Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions

Predicting concrete strength early age using a combination of machine learning and electromechanical impedance with nano-enhanced sensors

To ensure the structural integrity of concrete and prevent unanticipated fracturing, real-time monitoring of early-age concrete's strength development is essential, mainly through advanced techniques such as nano-enhanced sensors. The piezoelectric-based electro-mechanical impedance (EMI) method with nano-enhanced sensors is emerging as a practical solution for such monitoring requirements. This study presents a strength estimation method based on Non-Destructive Testing (NDT) Techniques and Long Short-Term Memory (LSTM) and artificial neural networks (ANNs) as hybrid (NDT-LSTMs-ANN), including several types of concrete strength-related agents. Input data includes water-to-cement rate, temperature, curing time, and maturity based on interior temperature, allowing experimentally monitoring the development of concrete strength from the early steps of hydration and casting to the last stages of hardening 28 days after the casting. The study investigated the impact of various factors on concrete strength development, utilizing a cutting-edge approach that combines traditional models with nano-enhanced piezoelectric sensors and NDT-LSTMs-ANN enhanced with nanotechnology. The results demonstrate that the hybrid provides highly accurate concrete strength estimation for construction safety and efficiency. Adopting the piezoelectric-based EMI technique with these advanced sensors offers a viable and effective monitoring solution, presenting a significant leap forward for the construction industry's structural health monitoring practices.

Intelligent wearable olfactory interface for latency-free mixed reality and fast olfactory enhancement

Olfaction feedback systems could be utilized to stimulate human emotion, increase alertness, provide clinical therapy, and establish immersive virtual environments. Currently, the reported olfaction feedback technologies still face a host of formidable challenges, including human perceivable delay in odor manipulation, unwieldy dimensions, and limited number of odor supplies. Herein, we report a general strategy to solve these problems, which associates with a wearable, high-performance olfactory interface based on miniaturized odor generators (OGs) with advanced artificial intelligence (AI) algorithms. The OGs serve as the core technology of the intelligent olfactory interface, which exhibit milestone advances in millisecond-level response time, milliwatt-scale power consumption, and the miniaturized size. Empowered by robust AI algorithms, the olfactory interface shows its great potentials in latency-free mixed reality (MR) and fast olfaction enhancement, thereby establishing a bridge between electronics and users for broad applications ranging from entertainment, to education, to medical treatment, and to human machine interfaces.

Permeable Bioelectronics toward Biointegrated Systems

Bioelectronics integrates electronics with biological organs, sustaining the natural functions of the organs. Organs dynamically interact with the external environment, managing internal equilibrium and responding to external stimuli. These interactions are crucial for maintaining homeostasis. Additionally, biological organs possess a soft and stretchable nature; encountering objects with differing properties can disrupt their function. Therefore, when electronic devices come into contact with biological objects, the permeability of these devices, enabling interactions and substance exchanges with the external environment, and the mechanical compliance are crucial for maintaining the inherent functionality of biological organs. This review discusses recent advancements in soft and permeable bioelectronics, emphasizing materials, structures, and a wide range of applications. The review also addresses current challenges and potential solutions, providing insights into the integration of electronics with biological organs.

All-Printed Finger-Inspired Tactile Sensor Array for Microscale Texture Detection and 3D Reconstruction

Electronic skins are expected to replicate a human-like tactile sense, which significantly detects surface information, including geometry, material, and temperature. Although most texture features can be sensed in the horizontal direction, the lack of effective approaches for detecting vertical properties limits the development of artificial skin based on tactile sensors. In this study, an all-printed finger-inspired tactile sensor array is developed to realize the 3D detection and reconstruction of microscale structures. A beam structure with a suspended multilayer membrane is proposed, and a tactile sensor array of 12 units arranged in a dual-column layout is developed. This architecture enables the tactile sensor array to obtain comprehensive geometric information of micro-textures, including 3D morphology and clearance characteristics, and optimizes the 3D reconstruction patterns by self-calibration. Moreover, an innovative screen-printing technology incorporating multilayer printing and sacrificial-layer techniques is adopted to print the entire device. In additon, a Braille recognition system utilizing this tactile sensor array is developed to interpret Shakespeare's quotes printed in Grade 2 Braille. The abovementioned demonstrations reveal an attractive future vision for endowing bioinspired robots with the unique capability of touching and feeling the microscale real world and reconstructing it in the cyber world.

Multimodal Sensors Enabled Autonomous Soft Robotic System with Self-Adaptive Manipulation

Human hands are amazingly skilled at recognizing and handling objects of different sizes and shapes. To date, soft robots rarely demonstrate autonomy equivalent to that of humans for fine perception and dexterous operation. Here, an intelligent soft robotic system with autonomous operation and multimodal perception ability is developed by integrating capacitive sensors with triboelectric sensor. With distributed multiple sensors, our robot system can not only sense and memorize multimodal information but also enable an adaptive grasping method for robotic positioning and grasp control, during which the multimodal sensory information can be captured sensitively and fused at feature level for crossmodally recognizing objects, leading to a highly enhanced recognition capability. The proposed system, combining the performance and physical intelligence of biological systems (i.e., self-adaptive behavior and multimodal perception), will greatly advance the integration of soft actuators and robotics in many fields.

AI-Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.

Single Channel Based Interference-Free and Self-Powered Human-Machine Interactive Interface Using Eigenfrequency-Dominant Mechanism

The recent development of wearable devices is revolutionizing the way of human-machine interaction (HMI). Nowadays, an interactive interface that carries more embedded information is desired to fulfill the increasing demand in era of Internet of Things. However, present approach normally relies on sensor arrays for memory expansion, which inevitably brings the concern of wiring complexity, signal differentiation, power consumption, and miniaturization. Herein, a one-channel based self-powered HMI interface, which uses the eigenfrequency of magnetized micropillar (MMP) as identification mechanism, is reported. When manually vibrated, the inherent recovery of the MMP causes a damped oscillation that generates current signals because of Faraday's Law of induction. The time-to-frequency conversion explores the MMP-related eigenfrequency, which provides a specific solution to allocate diverse commands in an interference-free behavior even with one electric channel. A cylindrical cantilever model is built to regulate the MMP eigenfrequencies via precisely designing the dimensional parameters and material properties. It is shown that using one device and two electrodes, high-capacity HMI interface can be realized when the magnetic micropillars (MMPs) with different eigenfrequencies have been integrated. This study provides the reference value to design the future HMI system especially for situations that require a more intuitive and intelligent communication experience with high-memory demand.

Cardio-oncology rehabilitation: are we ready?

