edge, yielding concise and undistorted interactive signals. By leveraging the advantages of hard-soft bonding and interface modulation of functional materials, the adaptability is achieved with a 200% strain range a 180° twist tolerance, and exceptional deformation stability of >10 000 cycles. The diverse application-specific configurations of the RAI touch sensor, enable a dynamic intention recognition accuracy of over 99%, advancing next-generation Internet of Things and edge computing research and innovation.

Biomimetic Heteromodulus All-Fluoropolymer Piezoelectric Nanofiber Mats for Highly Sensitive Acoustic Detection

Flexible piezoelectric pressure sensors have aroused a plethora of applications in wearable electronics, acoustic transducers, and energy harvesters thanks to many merits such as prompt response, good signal linearity, and ease of shaping. However, as all-polymer piezoelectric films have a low piezoelectric coefficient and severe stress dissipation, it is currently challenging to achieve a high piezoelectric output for the foregoing applications without introducing nanomaterials or piezoelectric ceramics. Here, we report a local stress engineering strategy to fabricate biomimetic all-fluoropolymer piezoelectric film pressure sensors with high-modulus poly(vinylidene fluoride) (PVDF) nanospheres embedded on low-modulus poly(vinylidene fluoride-trifluoride ethylene) (PVDF-TrFE) nanofibers for highly sensitive acoustic detection. High-modulus PVDF nanospheres create many local stress concentration sites on PVDF-TrFE nanofibers and increase the local deformation, leading to significantly improved force/pressure sensitivity. As such, by comparison with the force sensitivity of 60 mV/N for neat PVDF-TrFE, the heteromodulus fiber mats with 10 wt % PVDF nanospheres can achieve a force sensitivity of 145.1 mV/N over 0-25 N dynamic impact force (i.e., 0 ∼ 250 kPa pressure), together with an acoustic detection limit as low as 60 dB or 0.02 Pa.

Stretchable, Rechargeable, Multimodal Hybrid Electronics for Decoupled Sensing toward Emotion Detection

Despite the rapid development of stretchable electronic devices for various applications in biomedicine and healthcare, the coupling between multiple input signals remains an obstacle in multimodal sensing before use in practical environments. This work introduces a fully integrated stretchable, rechargeable, multimodal hybrid device that combines decoupled sensors with a flexible wireless powering and transmitting module for emotion recognition. Through optimized structural design and material selection, the sensors can provide continuous real-time decoupled monitoring of biaxial strain, temperature, humidity, heart rate, and SpO2 levels. With a stacked bilayer for both the sensors and the flexible circuit, the rechargeable system showcases a reduced device footprint and improved comfort. A neural network model is also demonstrated to allow for high-precision facial expression recognition. By transmitting the real-time measured data to mobile devices and the cloud, the system can allow healthcare professionals to evaluate psychological health and provide emotional support through telemedicine when needed.

Soft Materials and Devices Enabling Sensorimotor Functions in Soft Robots

Sensorimotor functions, the seamless integration of sensing, decision-making, and actuation, are fundamental for robots to interact with their environments. Inspired by biological systems, the incorporation of soft materials and devices into robotics holds significant promise for enhancing these functions. However, current robotics systems often lack the autonomy and intelligence observed in nature due to limited sensorimotor integration, particularly in flexible sensing and actuation. As the field progresses toward soft, flexible, and stretchable materials, developing such materials and devices becomes increasingly critical for advanced robotics. Despite rapid advancements individually in soft materials and flexible devices, their combined applications to enable sensorimotor capabilities in robots are emerging. This review addresses this emerging field by providing a comprehensive overview of soft materials and devices that enable sensorimotor functions in robots. We delve into the latest development in soft sensing technologies, actuation mechanism, structural designs, and fabrication techniques. Additionally, we explore strategies for sensorimotor control, the integration of artificial intelligence (AI), and practical application across various domains such as healthcare, augmented and virtual reality, and exploration. By drawing parallels with biological systems, this review aims to guide future research and development in soft robots, ultimately enhancing the autonomy and adaptability of robots in unstructured environments.

Hydrogel-Based Biointerfaces: Recent Advances, Challenges, and Future Directions in Human-Machine Integration

Human-machine interfacing (HMI) has emerged as a critical technology in healthcare, robotics, and wearable electronics, with hydrogels offering unique advantages as multifunctional materials that seamlessly connect biological systems with electronic devices. This review provides a detailed examination of recent advancements in hydrogel design, focusing on their properties and potential applications in HMI. We explore the key characteristics such as biocompatibility, mechanical flexibility, and responsiveness, which are essential for effective and long-term integration with biological tissues. Additionally, we highlight innovations in conductive hydrogels, hybrid and composite materials, and fabrication techniques such as 3D/4D printing, which allow for the customization of hydrogel properties to meet the demands of specific HMI applications. Further, we discuss the diverse classes of polymers that contribute to hydrogel conductivity, including conducting, natural, synthetic, and hybrid polymers, emphasizing their role in enhancing electrical performance and mechanical adaptability. In addition to material design, we examine the regulatory landscape governing hydrogel-based biointerfaces for HMI applications, addressing the key considerations for clinical translation and commercialization. An analysis of the patent landscape provides insights into emerging trends and innovations shaping the future of hydrogel technologies in human-machine interactions. The review also covers a range of applications, including wearable electronics, neural interfaces, soft robotics, and haptic systems, where hydrogels play a transformative role in enhancing human-machine interactions. Thereafter, the review addresses the challenges hydrogels face in HMI applications, including issues related to stability, biocompatibility, and scalability, while offering future perspectives on the continued evolution of hydrogel-based systems for HMI technologies.

Self-powered electrotactile textile haptic glove for enhanced human-machine interface

Human-machine interface (HMI) plays an important role in various fields, where haptic technologies provide crucial tactile feedback that greatly enhances user experience, especially in virtual reality/augmented reality, prosthetic control, and therapeutic applications. Through tactile feedback, users can interact with devices in a more realistic way, thereby improving the overall effectiveness of the experience. However, existing haptic devices are often bulky due to cumbersome instruments and power modules, limiting comfort and portability. Here, we introduce a concept of wearable haptic technology: a thin, soft, self-powered electrotactile textile haptic (SPETH) glove that uses the triboelectric effect and gas breakdown discharge for localized electrical stimulation. Daily hand movements generate sufficient mechanical energy to power the SPETH glove. Its features-softness, lightweight, self-sustainability, portability, and affordability-enable it to provide tactile feedback anytime and anywhere without external equipment. This makes the SPETH glove an enhanced, battery-free HMI suitable for a wide range of applications.

