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This article aims to address the issue of low recognition accuracy in existing sorting robots caused by lighting, occlusion, and environmental factors. A fruit recognition method based on a flexible tactile sensor array is described. This method enables the robot to directly perceive the attributes of the objects and identify fruits using a flexible gripper, facilitating intelligent sorting. A novel flexible tactile sensor array is utilized to construct a flexible hand tactile information acquisition platform, which collects tactile time series data for the fruits. Principal component analysis is then employed for dimensionality reduction, followed by the development of an improved particle swarm optimization for the support vector machine model. Through an experimental study, the optimized model is compared with four other models, demonstrating better classification performance. The optimized model achieves an average tactile classification accuracy of up to 98.10% for the five types of fruits. A comparison between the improved optimization algorithm, genetic algorithm, and grid search algorithm reveals the superior optimization performance of the new approach. In the future, this method is expected to be implemented in industrial sorting robots for intelligent sorting on automatic production lines. Furthermore, the algorithm will be further refined to enhance the classification accuracy and efficiency. [ABSTRACT FROM AUTHOR]

A touch sensor is an essential component in meeting the growing demand for human‐machine interfaces. These sensors have been developed in wearable, attachable, and even implantable forms to acquire a wide range of information from humans. To be applied to the human body, sensors are required to be biocompatible and not restrict the natural movement of the body. Ionic materials are a promising candidate for soft touch sensors due to their outstanding properties, which include high stretchability, transparency, ionic conductivity, and biocompatibility. Here, this review discusses the unique features of soft ionic touch point sensors, focusing on the ionic material and its key role in the sensor. The touch sensing mechanisms include piezocapacitive, piezoresistive, surface capacitive, piezoelectric, and triboelectric and triboresistive sensing. This review analyzes the implementation hurdles and future research directions of the soft ionic touch sensors for their transformative potential. [ABSTRACT FROM AUTHOR]

Soft sensors are flexible and stretchable, and because of this, they can be used on a wide range of surfaces, regardless of their size or shape. Such sensors may have applications such as in human–robot interaction, healthcare, soft robotics and human motion detection, where they can sense their surroundings and provide information. In this work, a soft piezoresistive sensor inspired by human finger ridges has been fabricated with liquid metal (EGaIn) electrode‐filled embedded microchannels on elastomeric material (Ecoflex 0030) and characterized for a pressure range of 0 to 280 kPa at different compression rates. The sensitivity of the elastomeric sensor increases and the limit of detection (LOD) decreases with a reduction in compression rate. In this work, microchannels on soft and stretchable elastomers are cast on a metallic mold prepared using the micromilling method. This method reduces the complexity of developing microchannels on the soft material using the metallic mold. The sensor has microchannels with a cross‐section of 200 μm × 200 μm, an active sensing area of 10 × 10 mm2 and overall dimensions of 15 × 15 × 2 mm3. At a low compression rate, this sensor exhibits a maximum sensitivity of 0.126 kPa−1 and a high linearity of 0.98, a LOD of 68 Pa, a response time of 30 ms and stability for 10 000 consecutive cycles at 100 kPa load. The developed sensor was shown to successfully differentiate various objects (soft to hard) based on the feedback it received when it was deployed on the gripper of an industrial manipulator. © 2024 The Author(s). Polymer International published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Tactile sensors have garnered considerable interest for their capacity to detect and quantify tactile information. The incorporation of microstructural designs into flexible tactile sensors has emerged as a potent strategy to augment their sensitivity to pressure variations, thereby enhancing their linearity, response spectrum, and mechanical robustness. This review underscores the imperative for progress in microstructured flexible tactile sensors. Subsequently, the discourse transitions to the prevalent materials employed in the fabrication of sensor electrodes, encapsulation layers, and active sensing mediums, elucidating their merits and limitations. In‐depth discussions are devoted to tactile sensors adorned with microstructures, including but not limited to, micropyramids, microhemispheres, micropillars, microporous configurations, microcracks, topological interconnections, multilevel constructs, random roughness, biomimetic microstructures inspired by flora and fauna, accompanied by exemplar studies from each category. Moreover, the utility of flexible tactile sensors within the realm of intelligent environments is explicated, highlighting their application in the monitoring of physiological signals, the detection of sliding motions, and the discernment of surface textures. The review culminates in a critical examination of the paramount challenges and predicaments that must be surmounted to further the development and enhance the functional performance of tactile sensors, paving the way for their integration into advanced sensory systems. [ABSTRACT FROM AUTHOR]