Cardio-oncology rehabilitation (CORE) is not only an essential component of cancer rehabilitation but also a pillar of preventive cardio-oncology. Cardio-oncology rehabilitation is a comprehensive model based on a multitargeted approach and its efficacy has been widely documented; when compared with an 'exercise only' programme, comprehensive CORE demonstrates a better outcome. It involves nutritional counselling, psychological support, and cardiovascular (CV) risk assessment, and it is directed to a very demanding population with a heavy burden of CV diseases driven by physical inactivity, cancer therapy-induced metabolic derangements, and cancer therapy-related CV toxicities. Despite its usefulness, CORE is still underused in cancer patients and we are still at the dawning of remote models of rehabilitation (tele-rehabilitation). Not all CORE is created equally: a careful screening procedure to identify patients who will benefit the most from CORE and a multidisciplinary customized approach are mandatory to achieve a better outcome for cancer survivors throughout their cancer journey. The aim of this paper is to provide an updated review of CORE not only for cardiologists dealing with this peculiar population of patients but also for oncologists, primary care providers, patients, and caregivers. This multidisciplinary team should help cancer patients to maintain a healthy and active life before, during, and after cancer treatment, in order to improve quality of life and to fight health inequities.

Perceived Realism of Virtual Textures Rendered by a Vibrotactile Wearable Ring Display

Wearable haptic displays that relocate feedback away from the fingertip provide a much-needed sense of touch to interactions in virtual reality, while also leaving the fingertip free from occlusion for augmented reality tasks. However, the impact of relocation on perceptual sensitivity to dynamic changes in actuation during active movement remains unclear. In this work, we investigate the perceived realism of virtual textures rendered via vibrations relocated to the base of the index finger and compare three different methods of modulating vibrations with active finger speed. For the first two methods, changing finger speed induced proportional changes in either frequency or amplitude of vibration, and for the third method did not modulate vibration. In psychophysical experiments, participants compared different types of modulation to each other, as well as to real 3D-printed textured surfaces. Results suggest that frequency modulation results in more realistic sensations for coarser textures, whereas participants were less discerning of modulation type for finer textures. Additionally, we presented virtual textures either fully virtually in midair or under augmented reality in which the finger contacted a flat surface; while we found no difference in experimental performance, participants were divided by a strong preference for either the contact or non-contact condition.

High Strength and Toughness Polymeric Triboelectric Materials Enabled by Dense Crystal-Domain Cross-Linking

Lightweight, easily processed, and durable polymeric materials play a crucial role in wearable sensor devices. However, achieving simultaneously high strength and toughness remains a challenge. This study addresses this by utilizing an ion-specific effect to control crystalline domains, enabling the fabrication of a polymeric triboelectric material with tunable mechanical properties. The dense crystal-domain cross-linking enhances energy dissipation, resulting in a material boasting both high tensile strength (58.0 MPa) and toughness (198.8 MJ m-3), alongside a remarkable 416.7% fracture elongation and 545.0 MPa modulus. Leveraging these properties, the material is successfully integrated into wearable self-powered devices, enabling real-time feedback on human joint movement. This work presents a valuable strategy for overcoming the strength-toughness trade-off in polymeric materials, paving the way for their enhanced applicability and broader use in diverse sensing applications.

Haptic Sensing and Feedback Techniques toward Virtual Reality

Haptic interactions between human and machines are essential for information acquisition and object manipulation. In virtual reality (VR) system, the haptic sensing device can gather information to construct virtual elements, while the haptic feedback part can transfer feedbacks to human with virtual tactile sensation. Therefore, exploring high-performance haptic sensing and feedback interface imparts closed-loop haptic interaction to VR system. This review summarizes state-of-the-art VR-related haptic sensing and feedback techniques based on the hardware parts. For the haptic sensor, we focus on mechanism scope (piezoresistive, capacitive, piezoelectric, and triboelectric) and introduce force sensor, gesture translation, and touch identification in the functional view. In terms of the haptic feedbacks, methodologies including mechanical, electrical, and elastic actuators are surveyed. In addition, the interactive application of virtual control, immersive entertainment, and medical rehabilitation is also summarized. The challenges of virtual haptic interactions are given including the accuracy, durability, and technical conflicts of the sensing devices, bottlenecks of various feedbacks, as well as the closed-loop interaction system. Besides, the prospects are outlined in artificial intelligence of things, wise information technology of medicine, and multimedia VR areas.

Advanced Dielectric Materials for Triboelectric Nanogenerators: Principles, Methods, and Applications

Triboelectric nanogenerator (TENG) manifests distinct advantages such as multiple structural selectivity, diverse selection of materials, environmental adaptability, low cost, and remarkable conversion efficiency, which becomes a promising technology for micro-nano energy harvesting and self-powered sensing. Tribo-dielectric materials are the fundamental and core components for high-performance TENGs. In particular, the charge generation, dissipation, storage, migration of the dielectrics, and dynamic equilibrium behaviors determine the overall performance. Herein, a comprehensive summary is presented to elucidate the dielectric charge transport mechanism and tribo-dielectric material modification principle toward high-performance TENGs. The contact electrification and charge transport mechanism of dielectric materials is started first, followed by introducing the basic principle and dielectric materials of TENGs. Subsequently, modification mechanisms and strategies for high-performance tribo-dielectric materials are highlighted regarding physical/chemical, surface/bulk, dielectric coupling, and structure optimization. Furthermore, representative applications of dielectric materials based TENGs as power sources, self-powered sensors are demonstrated. The existing challenges and promising potential opportunities for advanced tribo-dielectric materials are outlined, guiding the design, fabrication, and applications of tribo-dielectric materials.

High Signal to Noise Ratio Piezoelectric Thin Film Sensor Based on Elastomer Amplification for Ambulatory Blood Pressure Monitoring

Continuous pulse wave detection can be used for monitoring and diagnosing cardiovascular diseases, and research on pulse sensing based on piezoelectric thin films is one of the hot spots. Usually, piezoelectric thin films do not come into direct contact with the skin and need to be connected through a layer of an elastic medium. Most views think that the main function of this layer of elastic medium is to increase the adhesion between the sensor component and the skin, but there is little discussion about the impact of the elastic medium on pulse vibration transmission. Here, we conducted a detailed study on the effects of Young's modulus and the thickness of elastic media on pulse sensing signals. The results show that the waveform amplitude of the piezoelectric sensing signal decreases with the increase of Young's modulus and thickness of the elastic medium. Then, we constructed a theoretical model of the influence of elastic media on pulse wave propagation. The amplitude of the pulse wave signal detected by the optimized sensor was increased to 480%. Our research shows that by regulating Young's modulus and thickness of elastic media, pulse wave signals can undergo a similar amplification effect, which has an important theoretical reference value for achieving ambulatory blood pressure monitoring based on high-quality pulse waves.