A sensor-actuator-coupled gustatory interface chemically connecting virtual and real environments for remote tasting

Recent advancements in virtual reality (VR) and augmented reality (AR) have strengthened the bridge between virtual and real worlds via human-machine interfaces. Despite extensive research into biophysical signals, gustation, a fundamental component of the five senses, has experienced limited progress. This work reports a bio-integrated gustatory interface, "e-Taste," to address the underrepresented chemical dimension in current VR/AR technologies. This system facilitates remote perception and replication of taste sensations through the coupling of physically separated sensors and actuators with wireless communication modules. By using chemicals representing five basic tastes, systematic codesign of key functional components yields reliable performance including tunability, versatility, safety, and mechanical robustness. Field testing involving human subjects focusing on user perception confirms its proficiency in digitally simulating a range of taste intensities and combinations. Overall, this investigation pioneers a chemical dimension in AR/VR technology, paving the way for users to transcend visual and auditory virtual engagements by integrating the taste sensation into virtual environment for enhanced digital experiences.

High-Performance Flexible Capacitive Pressure Sensor Based on a Spiked Nickel/Polyimide Composite Nanofiber Membrane

Flexible capacitive pressure sensors are now widely used in the fields of electronic skin, medical monitoring, and human-computer interaction. However, most of the current flexible capacitive pressure sensors generally suffer easy saturation and low sensitivity under high pressure. This paper proposes a new strategy using evenly distributed spiked nickel (Ni) particles as fillers in a nanofiber membrane to prepare flexible capacitive pressure sensors. The spiked Ni particles are embedded into the interior of polyimide (PI) electrospun nanofiber membranes by electrostatic self-assembly. The experimental results show that the introduction of spiked Ni particles effectively increased the sensitivity of the sensor under high pressure due to the formation of many parallel microcapacitors. In addition, a novel combination method is adopted to integrate individual sensor modules into arbitrary sensor arrays for sensing field pressures. Specifically, the sensor prototype with a 2.7 weight ratio of spiked nickel/PI nanofiber membranes was characterized by short response/recovery times (30/40 ms), wide pressure detection range (1.5 MPa), and excellent mechanical stability (1000 cycles), more than 4-fold increase in sensor sensitivity (4.04 MPa-1 at 0-1.5 MPa) compared to pure PI nanofiber membrane dielectric layers. Due to its superior performance demonstration, the sensor could be applied in many scenarios, such as human motion detection, sleeping posture monitoring, and plantar pressure measurement, indicating good application prospects in diverse wearable systems.

Velocity-Adaptive Electrohydrodynamic Printing for Microscale Conformal Circuits on Freeform Curved Surfaces

High-resolution printing of conformal circuits on curved surfaces is critical to achieving structure-function integration in electromechanically coupled components like antennas. However, existing printing techniques such as inkjet or extrusion-based printing fail to conformally deposit microscale conductive circuits on freeform curved surfaces with curvature variations. Herein, we propose an innovative electrohydrodynamic (EHD) printing strategy that can adaptively adjust the nozzle-to-substrate distance and printing velocity according to surface curvature, enabling the direct printing of conductive circuits on diverse curved surfaces with microscale resolution and high uniformity. A path-planning algorithm is developed based on the target surface morphology captured from the scanned 3D point cloud data. The printing velocity at each point along the printing trajectory can be adaptively calculated according to the Gaussian curvature and mapping angle. This strategy makes the deposition rate well match the stage's moving speed, facilitating the uniform EHD printing of conductive patterns with a line width of 39.31 ± 4.06 μm on different surfaces with curvatures ranging from 10 to 2000 m-1. As a proof of concept, a uniform snowflake pattern with good conductivity is EHD printing on a naturally insulated conch with the smallest line width of 35.74 ± 4.24 μm. A metasurface with microscale conductive feature arrays is specially printed on a radome-shaped polymeric surface, exhibiting dual-band cloaking and reduced scattering characteristics compared to conventional metal substrates. We envision that the proposed velocity-adaptive EHD printing technique would mature into a promising and versatile tool to fabricate microscale conductive circuits on diverse curved surfaces for potential applications in conformal antennas and functional sensing or electromagnetic surfaces.

Ultrahigh-Current-Density Tribovoltaic Nanogenerators Based on Hydrogen Bond-Activated Flexible Organic Semiconductor Textiles

The polymer-based triboelectric nanogenerator (TENG) has long grappled with the constraint of limited current density (CD), whereas semiconductor-based triboelectric nanogenerators, using the tribovoltaic effect, have shown promising potential for achieving high current density. This study introduces an effective solution─a direct current tribovoltaic nanogenerator with ultrahigh current density─founded on a flexible organic semiconductor textile activated by solvents. By introducing 95% ethyl alcohol, an ultrahigh current density of 8.75 A/m2 and peak power density of 1.07 W/m2 are demonstrated, marking a striking enhancement of 438-fold and 170-fold, respectively, in comparison to the friction surface without 95% ethyl alcohol. The activation mechanism is that the poly(vinyl alcohol) dissolution by solvents exposes more PEDOT:PSS, and the formation of hydrogen bonds with PSS- releases more active PEDOT+. This advancement finds practical utility, as evidenced by successful demonstrations involving cell phone charging and small motor propulsion. The breakthrough unveiled in this work presents vistas for the widespread application of flexible organic semiconductor textile-based tribovoltaic nanogenerators, offering exciting opportunities for biomechanical energy harvesting.

Thin and Flexible Breeze-Sense Generators for Non-Contact Haptic Feedback in Virtual Reality

In the realm of virtual reality (VR), haptic feedback is integral to enhance the immersive experience; yet, existing wearable devices predominantly rely on skin contact feedback, lacking options for compact and non-contact breeze-sense feedback. Herein, we propose a compact and non-contact working model piezoelectret actuator for providing a gentle and safe breeze sensation. This easy-fabricated and flexible breeze-sense generator with thickness around 1 mm generates air flow pressure up to ~ 163 Pa, which is significantly sensed by human skin. In a typical demonstration, the breeze-sense generators array showcases its versatility by employing multiple coded modes for non-contact information transmitting. The thin thinness and good flexibility facilitate seamless integration with wearable VR setups, and the wearable arrays empower volunteers to precisely perceive the continuous and sudden breeze senses in the virtual environments. This work is expected to inspire developing new haptic feedback devices that play pivotal roles in human-machine interfaces for VR applications.

Biointerface engineering of flexible and wearable electronics

Biointerface sensing is a cutting-edge interdisciplinary field that merges conceptual and practical aspects. Wearable bioelectronics enable efficient interaction and close contact with biological components such as tissues and organs, paving the way for a wide range of medical applications, including personal health monitoring and medical intervention. To be applicable in real-world settings, the patches must be stable and adhere to the skin without causing discomfort or allergies in both wet and dry conditions, as well as other desirable features such as being ultra-soft, thin, flexible, and stretchable. Biosensors have emerged as promising tools primarily used to directly detect biological and electrophysiological signals, enhancing the efficacy of personalized medical treatments and enabling accurate tracking of human well-being. This review highlights the engineering of skin-tissue surfaces/interfaces and their interactions with wearable patches, aiming for both a broad and in-depth understanding of the mechanical and physicochemical properties required for the advancement of flexible and wearable skin patches. Specifically, the advantages of flexible bioelectronics and sensors with optimized surface geometry for long-term diagnosis are discussed. This insight aims to guide the future development of functional materials that can interact with human tissue in a controlled manner. Finally, we provide perspectives on the challenges and potential applications of biointerface engineering in wearable devices.

Triboelectric Bending Sensors for AI-Enabled Sign Language Recognition

Human-machine interfaces and wearable electronics, as fundamentals to achieve human-machine interactions, are becoming increasingly essential in the era of the Internet of Things. However, contemporary wearable sensors based on resistive and capacitive mechanisms demand an external power, impeding them from extensive and diverse deployment. Herein, a smart wearable system is developed encompassing five arch-structured self-powered triboelectric sensors, a five-channel data acquisition unit to collect finger bending signals, and an artificial intelligence (AI) methodology, specifically a long short-term memory (LSTM) network, to recognize signal patterns. A slider-crank mechanism that precisely controls the bending angle is designed to quantitively assess the sensor's performance. Thirty signal patterns of sign language of each letter are collected and analyzed after the environment noise and cross-talks among different channels are reduced and removed, respectively, by leveraging low pass filters. Two LSTM models are trained using different training sets, and four indexes are introduced to evaluate their performance, achieving a recognition accuracy of 96.15%. This work demonstrates a novel integration of triboelectric sensors with AI for sign language recognition, paving a new application avenue of triboelectric sensors in wearable electronics.

Ultra-Fast Moisture Sensor for Respiratory Cycle Monitoring and Non-Contact Sensing Applications

As human-machine interface hardware advances, better sensors are required to detect signals from different stimuli. Among numerous technologies, humidity sensors are critical for applications across different sectors, including environmental monitoring, food production, agriculture, and healthcare. Current humidity sensors rely on materials that absorb moisture, which can take some time to equilibrate with the surrounding environment, thus slowing their temporal response and limiting their applications. Here, this challenge is tackled by combining a nanogap electrode (NGE) architecture with chicked egg-derived albumen as the moisture-absorbing component. The sensors offer inexpensive manufacturing, high responsivity, ultra-fast response, and selectivity to humidity within a relative humidity range of 10-70% RH. Specifically, the egg albumen-based sensor showed negligible response to relevant interfering species and remained specific to water moisture with a room-temperature responsivity of 1.15 × 104. The nm-short interelectrode distance (circa 20 nm) of the NGE architecture enables fast temporal response, with rise/fall times of 10/28 ms, respectively, making the devices the fastest humidity sensors reported to date based on a biomaterial. By leveraging these features, non-contact moisture sensing and real-time respiratory cycle monitoring suitable for diagnosing chronic diseases such as sleep apnea, asthma, and pulmonary disease are demonstrated.

Wearable Resistive-Type Stretchable Strain Sensors: Materials and Applications

The rapid advancement of wearable electronics over recent decades has led to the development of stretchable strain sensors, which are essential for accurately detecting and monitoring mechanical deformations. These sensors have widespread applications, including movement detection, structural health monitoring, and human-machine interfaces. Resistive-type sensors have gained significant attention due to their simple design, ease of fabrication, and adaptability to different materials. Their performance, evaluated by metrics like stretchability and sensitivity, is influenced by the choice of strain-sensitive materials. This review offers a comprehensive comparison and evaluation of different materials used in resistive strain sensors, including metal and semiconductor films, low-dimensional materials, intrinsically conductive polymers, and gels. The review also highlights the latest applications of resistive strain sensors in motion detection, healthcare monitoring, and human-machine interfaces by examining device physics and material characteristics. This comparative analysis aims to support the selection, application, and development of resistive strain sensors tailored to specific applications.

Bio-inspired e-skin with integrated antifouling and comfortable wearing for self-powered motion monitoring and ultra-long-range human-machine interaction

Electronic skin (e-skin) inspired by the sensory function of the skin demonstrates broad application prospects in health, medicine, and human-machine interaction. Herein, we developed a self-powered all-fiber bio-inspired e-skin (AFBI E-skin) that integrated functions of antifouling, antibacterial, biocompatibility and breathability. AFBI E-skin was composed of three layers of electrospun nanofibrous films. The superhydrophobic outer layer Poly(vinylidene fluoride)-silica nanofibrous films (PVDF-SiO2 NFs) possessed antifouling properties against common liquids in daily life and resisted bacterial adhesion. The polyaniline nanofibrous films (PANI NFs) were used as the electrode layer, and it had strong "static" antibacterial capability. Meanwhile, the inner layer Polylactic acid nanofibrous films (PLA NFs) served as a biocompatible substrate. Based on the triboelectric nanogenerator principle, AFBI E-skin not only enabled self-powered sensing but also utilized the generated electrical stimulation for "dynamic" antibacterial. The "dynamic-static" synergistic antibacterial strategy greatly enhanced the antibacterial effect. AFBI E-skin could be used for self-powered motion monitoring to obtain a stable signal output even when water was splashed on its surface. Finally, based on AFBI E-skin, we constructed an ultra-long-range human-machine interaction control system, enabling synchronized hand gestures between human hand and robotic hand in any internet-covered area worldwide theoretically. AFBI E-skin exhibited vast application potential in fields like smart wearable electronics and intelligent robotics.