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.

Triboelectric micro-flexure-sensitive fiber electronics

Developing fiber electronics presents a practical approach for establishing multi-node distributed networks within the human body, particularly concerning triboelectric fibers. However, realizing fiber electronics for monitoring micro-physiological activities remains challenging due to the intrinsic variability and subtle amplitude of physiological signals, which differ among individuals and scenarios. Here, we propose a technical approach based on a dynamic stability model of sheath-core fibers, integrating a micro-flexure-sensitive fiber enabled by nanofiber buckling and an ion conduction mechanism. This scheme enhances the accuracy of the signal transmission process, resulting in improved sensitivity (detectable signal at ultra-low curvature of 0.1 mm-1; flexure factor >21.8% within a bending range of 10°.) and robustness of fiber under micro flexure. In addition, we also developed a scalable manufacturing process and ensured compatibility with modern weaving techniques. By combining precise micro-curvature detection, micro-flexure-sensitive fibers unlock their full potential for various subtle physiological diagnoses, particularly in monitoring fiber upper limb muscle strength for rehabilitation and training.

Recent Advances in Tactile Sensory Systems: Mechanisms, Fabrication, and Applications

Flexible electronics is a cutting-edge field that has paved the way for artificial tactile systems that mimic biological functions of sensing mechanical stimuli. These systems have an immense potential to enhance human-machine interactions (HMIs). However, tactile sensing still faces formidable challenges in delivering precise and nuanced feedback, such as achieving a high sensitivity to emulate human touch, coping with environmental variability, and devising algorithms that can effectively interpret tactile data for meaningful interactions in diverse contexts. In this review, we summarize the recent advances of tactile sensory systems, such as piezoresistive, capacitive, piezoelectric, and triboelectric tactile sensors. We also review the state-of-the-art fabrication techniques for artificial tactile sensors. Next, we focus on the potential applications of HMIs, such as intelligent robotics, wearable devices, prosthetics, and medical healthcare. Finally, we conclude with the challenges and future development trends of tactile sensors.

High-Performance Hydrogel Sensors Enabled Multimodal and Accurate Human-Machine Interaction System for Active Rehabilitation

Human-machine interaction (HMI) technology shows an important application prospect in rehabilitation medicine, but it is greatly limited by the unsatisfactory recognition accuracy and wearing comfort. Here, this work develops a fully flexible, conformable, and functionalized multimodal HMI interface consisting of hydrogel-based sensors and a self-designed flexible printed circuit board. Thanks to the component regulation and structural design of the hydrogel, both electromyogram (EMG) and forcemyography (FMG) signals can be collected accurately and stably, so that they are later decoded with the assistance of artificial intelligence (AI). Compared with traditional multichannel EMG signals, the multimodal human-machine interaction method based on the combination of EMG and FMG signals significantly improves the efficiency of human-machine interaction by increasing the information entropy of the interaction signals. The decoding accuracy of the interaction signals from only two channels for different gestures reaches 91.28%. The resulting AI-powered active rehabilitation system can control a pneumatic robotic glove to assist stroke patients in completing movements according to the recognized human motion intention. Moreover, this HMI interface is further generalized and applied to other remote sensing platforms, such as manipulators, intelligent cars, and drones, paving the way for the design of future intelligent robot systems.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Synergistic advancements in high-performance flexible capacitive pressure sensors: structural modifications, AI integration, and diverse applications

The development of flexible pressure sensors for monitoring human motion and physiological signals has attracted extensive scientific research. However, achieving low monitoring limits, a wide detection range, large bending stresses, and excellent mechanical stability simultaneously remains a serious challenge. With the aim of developing a high-performance capacitive pressure sensor (CPS), this paper introduces the successful preparation of a single-walled carbon nanotube (SWNT)/polydimethylsiloxane (S-PDMS) composite dielectric with a foam-like structure (high permittivity and low elasticity modulus) and MXene/SWNT (S-MXene) composite film electrodes with a micro-crumpled structure. The above structurally modified CPS (SMCPS) demonstrated an excellent response output during pressure loading, achieving a wide pressure detection range (up to 700 kPa), a low detection limit (16.55 Pa), fast response/recovery characteristics (48/60 ms), enhanced sensitivity across a wide pressure range, long-term stability under repeated heavy loading and unloading (40 kPa, >2000 cycles), and reliable performance under various temperature and humidity conditions. The SMCPS demonstrated a precise and stable capacitive response in monitoring subtle physiological signals and detecting motion, owing to its unique electrode structure. The flexible device was integrated with an Internet of Things module to create a smart glove system that enables real-time tracking of dynamic gestures. This system demonstrates exceptional performance in gesture recognition and prediction with artificial intelligence analysis, highlighting the potential of the SMCPS in human-machine interface applications.

Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement

Most wearable robots such as exoskeletons and prostheses can operate with dexterity, while wearers do not perceive them as part of their bodies. In this perspective, we contend that integrating environmental, physiological, and physical information through multi-modal fusion, incorporating human-in-the-loop control, utilizing neuromuscular interface, employing flexible electronics, and acquiring and processing human-robot information with biomechatronic chips, should all be leveraged towards building the next generation of wearable robots. These technologies could improve the embodiment of wearable robots. With optimizations in mechanical structure and clinical training, the next generation of wearable robots should better facilitate human motor and sensory reconstruction and enhancement.

Multi-Level Pyramidal Microstructure-Based Pressure Sensors with High Sensitivity and Wide Linear Range for Healthcare Monitoring

Flexible pressure sensors have garnered significant attention in the field of wearable healthcare due to their scalability and shape variability. However, a crucial challenge in their practical application for various healthcare scenarios is striking a balance between the sensitivity and sensing range. This limitation arises from the reduced compressibility of the microstructures on the surface of pressure-sensitive materials under high pressure, resulting in progressive saturation of the sensor's response and leading to a restricted and nonlinear pressure sensing range. In this study, we present a novel approach utilizing multi-level pyramidal microstructures in flexible pressure sensors to achieve both high sensitivity (8775 kPa-1) and linear response (R2 = 0.997) over a wide pressure range (up to 1000 kPa). The effectiveness of the proposed design stems from the compensatory behavior of the lower pyramidal microstructures, which counteracts the declining sensitivity associated with the gradual hardening of the higher pyramidal microstructures. Furthermore, the sensor demonstrates a fast response time of 11.6 ms and a fast relaxation time of 3.8 ms and can reliably detect pressures as low as 30.2 Pa. Our findings highlight the applicability of this flexible pressure sensor in diverse human body health detection tasks, ranging from weak pulses to finger flexion and plantar pressure distribution. Notably, the proposed sensor design eliminates the need for replacing flexible pressure sensors with varying ranges, thereby enhancing their practical utility.