Passive Isothermal Flexible Sensor Enabled by Smart Thermal-Regulating Aerogels

Environmentally induced sensor temperature fluctuations can distort the outputs of a sensor, reducing their stability during long-term health monitoring. Here, a passive isothermal flexible sensor is proposed by using hierarchical cellulose aerogel (HCA) as the top tribonegative layer, which allows the sensor to adapt dynamic thermal environments through both radiative cooling and heat insulation. The radiative cooling effect can cool down the temperatures of a sensor in summer, while the hollow microfibers in HCA provide ultralow thermal conductivity to reduce internal heat loss in winter. The prepared passive isothermal sensor is capable of maintaining the rated working temperature over an extensive temperature range of 0-100 °C, demonstrating for gripping hot and cold objects. While monitoring human movements under direct sunlight, the temperature of a conventional sensor rose by 12.3 °C, whereas the sensor experienced an increase of only 0.3 °C. Therefore, this work presents a promising strategy for adapting to environments, enabling wearable electronics to function effectively in dynamic thermal conditions.

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.

3D printable and myoelectrically sensitive hydrogel for smart prosthetic hand control

Surface electromyogram (sEMG) serves as a means to discern human movement intentions, achieved by applying epidermal electrodes to specific body regions. However, it is difficult to obtain high-fidelity sEMG recordings in areas with intricate curved surfaces, such as the body, because regular sEMG electrodes have stiff structures. In this study, we developed myoelectrically sensitive hydrogels via 3D printing and integrated them into a stretchable, flexible, and high-density sEMG electrodes array. This electrode array offered a series of excellent human-machine interface (HMI) features, including conformal adherence to the skin, high electron-to-ion conductivity (and thus lower contact impedance), and sustained stability over extended periods. These attributes render our electrodes more conducive than commercial electrodes for long-term wearing and high-fidelity sEMG recording at complicated skin interfaces. Systematic in vivo studies were used to investigate its efficacy to control a prosthetic hand by decoding sEMG signals from the human hand via a multiple-channel readout circuit and a sophisticated artificial intelligence algorithm. Our findings demonstrate that the 3D printed gel myoelectric sensing system enables real-time and highly precise control of a prosthetic hand.

Recent Advances in Self-Powered Sensors Based on Ionic Hydrogels

After years of research and development, flexible sensors are gradually evolving from the traditional "electronic" paradigm to the "ionic" dimension. Smart flexible sensors derived from the concept of ion transport are gradually emerging in the flexible electronics. In particular, ionic hydrogels have increasingly become the focus of research on flexible sensors as a result of their tunable conductivity, flexibility, biocompatibility, and self-healable capabilities. Nevertheless, the majority of existing sensors based on ionic hydrogels still mainly rely on external power sources, which greatly restrict the dexterity and convenience of their applications. Advances in energy harvesting technologies offer substantial potential toward engineering self-powered sensors. This article reviews in detail the self-powered mechanisms of ionic hydrogel self-powered sensors (IHSSs), including piezoelectric, triboelectric, ionic diode, moist-electric, thermoelectric, potentiometric transduction, and hybrid modes. At the same time, structural engineering related to device and material characteristics is discussed. Additionally, the relevant applications of IHSS toward wearable electronics, human-machine interaction, environmental monitoring, and medical diagnostics are further reviewed. Lastly, the challenges and prospective advancement of IHSS are outlined.

Mechano-Graded Contact-Electrification Interfaces Based Artificial Mechanoreceptors for Robotic Adaptive Reception

Triboelectrification-based artificial mechanoreceptors (TBAMs) is able to convert mechanical stimuli directly into electrical signals, realizing self-adaptive protection and human-machine interactions of robots. However, traditional contact-electrification interfaces are prone to reaching their deformation limits under large pressures, resulting in a relatively narrow linear range. In this work, we fabricated mechano-graded microstructures to modulate the strain behavior of contact-electrification interfaces, simultaneously endowing the TBAMs with a high sensitivity and a wide linear detection range. The presence of step regions within the mechanically graded microstructures helps contact-electrification interfaces resist fast compressive deformation and provides a large effective area. The highly sensitive linear region of TBAM with 1.18 V/kPa can be effectively extended to four times of that for the devices with traditional interfaces. In addition, the device is able to maintain a high sensitivity of 0.44 V/kPa even under a large pressure from 40 to 600 kPa. TBAM has been successfully used as an electronic skin to realize self-adaptive protection and grip strength perception for a commercial robot arm. Finally, a high angle resolution of 2° and an excellent linearity of 99.78% for joint bending detection were also achieved. With the aid of a convolutional neural network algorithm, a data glove based on TBAMs realizes a high accuracy rate of 95.5% for gesture recognition in a dark environment.

An intelligent hybrid-fabric wristband system enabled by thermal encapsulation for ergonomic human-machine interaction

Human-machine interaction has emerged as a revolutionary and transformative technology, bridging the gap between human and machine. Gesture recognition, capitalizing on the inherent dexterity of human hands, plays a crucial role in human-machine interaction. However, existing systems often struggle to meet user expectations in terms of comfort, wearability, and seamless daily integration. Here, we propose a handwriting recognition technology utilizing an intelligent hybrid-fabric wristband system. This system integrates spun-film sensors into textiles to form the smart fabric, enabling intelligent functionalities. A thermal encapsulation process is proposed to bond multiple spun-films without additional materials, ensuring the lightweight, breathability, and stretchability of the spun-film sensors. Furthermore, recognition algorithms facilitate precise accurate handwriting recognition of letters, with an accuracy of 96.63%. This system represents a significant step forward in the development of ergonomic and user-friendly wearable devices for enhanced human-machine interaction, particularly in the virtual world.

Triboelectric-Inertial Sensing Glove Enhanced by Charge-Retained Strategy for Human-Machine Interaction

As technology advances, human-machine interaction (HMI) demands more intuitive and natural methods. To meet this demand, smart gloves, capable of capturing intricate hand movements, are emerging as vital HMI tools. Moreover, triboelectric-based sensors, with their self-powered, cost-effective, and material various characteristics, can offer promising solutions for enhancing existing glove systems. However, a key limitation of these sensors is that charge leakage in the measurement circuit results in capturing only transient signals, rather than continuous changes. To address this issue, a charge-retained circuit that effectively prevents triboelectric signal attenuation is developed, enabling accurate measurement of continuous finger movements. This innovation forms the foundation of a highly integrated smart glove system, enhancing HMI functionality by combining continuous triboelectric signals with inertial sensor data. The system showcases a diverse range of applications, including complex robotic control, virtual reality interaction, smart home lighting adjustments, and intuitive interface operations. Furthermore, by leveraging artificial intelligence (AI) techniques, the system achieves accurate recognition of complex sign language with an impressive 99.38% accuracy. This work presents a charge-retained approach for continuous sensing with triboelectric-based sensors, offering valuable insights for developing future multifunctional HMI and sign language recognition systems. The proposed smart glove system, with its dual-mode sensing and AI integration, holds great potential for revolutionizing various domains and enhancing user experiences.

Robust Sodium Carboxymethyl Cellulose-Based Neuromorphic Device with High Biocompatibility Engineered through Molecular Polarization for the Emulation of Learning Behaviors in the Human Brain

Sodium carboxymethyl cellulose (CMC-Na), derived from natural cellulose and frequently employed as a biocompatible coating, thus renders it an ideal component for the construction of highly biocompatible neuromorphic devices aimed at biomachine interfaces. Here, an array of Mo/CMC-Na/ITO neuromorphic devices is fabricated, with CMC-Na serving as the functional layer. The devices exhibit capabilities to emulate various synaptic learning rules and demonstrate high endurance performance among biomaterial-based electronics, achieving stability over 2 × 104 pulses. Then, simulations of human brain-inspired learning and forgetting paradigms are conducted, highlighting the versatility of the device array in mimicking learning processes. Applications in pattern recognition leverage "learning-forgetting" paradigms, showcasing the potential of the device in cognitive tasks. Electrical measurements elucidate the mechanism of molecular polarization rotation, which offers insights into the modulation of synaptic weights within biocompatible biomaterial-based devices. Furthermore, the biocompatible properties of the devices are evaluated using human embryonic kidney 293 cells, confirming their excellent biocompatibility. The biodegradability of the devices is assessed by using physical transient tests to evaluate their sustainability in biomedical applications. Such advances represent pivotal improvements in implantable bioinspired electronics and show potential in biomachine interface and cognitive computing applications.

Mormyroidea-inspired electronic skin for active non-contact three-dimensional tracking and sensing

The capacity to discern and locate positions in three-dimensional space is crucial for human-machine interfaces and robotic perception. However, current soft electronics can only obtain two-dimensional spatial locations through physical contact. In this study, we report a non-contact position targeting concept enabled by transparent and thin soft electronic skin (E-skin) with three-dimensional sensing capabilities. Inspired by the active electrosensation of mormyroidea fish, this E-skin actively ascertains the 3D positions of targeted objects in a contactless manner and can wirelessly convey the corresponding positions to other devices in real-time. Consequently, this E-skin readily enables interaction with machines, i.e., manipulating virtual objects, controlling robotic arms, and drones in either virtual or actual 3D space. Additionally, it can be integrated with robots to provide them with 3D situational awareness for perceiving their surroundings, avoiding obstacles, or tracking targets.

A fully integrated breathable haptic textile

Wearable haptics serve as an enhanced media to connect humans and VR/robots. The inevitable sweating issue in all wearables creates a bottleneck for wearable haptics, as the sweat/moisture accumulated in the skin/device interface can substantially affect feedback accuracy, comfortability, and create hygienic problems. Nowadays, wearable haptics typically gain performance at the cost of sacrificing the breathability, comfort, and biocompatibility. Here, we developed a fully integrated breathable haptic textile (FIBHT) to solve these trade-off issues, where the FIBHT exhibits high-level integration of 128 pixels over the palm, great stretchability of 400%, and superior permeability of over 657 g/m2/day (moisture) and 40 mm/s (air). It is a stand-alone haptic system totally composed of stretchable, breathable, and bioadhesive materials, which empowers it with precise, sweating/movement-insensitive and dynamic feedback, and makes FIBHT powerful for virtual touching in broad scenarios.

A Rapid Adaptation Approach for Dynamic Air-Writing Recognition Using Wearable Wristbands with Self-Supervised Contrastive Learning

Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities. Unlike existing approaches that often focus on static gestures and require extensive labeled data, the proposed wearable wristband with self-supervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios. It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes, resulting in high-sensitivity capacitance output. Through wireless transmission from a Wi-Fi module, the proposed algorithm learns latent features from the unlabeled signals of random wrist movements. Remarkably, only few-shot labeled data are sufficient for fine-tuning the model, enabling rapid adaptation to various tasks. The system achieves a high accuracy of 94.9% in different scenarios, including the prediction of eight-direction commands, and air-writing of all numbers and letters. The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training. Its utility has been further extended to enhance human-machine interaction over digital platforms, such as game controls, calculators, and three-language login systems, offering users a natural and intuitive way of communication.

Encapsulated stretchable amphibious strain sensors

Soft and stretchable strain sensors have found wide applications in health monitoring, motion tracking, and robotic sensing. There is a growing demand for strain sensors in amphibious environments, such as implantable sensors, wearable sensors for swimmers/divers, and underwater robotic sensors. However, developing a sensitive, stretchable, and robust amphibious strain sensor remains challenging. This work presents an encapsulated stretchable amphibious strain sensor. The conductive layer, made of silver nanowires embedded below the surface of polydimethylsiloxane, was sandwiched by two layers of thermoplastic polyurethane. Periodic sharp cuts were introduced to change the direction of flow from across the sensor to along the conductive path defined by the opening cracks. The crack advancing and opening is controlled by a unique combination of weak/strong interfaces within the sandwich structure. The cut design and the interfacial interactions between the layers were investigated. The strain sensor exhibited a high gauge factor up to 289, a linear sensing response, a fast response time (53 ms), excellent robustness against over-strain, and stability after 16 000 loading cycles and 20 days in an aqueous saline solution. The functionality of this amphibious strain sensor was demonstrated by tracking the motion of a robotic fish, undertaking language recognition underwater, and monitoring the blood pressure of a porcine aorta. This illustrates the promising potential for this strain sensor for both underwater use and surgically implantable applications.