One Pot Photomediated Formation of Electrically Conductive Hydrogels

Electrically conductive hydrogels represent an innovative platform for the development of bioelectronic devices. While photolithography technologies have enabled the fabrication of complex architectures with high resolution, photoprinting conductive hydrogels is still a challenging task because the conductive polymer absorbs light which can outcompete photopolymerization of the insulating scaffold. In this study, we introduce an approach to synthesizing conductive hydrogels in one step. Our approach combines the simultaneous photo-cross-linking of a polymeric scaffold and the polymerization of 3,4-ethylene dioxythiophene (EDOT), without additional photocatalysts. This process involves the copolymerization of photo-cross-linkable coumarin-containing monomers with sodium styrenesulfonate to produce a water-soluble poly(styrenesulfonate-co-coumarin acrylate) (P(SS-co-CoumAc)) copolymer. Our findings reveal that optimizing the [SS]:[CoumAc] ratio at 100:5 results in hydrogels with the strain at break up to 16%. This mechanical resilience is coupled with an electronic conductivity of 9.2 S m-1 suitable for wearable electronics. Furthermore, the conductive hydrogels can be photopatterned to achieve micrometer-sized structures with high resolution. The photo-cross-linked hydrogels are used as electrodes to record stable and reliable surface electromyography (sEMG) signals. These novel photo-cross-linkable polymers combined with one-pot PEDOT (poly-EDOT) polymerization open possibilities for rapidly prototyping complex bioelectronic devices and creating custom-designed interfaces between electronics and biological systems.

Low-dimensional nanostructures for monolithic 3D-integrated flexible and stretchable electronics

Flexible/stretchable electronics, which are characterized by their ultrathin design, lightweight structure, and excellent mechanical robustness and conformability, have garnered significant attention due to their unprecedented potential in healthcare, advanced robotics, and human-machine interface technologies. An increasing number of low-dimensional nanostructures with exceptional mechanical, electronic, and/or optical properties are being developed for flexible/stretchable electronics to fulfill the functional and application requirements of information sensing, processing, and interactive loops. Compared to the traditional single-layer format, which has a restricted design space, a monolithic three-dimensional (M3D) integrated device architecture offers greater flexibility and stretchability for electronic devices, achieving a high-level of integration to accommodate the state-of-the-art design targets, such as skin-comfort, miniaturization, and multi-functionality. Low-dimensional nanostructures possess small size, unique characteristics, flexible/elastic adaptability, and effective vertical stacking capability, boosting the advancement of M3D-integrated flexible/stretchable systems. In this review, we provide a summary of the typical low-dimensional nanostructures found in semiconductor, interconnect, and substrate materials, and discuss the design rules of flexible/stretchable devices for intelligent sensing and data processing. Furthermore, artificial sensory systems in 3D integration have been reviewed, highlighting the advancements in flexible/stretchable electronics that are deployed with high-density, energy-efficiency, and multi-functionalities. Finally, we discuss the technical challenges and advanced methodologies involved in the design and optimization of low-dimensional nanostructures, to achieve monolithic 3D-integrated flexible/stretchable multi-sensory systems.

A Versatile Strategy for Concurrent Passive Daytime Radiative Cooling and Sustainable Energy Harvesting

Developing versatile systems that can concurrently achieve energy saving and energy generation is critical to accelerate carbon neutrality. However, challenges on designing highly effective, large scale, and multifunctional photonic film hinder the concurrent combination of passive daytime radiative cooling (PDRC) and utilization of sustainable clean energies. Herein, a versatile scalable photonic film (Ecoflex@h-BN) with washable property and excellent mechanical stability is developed by combining the excellent scattering efficiency of the hexagonal boron nitride (h-BN) nanoplates with the high infrared emissivity and ideal triboelectric negative property of the Ecoflex matrix. Strikingly, sufficiently high solar reflectance (0.92) and ideal emissivity (0.97) endow the Ecoflex@h-BN film with subambient cooling effect of ≈9.5 °C at midday during the continuous outdoor measurements. In addition, the PDRC Ecoflex@h-BN film-based triboelectric nanogenerator (PDRC-TENG) exhibits a maximum peak power density of 0.5 W m-2 . By reasonable structure design, the PDRC-TENG accomplishes effective wind energy harvesting and can successfully drive the electronic device. Meanwhile, an on-skin PDRC-TENG is fabricated to harvest human motion energy and monitor moving states. This research provides a novel design of a multifunctional PDRC photonic film, and offers a versatile strategy to realize concurrent PDRC and sustainable energies harvesting.

Deep-learning enabled smart insole system aiming for multifunctional foot-healthcare applications

Real-time foot pressure monitoring using wearable smart systems, with comprehensive foot health monitoring and analysis, can enhance quality of life and prevent foot-related diseases. However, traditional smart insole solutions that rely on basic data analysis methods of manual feature extraction are limited to real-time plantar pressure mapping and gait analysis, failing to meet the diverse needs of users for comprehensive foot healthcare. To address this, we propose a deep learning-enabled smart insole system comprising a plantar pressure sensing insole, portable circuit board, deep learning and data analysis blocks, and software interface. The capacitive sensing insole can map both static and dynamic plantar pressure with a wide range over 500 kPa and excellent sensitivity. Statistical tools are used to analyze long-term foot pressure usage data, providing indicators for early prevention of foot diseases and key data labels for deep learning algorithms to uncover insights into the relationship between plantar pressure patterns and foot issues. Additionally, a segmentation method assisted deep learning model is implemented for exercise-fatigue recognition as a proof of concept, achieving a high classification accuracy of 95%. The system also demonstrates various foot healthcare applications, including daily activity statistics, exercise injury avoidance, and diabetic foot ulcer prevention.

Viscoelastic blood coagulation testing system enabled by a non-contact triboelectric angle sensor

Thromboelastography (TEG) remains a convenient and effective viscoelastic blood coagulation testing device for guiding blood component transfusion and assessing the risk of thrombosis. Here, a TEG enabled by a non-contact triboelectric angle sensor (NTAS) with a small size (∼7 cm3) is developed for assessing the blood coagulation system. With the assistance of a superelastic torsion wire structure, the NTAS-TEG realizes the detection of blood viscoelasticity. Benefiting from a grating and convex design, the NTAS holds a collection of compelling features, including accurate detection of rotation angles from -2.5° to 2.5°, high linearity (R 2 = 0.999), and a resolution of 0.01°. Besides, the NTAS exhibits merits of low cost and simplified fabrication. Based on the NTAS-TEG, a viscoelastic blood coagulation detection and analysis system is successfully constructed, which can provide a graph and parameters associated with clot initiation, formation, and stability for clinicians by using 0.36 mL of whole blood. The system not only validates the feasibility of the triboelectric coagulation testing sensor, but also further expands the application of triboelectric sensors in healthcare.