Tactile Sensing and Rendering Patch with Dynamic and Static Sensing and Haptic Feedback for Immersive Communication

Wearable human-machine interface (HMI) with bidirectional and multimodal tactile information exchange is of paramount importance in teleoperation by providing more intuitive data interpretation and delivery of tactilely related signals. However, the current sensing and feedback devices still lack enough integration and modalities. Here, we present a Tactile Sensing and Rendering Patch (TSRP) that is made of a customized expandable array which consists of a piezoelectric sensing and feedback unit fused with an elastomeric triboelectric multidimensional sensor and its inner pneumatic feedback structure. The primary functional unit of TSRP is mainly featured with a soft silicone substrate with compact multilayer structure integrating static and dynamic multidimensional tactile sensing capabilities, which synergistically leverage both triboelectric and piezoelectric effects. Additionally, based on the air chamber created by the triboelectric sensor and the converse piezoelectric effect, it provides pneumatic and vibrational haptic feedback simultaneously for both static and dynamic perception regeneration. With the aid of the other variants of this unit, the array shaped TSRP is capable of simulating different terrains, geometries, sliding, collisions, and other critical interactive events during teleoperation via skin perception. Moreover, immediate manipulation can be done on TSRP through the tactile sensors. The preliminary demonstration of TSRP interface with a completed control module in robotic teleoperation is provided, which shows the feasibility of assisting certain tasks in a complex environment by direct tactile communication. The proposed device offers a potential method of enabling bidirectional tactile communication with enriched key information for improving interaction efficiency in the fields of robot teleoperation and training.

Multimodal Intelligent Flooring System for Advanced Smart-Building Monitoring and Interactions

The floor constitutes one of the largest areas within a building with which users interact most frequently in daily activities. Employing floor sensors is vital for smart-building digital twins, wherein triboelectric nanogenerators demonstrate wide application potential due to their good performance and self-powering characteristics. However, their sensing stability, reliability, and multimodality require further enhancement to meet the rapidly evolving demands. Thus, this work introduces a multimodal intelligent flooring system, implementing a 4 × 4 floor array for multimodal information detection (including position, pressure, material, user identity, and activity) and human-machine interactions. The floor unit incorporates a hybrid structure of triboelectric pressure sensors and a top-surface material sensor, facilitating linear and enhanced sensitivity across a wide pressure range (0-800 N), alongside the material recognition capability. The floor array is implemented by an advanced output-ratio method with minimalist output channels, which is insensitive to environmental factors such as humidity and temperature. In addition to multimodal sensing, energy harvesting is co-designed with the pressure sensors for scavenging waste energy to power smart-building sensor nodes. This developed flooring system enables multimodal sensing, energy harvesting, and smart-sport interactions in smart buildings, greatly expanding the floor sensing scenarios and applications.

Dual Interactive Mode Human-Machine Interfaces Based on Triboelectric Nanogenerator and IGZO/In2O3 Heterojunction Synaptic Transistor

Imitating human neural networks via bio-inspired electronics advances human-machine interfaces (HMI), overcoming von Neumann limitations and enabling efficient, low-energy data processing in the big data era. However, single-contact mode HMIs have inherent limitations in terms of their capabilities and performances, such as constrained adaptability to dynamic environments, and reduced cognitive processing capabilities. Here, a dual-interactive-mode HMI system based on a triboelectric nanogenerator (TENG) and heterojunction synaptic transistor (HJST) is proposed for both contact and non-contact applications. The TENG incorporates a poly-methyl meth-acrylate (PMMA)-NiCo2S4/S film, in which the NiCo2S4/S composite traps and blocks electrons to optimize charge generation and storage. The heterojunction structure, mitigates the Debye screening effect, thereby improving transistor characteristics and reliability. The integrated TENG-HJST system exhibits synaptic functions, including excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation/depression (PPF/PPD), and synaptic plasticity, enabling emulation of neural behavior and advanced information processing. Moreover, neural morphology manipulation is demonstrated in practical tasks, such as controlling international chess games. By integrating the TENG-HJST device with a robotic hand, conscious artificial responses are generated, enhancing event accuracy. This breakthrough in dual-interactive-mode interfacing holds promise for HMI systems and neural prostheses.

Toward an AI Era: Advances in Electronic Skins

Electronic skins (e-skins) have seen intense research and rapid development in the past two decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors that detect physiological and environmental signals have been designed and integrated into functional systems. Recently, researchers have increasingly deployed machine learning and other artificial intelligence (AI) technologies to mimic the human neural system for the processing and analysis of sensory data collected by e-skins. Integrating AI has the potential to enable advanced applications in robotics, healthcare, and human-machine interfaces but also presents challenges such as data diversity and AI model robustness. In this review, we first summarize the functions and features of e-skins, followed by feature extraction of sensory data and different AI models. Next, we discuss the utilization of AI in the design of e-skin sensors and address the key topic of AI implementation in data processing and analysis of e-skins to accomplish a range of different tasks. Subsequently, we explore hardware-layer in-skin intelligence before concluding with an analysis of the challenges and opportunities in the various aspects of AI-enabled e-skins.

Three-dimensional micro strain gauges as flexible, modular tactile sensors for versatile integration with micro- and macroelectronics

Flexible tactile sensors play important roles in many areas, like human-machine interface, robotic manipulation, and biomedicine. However, their flexible form factor poses challenges in their integration with wafer-based devices, commercial chips, or circuit boards. Here, we introduce manufacturing approaches, device designs, integration strategies, and biomedical applications of a set of flexible, modular tactile sensors, which overcome the above challenges and achieve cooperation with commercial electronics. The sensors exploit lithographically defined thin wires of metal or alloy as the sensing elements. Arranging these elements across three-dimensional space enables accurate, hysteresis-free, and decoupled measurements of temperature, normal force, and shear force. Assembly of such sensors on flexible printed circuit boards together with commercial electronics forms various flexible electronic systems with capabilities in wireless measurements at the skin interface, continuous monitoring of biomechanical signals, and spatial mapping of tactile information. The flexible, modular tactile sensors expand the portfolio of functional components in both microelectronics and macroelectronics.

Self-Healing Materials for Bioelectronic Devices

Though the history of self-healing materials stretches far back to the mid-20th century, it is only in recent years where such unique classes of materials have begun to find use in bioelectronics-itself a burgeoning area of research. Inspired by the natural ability of biological tissue to self-repair, self-healing materials play a multifaceted role in the context of soft, wireless bioelectronic systems, in that they can not only serve as a protective outer shell or substrate for the internal electronic circuitry-analogous to the mechanical barrier that skin provides for the human body-but also, and most importantly, act as an active sensing safeguard against mechanical damage to preserve device functionality and enhance overall durability. This perspective presents the historical overview, general design principles, recent developments, and future outlook of self-healing materials for bioelectronic devices, which integrates topics in many research disciplines-from materials science and chemistry to electronics and bioengineering-together.

Creep-free polyelectrolyte elastomer for drift-free iontronic sensing

Artificial pressure sensors often use soft materials to achieve skin-like softness, but the viscoelastic creep of soft materials and the ion leakage, specifically for ionic conductors, cause signal drift and inaccurate measurement. Here we report drift-free iontronic sensing by designing and copolymerizing a leakage-free and creep-free polyelectrolyte elastomer containing two types of segments: charged segments having fixed cations to prevent ion leakage and neutral slippery segments with a high crosslink density for low creep. We show that an iontronic sensor using the polyelectrolyte elastomer barely drifts under an ultrahigh static pressure of 500 kPa (close to its Young's modulus), exhibits a drift rate two to three orders of magnitude lower than that of the sensors adopting conventional ionic conductors and enables steady and accurate control for robotic manipulation. Such drift-free iontronic sensing represents a step towards highly accurate sensing in robotics and beyond.

Monoolein-Based Wireless Capacitive Sensor for Probing Skin Hydration

Capacitive humidity sensors typically consist of interdigitated electrodes coated with a dielectric layer sensitive to varying relative humidity levels. Previous studies have investigated different polymeric materials that exhibit changes in conductivity in response to water vapor to design capacitive humidity sensors. However, lipid films like monoolein have not yet been integrated with humidity sensors, nor has the potential use of capacitive sensors for skin hydration measurements been fully explored. This study explores the application of monoolein-coated wireless capacitive sensors for assessing relative humidity and skin hydration, utilizing the sensitive dielectric properties of the monoolein-water system. This sensitivity hinges on the water absorption and release from the surrounding environment. Tested across various humidity levels and temperatures, these novel double functional sensors feature interdigitated electrodes covered with monoolein and show promising potential for wireless detection of skin hydration. The water uptake and rheological behavior of monoolein in response to humidity were evaluated using a quartz crystal microbalance with dissipation monitoring. The findings from these experiments suggest that the capacitance of the system is primarily influenced by the amount of water in the monoolein system, with the lyotropic or physical state of monoolein playing a secondary role. A proof-of-principle demonstration compared the sensor's performance under varying conditions to that of other commercially available skin hydration meters, affirming its effectiveness, reliability, and commercial viability.

Data-driven inverse design of flexible pressure sensors

Artificial skins or flexible pressure sensors that mimic human cutaneous mechanoreceptors transduce tactile stimuli to quantitative electrical signals. Conventional trial-and-error designs for such devices follow a forward structure-to-property routine, which is usually time-consuming and determines one possible solution in one run. Data-driven inverse design can precisely target desired functions while showing far higher productivity, however, it is still absent for flexible pressure sensors because of the difficulties in acquiring a large amount of data. Here, we report a property-to-structure inverse design of flexible pressure sensors, exhibiting a significantly greater efficiency than the conventional routine. We use a reduced-order model that analytically constrains the design scope and an iterative "jumping-selection" method together with a surrogate model that enhances data screening. As an exemplary scenario, hundreds of solutions that overcome the intrinsic signal saturation have been predicted by the inverse method, validating for a variety of material systems. The success in property design on multiple indicators demonstrates that the proposed inverse design is an efficient and powerful tool to target multifarious applications of flexible pressure sensors, which can potentially advance the fields of intelligent robots, advanced healthcare, and human-machine interfaces.

Neuromorphic Computing-Assisted Triboelectric Capacitive-Coupled Tactile Sensor Array for Wireless Mixed Reality Interaction

Flexible tactile sensors show promise for artificial intelligence applications due to their biological adaptability and rapid signal perception. Triboelectric sensors enable active dynamic tactile sensing, while integrating static pressure sensing and real-time multichannel signal transmission is key for further development. Here, we propose an integrated structure combining a capacitive sensor for static spatiotemporal mapping and a triboelectric sensor for dynamic tactile recognition. A liquid metal-based flexible dual-mode triboelectric-capacitive-coupled tactile sensor (TCTS) array of 4 × 4 pixels achieves a spatial resolution of 7 mm, exhibiting a pressure detection limit of 0.8 Pa and a fast response of 6 ms. Furthermore, neuromorphic computing using the MXene-based synaptic transistor achieves 100% recognition accuracy of handwritten numbers/letters within 90 epochs based on dynamic triboelectric signals collected by the TCTS array, and cross-spatial information communication from the perceived multichannel tactile data is realized in the mixed reality space. The results illuminate considerable application possibilities of dual-mode tactile sensing technology in human-machine interfaces and advanced robotics.

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.

Electronic Skin: Opportunities and Challenges in Convergence with Machine Learning

Recent advancements in soft electronic skin (e-skin) have led to the development of human-like devices that reproduce the skin's functions and physical attributes. These devices are being explored for applications in robotic prostheses as well as for collecting biopotentials for disease diagnosis and treatment, as exemplified by biomedical e-skins. More recently, machine learning (ML) has been utilized to enhance device control accuracy and data processing efficiency. The convergence of e-skin technologies with ML is promoting their translation into clinical practice, especially in healthcare. This review highlights the latest developments in ML-reinforced e-skin devices for robotic prostheses and biomedical instrumentations. We first describe technological breakthroughs in state-of-the-art e-skin devices, emphasizing technologies that achieve skin-like properties. We then introduce ML methods adopted for control optimization and pattern recognition, followed by practical applications that converge the two technologies. Lastly, we briefly discuss the challenges this interdisciplinary research encounters in its clinical and industrial transition.

Use of Artificial Intelligence Techniques to Assist Individuals with Physical Disabilities

Assistive technologies (AT) enable people with disabilities to perform activities of daily living more independently, have greater access to community and healthcare services, and be more productive performing educational and/or employment tasks. Integrating artificial intelligence (AI) with various agents, including electronics, robotics, and software, has revolutionized AT, resulting in groundbreaking technologies such as mind-controlled exoskeletons, bionic limbs, intelligent wheelchairs, and smart home assistants. This article provides a review of various AI techniques that have helped those with physical disabilities, including brain-computer interfaces, computer vision, natural language processing, and human-computer interaction. The current challenges and future directions for AI-powered advanced technologies are also addressed.

Advanced Design of Soft Robots with Artificial Intelligence

A comprehensive review focused on the whole systems of the soft robotics with artificial intelligence, which can feel, think, react and interact with humans, is presented. The design strategies concerning about various aspects of the soft robotics, like component materials, device structures, prepared technologies, integrated method, and potential applications, are summarized. A broad outlook on the future considerations for the soft robots is proposed.