Adaptive tactile interaction transfer via digitally embroidered smart gloves

Human-machine interfaces for capturing, conveying, and sharing tactile information across time and space hold immense potential for healthcare, augmented and virtual reality, human-robot collaboration, and skill development. To realize this potential, such interfaces should be wearable, unobtrusive, and scalable regarding both resolution and body coverage. Taking a step towards this vision, we present a textile-based wearable human-machine interface with integrated tactile sensors and vibrotactile haptic actuators that are digitally designed and rapidly fabricated. We leverage a digital embroidery machine to seamlessly embed piezoresistive force sensors and arrays of vibrotactile actuators into textiles in a customizable, scalable, and modular manner. We use this process to create gloves that can record, reproduce, and transfer tactile interactions. User studies investigate how people perceive the sensations reproduced by our gloves with integrated vibrotactile haptic actuators. To improve the effectiveness of tactile interaction transfer, we develop a machine-learning pipeline that adaptively models how each individual user reacts to haptic sensations and then optimizes haptic feedback parameters. Our interface showcases adaptive tactile interaction transfer through the implementation of three end-to-end systems: alleviating tactile occlusion, guiding people to perform physical skills, and enabling responsive robot teleoperation.

A shadow enabled non-invasive probe for multi-feature intelligent liquid surveillance system

Liquid detection probes used to identify the features of liquids show great promise in a variety of important applications. However, some challenges, such as sample contamination by direct contact with the liquid, the requirement of additional signal emitters, and complex fabrication, hindered the development of liquid detection probes. Here, we developed a non-invasive shadow probe (SP) for a multi-feature intelligent liquid surveillance system (ILSS). The self-powered SP with the working mechanism of the shadow effect can detect the features of liquids by analyzing the variations of liquid shadows such as the area, wavelength, and brightness. The exact resolution (5 different colors, 6 different concentrations, 6 different levels, 100% accuracy) and fast response time (0.2 ms) are shown by the SP under ambient light conditions (even in 0.003 sun). The ILSS, which integrated the SPs with signal processing circuits and applied the artificial intelligence (AI) technique, successfully detects and synoptically learns about liquids simultaneously. The in-real time ILSS reaches a test accuracy of 99.3% for 10 types of liquids with multiple features. This work showcases a promising solution for non-invasive multi-feature liquid detection, displaying great potential for future applications.

Fiber-Based Noncontact Sensor with Stretchability for Underwater Wearable Sensing and VR Applications

The rapid development of artificial intelligent wearable devices has led to an increasing need for seamless information exchange between humans, machines, and virtual spaces, often relying on touch sensors as the primary interaction medium. Additionally, the demand for underwater detection technologies is on the rise owing to the prevalent wet and submerged environment. Here, a fiber-based capacitive sensor with superior stretchability and hydrophobicity is proposed, designed to cater to noncontact and underwater applications. The sensor is constructed using bacterial cellulose (BC)@BC/carbon nanotubes (CNTs) (BBT) helical fiber as the matrix and methyltrimethoxysilane (MTMS) as the hydrophobic modified agent, forming a hydrophobic silylated BC@BC/CNT (SBBT) helical fiber by the chemical vapor deposition (CVD) technique. These fibers exhibit an impressive contact angle of 132.8°. The SBBT helicalfiber-based capacitive sensor presents capabilities for both noncontact and underwater sensing, which exhibits a significant capacitance change of -0.27 (at a distance of 0.5 cm). We have achieved interactive control between real space and virtual space through intelligent data analysis technology with minimal interference from the presence of water. This work has laid a solid foundation of noncontact sensing with attributes such as degradability, stretchability, and hydrophobicity. Moreover, it offers promising solutions for barrier-free communication in virtual reality (VR) and underwater applications, providing avenues for smart human-machine interfaces for submerged use.

Mechanochromic and ionic conductive cholesteric liquid crystal elastomers for biomechanical monitoring and human-machine interaction

Cholesteric liquid crystal elastomers (CLCEs) that combine rubbery elasticity with structural colour from self-assembled helical nanostructures are of paramount importance for diverse applications such as biomimetic skins, adaptive optics and soft robotics. Despite great advances, it is challenging to integrate electrical sensing and colour-changing characteristics in a single CLCE system. Here, we report the design and synthesis of an ionic conductive cholesteric liquid crystal elastomer (iCLCE) through in situ Michael addition and free-radical photopolymerization of CLCE precursors on silane-functionalized polymer ionic liquid networks, in which robust covalent chemical bonding was formed at the interface. Thanks to superior mechanochromism and ionic conductivity, the resulting iCLCEs exhibit dynamic colour-changing and electrical sensing functions in a wide range upon mechanical stretching, and can be used for biomechanical monitoring during joint bending. Importantly, a capacitive elastomeric sensor can be constructed through facilely stacking iCLCEs, where the optical and electrical dual-signal reporting performance allows intuitive visual localization of pressure intensity and distribution. Moreover, proof-of-concept application of the iCLCEs has been demonstrated with human-interactive systems. The research disclosed herein can provide new insights into the development of bioinspired somatosensory materials for emerging applications in diverse fields such as human-machine interaction, prostheses and intelligent robots.

Micropyramid Array Bimodal Electronic Skin for Intelligent Material and Surface Shape Perception Based on Capacitive Sensing

Developing electronic skins (e-skins) that are comparable to or even beyond human tactile perception holds significant importance in advancing the process of intellectualization. In this context, a machine-learning-motivated micropyramid array bimodal (MAB) e-skin based on capacitive sensing is reported, which enables spatial mapping applications based on bimodal sensing (proximity and pressure) implemented via fringing and iontronic effects, such as contactless measurement of 3D objects and contact recognition of Braille letters. Benefiting from the iontronic effect and single-micropyramid structure, the MAB e-skin in pressure mode yields impressive features: a maximum sensitivity of 655.3 kPa-1 (below 0.5 kPa), a linear sensitivity of 327.9 kPa-1 (0.5-15 kPa), and an ultralow limit of detection of 0.2 Pa. With the assistance of multilayer perceptron and convolutional neural network, the MAB e-skin can accurately perceive 6 materials and 10 surface shapes based on the training and learning using the collected datasets from proximity and pressure modes, thus allowing it to achieve the precise perception of different objects within one proximity-pressure cycle. The development of this MAB e-skin opens a new avenue for robotic skin and the expansion of advanced applications.