In recent years, breakthrough has been made in the field of artificial intelligence (AI), which has also revolutionized the industry of robotics. Soft robots featured with high-level safety, less weight, lower power consumption have always been one of the research hotspots. Recently, multifunctional sensors for perception of soft robotics have been rapidly developed, while more algorithms and models of machine learning with high accuracy have been optimized and proposed. Designs of soft robots with AI have also been advanced ranging from multimodal sensing, human–machine interaction to effective actuation in robotic systems. Nonetheless, comprehensive reviews concerning the new developments and strategies for the ingenious design of the soft robotic systems equipped with AI are rare. Here, the new development is systematically reviewed in the field of soft robots with AI. First, background and mechanisms of soft robotic systems are briefed, after which development focused on how to endow the soft robots with AI, including the aspects of feeling, thought and reaction, is illustrated. Next, applications of soft robots with AI are systematically summarized and discussed together with advanced strategies proposed for performance enhancement. Design thoughts for future intelligent soft robotics are pointed out. Finally, some perspectives are put forward.

Electrostatic Smart Textiles for Braille-To-Speech Translation

A wearable Braille-to-speech translation system is of great importance for providing auditory feedback in assisting blind people and people with speech impairment. However, previous reported Braille-to-speech translation systems still need to be improved in terms of comfortability or integration. Here, a Braille-to-speech translation system that uses dual-functional electrostatic transducers which are made of fabric-based materials and can be integrated into textiles is reported. Based on electrostatic induction, the electrostatic transducer can either serve as a tactile sensor or a loudspeaker with the same design. The proposed electrostatic transducers have excellent output performances, mechanical robustness, and working stability. By combining the devices with machine learning algorithms, it is possible to translate the Braille alphabet and 40 commonly used words (extensible) into speech with an accuracy of 99.09% and 97.08%, respectively. This work demonstrates a new approach for further developments of advanced assistive technology toward improving the lives of disabled people.

A Soft Collaborative Robot for Contact-based Intuitive Human Drag Teaching

Soft material-based robots, known for their safety and compliance, are expected to play an irreplaceable role in human-robot collaboration. However, this expectation is far from real industrial applications due to their complex programmability and poor motion precision, brought by the super elasticity and large hysteresis of soft materials. Here, a soft collaborative robot (Soft Co-bot) with intuitive and easy programming by contact-based drag teaching, and also with exceptional motion repeatability (< 0.30% of body length) and ultra-low hysteresis (< 2.0%) is reported. Such an unprecedented capability is achieved by a biomimetic antagonistic design within a pneumatic soft robot, in which cables are threaded to servo motors through tension sensors to form a self-sensing system, thus providing both precise actuation and dragging-aware collaboration. Hence, the Soft Co-bots can be first taught by human drag and then precisely repeat various tasks on their own, such as electronics assembling, machine tool installation, etc. The proposed Soft Co-bots exhibit a high potential for safe and intuitive human-robot collaboration in unstructured environments, promoting the immediate practical application of soft robots.

Strain-invariant stretchable radio-frequency electronics

Wireless modules that provide telecommunications and power-harvesting capabilities enabled by radio-frequency (RF) electronics are vital components of skin-interfaced stretchable electronics1-7. However, recent studies on stretchable RF components have demonstrated that substantial changes in electrical properties, such as a shift in the antenna resonance frequency, occur even under relatively low elastic strains8-15. Such changes lead directly to greatly reduced wireless signal strength or power-transfer efficiency in stretchable systems, particularly in physically dynamic environments such as the surface of the skin. Here we present strain-invariant stretchable RF electronics capable of completely maintaining the original RF properties under various elastic strains using a 'dielectro-elastic' material as the substrate. Dielectro-elastic materials have physically tunable dielectric properties that effectively avert frequency shifts arising in interfacing RF electronics. Compared with conventional stretchable substrate materials, our material has superior electrical, mechanical and thermal properties that are suitable for high-performance stretchable RF electronics. In this paper, we describe the materials, fabrication and design strategies that serve as the foundation for enabling the strain-invariant behaviour of key RF components based on experimental and computational studies. Finally, we present a set of skin-interfaced wireless healthcare monitors based on strain-invariant stretchable RF electronics with a wireless operational distance of up to 30 m under strain.

Ultrafast piezocapacitive soft pressure sensors with over 10 kHz bandwidth via bonded microstructured interfaces

Flexible pressure sensors can convert mechanical stimuli to electrical signals to interact with the surroundings, mimicking the functionality of the human skins. Piezocapacitive pressure sensors, a class of most widely used devices for artificial skins, however, often suffer from slow response-relaxation speed (tens of milliseconds) and thus fail to detect dynamic stimuli or high-frequency vibrations. Here, we show that the contact-separation behavior of the electrode-dielectric interface is an energy dissipation process that substantially determines the response-relaxation time of the sensors. We thus reduce the response and relaxation time to ~0.04 ms using a bonded microstructured interface that effectively diminishes interfacial friction and energy dissipation. The high response-relaxation speed allows the sensor to detect vibrations over 10 kHz, which enables not only dynamic force detection, but also acoustic applications. This sensor also shows negligible hysteresis to precisely track dynamic stimuli. Our work opens a path that can substantially promote the response-relaxation speed of piezocapacitive pressure sensors into submillisecond range and extend their applications in acoustic range.

SORI: A softness-rendering interface to unravel the nature of softness perception

Tactile perception of softness serves a critical role in the survival, well-being, and social interaction among various species, including humans. This perception informs activities from food selection in animals to medical palpation for disease detection in humans. Despite its fundamental importance, a comprehensive understanding of how softness is neurologically and cognitively processed remains elusive. Previous research has demonstrated that the somatosensory system leverages both cutaneous and kinesthetic cues for the sensation of softness. Factors such as contact area, depth, and force play a particularly critical role in sensations experienced at the fingertips. Yet, existing haptic technologies designed to explore this phenomenon are limited, as they often couple force and contact area, failing to provide a real-world experience of softness perception. Our research introduces the softness-rendering interface (SORI), a haptic softness display designed to bridge this knowledge gap. Unlike its predecessors, SORI has the unique ability to decouple contact area and force, thereby allowing for a quantitative representation of softness sensations at the fingertips. Furthermore, SORI incorporates individual physical fingertip properties and model-based softness cue estimation and mapping to provide a highly personalized experience. Utilizing this method, SORI quantitatively replicates the sensation of softness on stationary, dynamic, homogeneous, and heterogeneous surfaces. We demonstrate that SORI accurately renders the surfaces of both virtual and daily objects, thereby presenting opportunities across a range of fields, from teleoperation to medical technology. Finally, our proposed method and SORI will expedite psychological and neuroscience research to unlock the nature of softness perception.