Bioinspired nanoplatforms for human-machine interfaces: Recent progress in materials and device applications

The panoramic characteristics of human-machine interfaces (HMIs) have prompted the needs to update the biotechnology community with the recent trends, developments, and future research direction toward next-generation bioelectronics. Bioinspired materials are promising for integrating various bioelectronic devices to realize HMIs. With the advancement of scientific biotechnology, state-of-the-art bioelectronic applications have been extensively investigated to improve the quality of life by developing and integrating bioinspired nanoplatforms in HMIs. This review highlights recent trends and developments in the field of biotechnology based on bioinspired nanoplatforms by demonstrating recently explored materials and cutting-edge device applications. Section 1 introduces the recent trends and developments of bioinspired nanomaterials for HMIs. Section 2 reviews various flexible, wearable, biocompatible, and biodegradable nanoplatforms for bioinspired applications. Section 3 furnishes recently explored substrates as carriers for advanced nanomaterials in developing HMIs. Section 4 addresses recently invented biomimetic neuroelectronic, nanointerfaces, biointerfaces, and nano/microfluidic wearable bioelectronic devices for various HMI applications, such as healthcare, biopotential monitoring, and body fluid monitoring. Section 5 outlines designing and engineering of bioinspired sensors for HMIs. Finally, the challenges and opportunities for next-generation bioinspired nanoplatforms in extending the potential on HMIs are discussed for a near-future scenario. We believe this review can stimulate the integration of bioinspired nanoplatforms into the HMIs in addition to wearable electronic skin and health-monitoring devices while addressing prevailing and future healthcare and material-related problems in biotechnologies.

A Flexible Self-Powered Noncontact Sensor with an Ultrawide Sensing Range for Human-Machine Interactions in Harsh Environments

Noncontact human-machine interactions (HMIs) provide a hygienic and intelligent approach to communicate between humans and machines. However, current noncontact HMIs are generally hampered by the interaction distance, and they lack the adaptability to environmental interference such as high humidity conditions. Here, we explore a self-powered electret-based noncontact sensor (ENS) with moisture-resisting ability and ultrawide sensing range exceeding 2.5 m. A megascopic air-bubble structure is designed to enhance charge-storage stability and charge-recovery ability of the ENS based on the heterocharge-synergy effect in electrets. Besides, multilayer electret films are introduced to strengthen the electric field by utilizing the electrostatic field superposition effect. Thanks to the above improved performances of the ENS, we demonstrate various noncontact HMI applications in harsh environments, including noncontact appliances, a moving trajectory and accidental fall tracking system, and a real-time machine learning-assisted gesture recognition system with accuracy as high as 99.21%. This research expands the way for noncontact sensor design and may further broaden applications in noncontact HMIs.

Rational Design of Flexible Mechanical Force Sensors for Healthcare and Diagnosis

Over the past decade, there has been a significant surge in interest in flexible mechanical force sensing devices and systems. Tremendous efforts have been devoted to the development of flexible mechanical force sensors for daily healthcare and medical diagnosis, driven by the increasing demand for wearable/portable devices in long-term healthcare and precision medicine. In this review, we summarize recent advances in diverse categories of flexible mechanical force sensors, covering piezoresistive, capacitive, piezoelectric, triboelectric, magnetoelastic, and other force sensors. This review focuses on their working principles, design strategies and applications in healthcare and diagnosis, with an emphasis on the interplay among the sensor architecture, performance, and application scenario. Finally, we provide perspectives on the remaining challenges and opportunities in this field, with particular discussions on problem-driven force sensor designs, as well as developments of novel sensor architectures and intelligent mechanical force sensing systems.

A Metamaterial Computational Multi-Sensor of Grip-Strength Properties with Point-of-Care Human-Computer Interaction

Grip strength is a biomarker of frailty and an evaluation indicator of brain health, cardiovascular morbidity, and psychological health. Yet, the development of a reliable, interactive, and point-of-care device for comprehensive multi-sensing of hand grip status is challenging. Here, a relation between soft buckling metamaterial deformations and built piezoelectric voltage signals is uncovered to achieve multiple sensing of maximal grip force, grip speed, grip impulse, and endurance indicators. A metamaterial computational sensor design is established by hyperelastic model that governs the mechanical characterization, machine learning models for computational sensing, and graphical user interface to provide visual cues. A exemplify grip measurement for left and right hands of seven elderly campus workers is conducted. By taking indicators of grip status as input parameters, human-computer interactive games are incorporated into the computational sensor to improve the user compliance with measurement protocols. Two elderly female schizophrenic patients are participated in the real-time interactive point-of-care grip assessment and training for potentially sarcopenia screening. The attractive features of this advanced intelligent metamaterial computational sensing system are crucial to establish a point-of-care biomechanical platform and advancing the human-computer interactive healthcare, ultimately contributing to a global health ecosystem.

A robotic sensory system with high spatiotemporal resolution for texture recognition

Humans can gently slide a finger on the surface of an object and identify it by capturing both static pressure and high-frequency vibrations. Although modern robots integrated with flexible sensors can precisely detect pressure, shear force, and strain, they still perform insufficiently or require multi-sensors to respond to both static and high-frequency physical stimuli during the interaction. Here, we report a real-time artificial sensory system for high-accuracy texture recognition based on a single iontronic slip-sensor, and propose a criterion-spatiotemporal resolution, to corelate the sensing performance with recognition capability. The sensor can respond to both static and dynamic stimuli (0-400 Hz) with a high spatial resolution of 15 μm in spacing and 6 μm in height, together with a high-frequency resolution of 0.02 Hz at 400 Hz, enabling high-precision discrimination of fine surface features. The sensory system integrated on a prosthetic fingertip can identify 20 different commercial textiles with a 100.0% accuracy at a fixed sliding rate and a 98.9% accuracy at random sliding rates. The sensory system is expected to help achieve subtle tactile sensation for robotics and prosthetics, and further be applied to haptic-based virtual reality and beyond.

Intelligent Recognition Using Ultralight Multifunctional Nano-Layered Carbon Aerogel Sensors with Human-Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion. Under limited visual conditions, people can sense a variety of tactile signals to identify objects accurately and rapidly. However, replicating this unique capability in robots remains a significant challenge. Here, we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure, temperature, material recognition and 3D location capabilities, which is combined with multimodal supervised learning algorithms for object recognition. The sensor exhibits human-like pressure (0.04-100 kPa) and temperature (21.5-66.2 °C) detection, millisecond response times (11 ms), a pressure sensitivity of 92.22 kPa-1 and triboelectric durability of over 6000 cycles. The devised algorithm has universality and can accommodate a range of application scenarios. The tactile system can identify common foods in a kitchen scene with 94.63% accuracy and explore the topographic and geomorphic features of a Mars scene with 100% accuracy. This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing, recognition and intelligence.

Bioinspired Young's Modulus-Hierarchical E-Skin with Decoupling Multimodality and Neuromorphic Encoding Outputs to Biosystems

As key interfaces for the disabled, optimal prosthetics should elicit natural sensations of skin touch or proprioception, by unambiguously delivering the multimodal signals acquired by the prosthetics to the nervous system, which still remains challenging. Here, a bioinspired temperature-pressure electronic skin with decoupling capability (TPD e-skin), inspired by the high-low modulus hierarchical structure of human skin, is developed to restore such functionality. Due to the bionic dual-state amplifying microstructure and contact resistance modulation, the MXene TPD e-skin exhibits high sensitivity over a wide pressure range and excellent temperature insensitivity (91.2% reduction). Additionally, the high-low modulus structural configuration enables the pressure insensitivity of the thermistor. Furthermore, a neural model is proposed to neutrally code the temperature-pressure signals into three types of nerve-acceptable frequency signals, corresponding to thermoreceptors, slow-adapting receptors, and fast-adapting receptors. Four operational states in the time domain are also distinguished after the neural coding in the frequency domain. Besides, a brain-like machine learning-based fusion process for frequency signals is also constructed to analyze the frequency pattern and achieve object recognition with a high accuracy of 98.7%. The TPD neural system offers promising potential to enable advanced prosthetic devices with the capability of multimodality-decoupling sensing and deep neural integration.

Shape-Configurable MXene-Based Thermoacoustic Loudspeakers with Tunable Sound Directivity

Film-type shape-configurable speakers with tunable sound directivity are in high demand for wearable electronics. Flexible, thin thermoacoustic (TA) loudspeakers-which are free from bulky vibrating diaphragms-show promise in this regard. However, configuring thin TA loudspeakers into arbitrary shapes is challenging because of their low sound pressure level (SPL) under mechanical deformations and low conformability to other surfaces. By carefully controlling the heat capacity per unit area and thermal effusivity of an MXene conductor and substrates, respectively, it fabricates an ultrathin MXene-based TA loudspeaker exhibiting high SPL output (74.5 dB at 15 kHz) and stable sound performance for 14 days. Loudspeakers with the parylene substrate, whose thickness is less than the thermal penetration depth, generated bidirectional and deformation-independent sound in bent, twisted, cylindrical, and stretched-kirigami configurations. Furthermore, it constructs parabolic and spherical versions of ultrathin, large-area (20 cm × 20 cm) MXene-based TA loudspeakers, which display sound-focusing and 3D omnidirectional-sound-generating attributes, respectively.

Metaverse Wearables for Immersive Digital Healthcare: A Review

The recent exponential growth of metaverse technology has been instrumental in reshaping a myriad of sectors, not least digital healthcare. This comprehensive review critically examines the landscape and future applications of metaverse wearables toward immersive digital healthcare. The key technologies and advancements that have spearheaded the metamorphosis of metaverse wearables are categorized, encapsulating all-encompassed extended reality, such as virtual reality, augmented reality, mixed reality, and other haptic feedback systems. Moreover, the fundamentals of their deployment in assistive healthcare (especially for rehabilitation), medical and nursing education, and remote patient management and treatment are investigated. The potential benefits of integrating metaverse wearables into healthcare paradigms are multifold, encompassing improved patient prognosis, enhanced accessibility to high-quality care, and high standards of practitioner instruction. Nevertheless, these technologies are not without their inherent challenges and untapped opportunities, which span privacy protection, data safeguarding, and innovation in artificial intelligence. In summary, future research trajectories and potential advancements to circumvent these hurdles are also discussed, further augmenting the incorporation of metaverse wearables within healthcare infrastructures in the post-pandemic era.

Easy-To-Wear Auxetic SMA Knot-Architecture for Spatiotemporal and Multimodal Haptic Feedbacks

Wearable haptic interfaces prioritize user comfort, but also value the ability to provide diverse feedback patterns for immersive interactions with the virtual or augmented reality. Here, to provide both comfort and diverse tactile feedback, an easy-to-wear and multimodal wearable haptic auxetic fabric (WHAF) is prepared by knotting shape-memory alloy wires into an auxetic-structured fabric. This unique meta-design allows the WHAF to completely expand and contract in 3D, providing superior size-fitting and shape-fitting capabilities. Additionally, a microscale thin layer of Parylene is coated on the surface to create electrically separated zones within the WHAF, featuring zone-specified actuation for conveying diverse spatiotemporal information to users with using the WHAF alone. Depending on the body part it is worn on, the WHAF conveys either cutaneous or kinesthetic feedback, thus, working as a multimodal wearable haptic interface. As a result, when worn on the forearm, the WHAF intuitively provides spatiotemporal information to users during hands-free navigation and teleoperation in virtual reality, and when worn on the elbow, the WHAF guides users to reach the desired elbow flexion, like a personal exercise advisor.

Injection-on-Skin Granular Adhesive for Interactive Human-Machine Interface

Realization of interactive human-machine interfaces (iHMI) is improved with development of soft tissue-like strain sensors beyond hard robotic exosuits, potentially allowing cognitive behavior therapy and physical rehabilitation for patients with brain disorders. Here, this study reports on a strain-sensitive granular adhesive inspired by the core-shell architectures of natural basil seeds for iHMI as well as human-metaverse interfacing. The granular adhesive sensor consists of easily fragmented hydropellets as a core and tissue-adhesive catecholamine layers as a shell, satisfying great on-skin injectability, ionic-electrical conductivity, and sensitive resistance changes through reversible yet robust cohesion among the hydropellets. Particularly, it is found that the ionic-electrical self-doping of the catecholamine shell on hydrosurfaces leads to a compact ion density of the materials. Based on these physical and electrical properties of the sensor, it is demonstrated that successful iHMI integration with a robot arm in both real and virtual environments enables robotic control by finger gesture and haptic feedback. This study expresses benefits of using granular hydrogel-based strain sensors for implementing on-skin writable bioelectronics and their bridging into the metaverse world.

Prosthetic finger for fingertip tactile sensing via flexible chromatic optical waveguides

Building prosthetics indistinguishable from human limbs to accurately receive and transmit sensory information to users not only promises to radically improve the lives of amputees, but also shows potential in a range of robotic applications. Currently, a mainstream approach is to embed electrical or optical sensors with force/thermal sensing functions on the surface or inside of prosthetic fingers. Compared with electrical sensing technologies, tactile sensors based on stretchable optical waveguides have the advantages of easy fabrication, chemical safety, environmental stability, and compatibility with prosthetic structural materials. However, so far, research has mainly focused on the perception of finger joint motion or external press, and there is still a lack of study on optical sensors with fingertip tactile capabilities (such as texture, hardness, slip detection, etc.). Here we report a 3D printing prosthetic finger with flexible chromatic optical waveguides implanted at the fingertip. The finger achieves distributed displacement/force sensing detection, and exhibits high sensitivity, fast response and good stability. The finger can be used to conduct active sensory experiments, and the detection parameters include object contour, hardness, slip direction and speed, temperature, etc. Finally, exploratory research on identifying and manipulating objects is carried out with this finger. The developed prosthetic finger can artificially recreate touch perception and realize complex functions such as note-writing analysis and braille recognition.

Flexible optical tactile sensor based on a liquid-membrane lens structure

Based on the liquid lens focus mechanism, a novel, to the best of our knowledge, optical tactile sensor is designed by taking advantage of the structure simplicity, fast response, and environmental immunity. The design of the tactile sensing mechanism used the liquid-membrane lens structure. To integrate the tactile sensing system, we designed a data acquisition circuit unit. A performance test platform was built, and performance testing and two application demonstrations were conducted. The experiment's result showed that the linear fitting degree was greater than 0.988, the load response time was 0.078 s, the target mass was accurately measured, the maximum error was less than 0.02 N, and the fine adjustment of the LED light intensity was achieved.

All Resistive Pressure-Temperature Bimodal Sensing E-Skin for Object Classification

Electronic skin (E-skin) with multimodal sensing ability demonstrates huge prospects in object classification by intelligent robots. However, realizing the object classification capability of E-skin faces severe challenges in multiple types of output signals. Herein, a hierarchical pressure-temperature bimodal sensing E-skin based on all resistive output signals is developed for accurate object classification, which consists of laser-induced graphene/silicone rubber (LIG/SR) pressure sensing layer and NiO temperature sensing layer. The highly conductive LIG is employed as pressure-sensitive material as well as the interdigital electrode. Benefiting from high conductivity of LIG, pressure perception exhibits an excellent sensitivity of -34.15 kPa-1 . Meanwhile, a high temperature coefficient of resistance of -3.84%°C-1 is obtained in the range of 24-40 °C. More importantly, based on only electrical resistance as the output signal, the bimodal sensing E-skin with negligible crosstalk can simultaneously achieve pressure and temperature perception. Furthermore, a smart glove based on this E-skin enables classifying various objects with different shapes, sizes, and surface temperatures, which achieves over 92% accuracy under assistance of deep learning. Consequently, the hierarchical pressure-temperature bimodal sensing E-skin demonstrates potential application in human-machine interfaces, intelligent robots, and smart prosthetics.

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on tactile hardware, algorithmic complexities, and the distinct features offered by each sensor. This paper has a special emphasis on agri-food manipulation and relevant tactile-sensing technologies. It highlights key areas in agri-food manipulation, including robotic harvesting, food item manipulation, and feature evaluation, such as fruit ripeness assessment, along with the emerging field of kitchen robotics. Through this interdisciplinary exploration, we aim to inspire researchers, engineers, and practitioners to harness the power of tactile-sensing technology for transformative advancements in agri-food robotics. By providing a comprehensive understanding of the current landscape and future prospects, this review paper serves as a valuable resource for driving progress in the field of tactile sensing and its application in agri-food systems.

Conformal Human-Machine Integration Using Highly Bending-Insensitive, Unpixelated, and Waterproof Epidermal Electronics Toward Metaverse

Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals, which is critical to the breakthrough development of metaverse. Interactive electronics face common dilemmas, which realize high-precision and stable touch detection but are rigid, bulky, and thick or achieve high flexibility to wear but lose precision. Here, we construct highly bending-insensitive, unpixelated, and waterproof epidermal interfaces (BUW epidermal interfaces) and demonstrate their interactive applications of conformal human-machine integration. The BUW epidermal interface based on the addressable electrical contact structure exhibits high-precision and stable touch detection, high flexibility, rapid response time, excellent stability, and versatile "cut-and-paste" character. Regardless of whether being flat or bent, the BUW epidermal interface can be conformally attached to the human skin for real-time, comfortable, and unrestrained interactions. This research provides promising insight into the functional composite and structural design strategies for developing epidermal electronics, which offers a new technology route and may further broaden human-machine interactions toward metaverse.

Flexible Optoelectronic Multimodal Proximity/Pressure/Temperature Sensors with Low Signal Interference

Multimodal tactile sensors are a crucial part of intelligent human-machine interaction and collaboration. Simultaneous detection of proximity, pressure, and temperature on a single sensor can greatly promote the safety, interactivity, and compactness of interaction systems. However, severe signal interference and complex decoupling algorithms hinder the actual applications. Here, this work reports a flexible optoelectronic multimodal sensor capable of detecting and decoupling proximity/pressure/temperature by integrating a light waveguide and an interdigital electrode (IDE) into a compact fibrous sensor. Negligible signal interference is realized by combining heterogeneous sensing mechanisms of optics and electronics, which encodes proximity into capacitance, pressure into light intensity and temperature into resistance. The sensor exhibits a large sensing distance of 225 mm with fast responses for proximity detection, a pressure sensitivity of 0.42 N-1 , and a temperature sensitivity of 7% °C-1 . As a proof of concept, a doll equipped with the sensor can accurately discriminate and detect various stimuli, thus achieving safe and immersive interactions with the user. This work opens up promising paths for self-decoupled multimodal sensors and related human/machine/environment interaction applications.

Highly Sensitive Fiber Pressure Sensors over a Wide Pressure Range Enabled by Resistive-Capacitive Hybrid Response

Soft capacitive pressure sensors with high performance are becoming increasingly in demand in the emerging flexible wearable field. While capacitive fiber pressure sensors have achieved high sensitivity, their sensitivity range is limited to low-pressure levels. As fiber sensors typically require preloading and fixation, this narrow range of high sensitivity poses a challenge for practical applications. To overcome this limitation, the study proposes resistive-capacitive hybrid response fiber pressure sensors (HFPSs) with three-layer core-sheath structures. The trigger and sensitivity enhancement mechanisms of the hybrid response are determined through model analysis and experimental verification. By adjustment of the sensitivity enhancement range of the hybrid response, the sensitivity attenuation of HFPSs is alleviated significantly. The obtained results demonstrate that HFPSs have excellent characteristics such as fast response, low hysteresis, wide response frequency, small signal drift, and good durability. The hybrid response enhances the practical sensitivity of HFPSs for various applications. With enhanced sensitivity, HFPSs can effectively monitor pulse signals at preloads ranging from 0 to 22.7 kPa. This wide range of preloads improves the fault tolerance of pulse monitoring and expands the potential application scenarios of fiber pressure sensors.