Artificial skin has shown great potential in robot perception and human healthcare. It provides multifunctional tactile sensing, including 3D shape reconstruction, contact feedback, and temperature perception, where the 3D reconstruction function is indispensable for dexterous hands in tactile cognition and interaction. Vision‐based tactile sensor (VTS) is an innovative bionic tactile sensor and supports high‐resolution, high‐precision, and high‐density tactile reconstruction compared with electronic tactile sensors. Considering the unique contribution of visuo‐tactile sensing to artificial skin, this review focuses on the 3D reconstruction techniques of the VTS. 3D reconstruction methods are classified into five categories based on sensing modalities, hardware categories, and modeling approaches: 1) photometric stereo, 2) binocular depth calibration, 3) optical flow, 4) deep learning, and 5) ToF (time of flight). In addition, the association and difference of reconstruction methods are analyzed from the hardware perspective, and the development and technological details of 3D reconstruction are summarized. On this basis, the challenges and development direction are discussed. This review can be viewed as a technology guide to provide references for interested researchers. Furthermore, it is expected to promote the extensive application of the VTS in artificial skins. [ABSTRACT FROM AUTHOR]

Recent advances in manufacturing technology and new material processes have enabled novel device designs. As the growing field of robotic tactile sensing calls for advanced tactile sensors, waveguide‐based tactile sensors have shown promising mechanisms but system‐level integrated solutions are needed to demonstrate their feasibility for sensor applications. In this work, a novel ultra‐compact high‐resolution tactile sensor based on asymmetric Mach‐Zehnder interferometers (MZI) is proposed: PITS (Photonic Integrated Tactile Sensor). It is made from Parylene C waveguides using the Parylene photonic material platform. The sensor is composed of a 4 × 4 array of MZI sensing units and demonstrates multipoint contact sensing as well as shape detection with high sensitivity and low inter‐unit crosstalk. The sensing unit is based on multimode interference mechanism explored with supplementary mechanical and optical simulation models. It demonstrates a 0.08 N dynamic range with <0.01 N force resolution, 7.59 signal‐to‐noise ratio and an average hysteresis of 7.7% over 10 repeated indents. The sensor is fabricated in two layers: a Parylene photonic layer and an align‐bonded polydimethylsiloxane (PDMS) micropillar layer on top, which actuates the sensing units. This architecture features a design and fabrication pipeline that allows customizable sensitivity and dynamic range as well as scalable array designs. [ABSTRACT FROM AUTHOR]

Artificial tactile sensing capable of measuring shear and normal forces is crucial for the diverse human‐machine interactions and dexterous robotic manipulations. However, existing soft multi‐axis force sensors usually suffer from limited detectable directions and complex coupling mechanisms, limiting their applications in realistic wearable and robotic systems. Here, a hierarchically‐interlocked, three‐axis soft iontronic sensor is presented with an asymmetrical electrode pattern that leverages the mortise‐and‐tenon structure and ultracapacitive principle to detect omnidirectional shear and normal forces. The designed sensor facilitates the decoupling process and achieves enhanced sensing performances including high accuracy, fast response ability, and mechanical robustness. Prototypical integration and application of the sensor are demonstrated to perform wearable telecontrol of virtual platforms and assist robot gripper through closed‐loop force feedback. A sensing array is further developed to construct a touch panel and identify handwriting based on the continuously measured directions of applied forces. This work may offer a potentially promising solution for the next‐generation intelligent electronic skins requiring multimodal tactile sensing information. [ABSTRACT FROM AUTHOR]

The combination of fluidity and metallic conductivity has attracted considerable attention to liquid metal (LM), but its development remains challenging due to enormous surface tension. Here, vinyl‐terminated silicone oil and platinum catalyst are added to LM to reduce its surface tension, which develops a special type of liquid‐metal‐silicone (LMS) ink with a catalyst diffusion effect. Combined with an embedded three‐dimentional (3D) printing method, the LMS ink is printed on the support matrix, and the catalyst diffuses outward along the print path to cure the silicone around it, directly constructing self‐encapsulated conductive composites with excellent conductivity and self‐encapsulated flexible tactile sensors based on triboelectric nanogenerator (TENG). The sensor exhibits excellent sensitivity (0.308 V kPa−1), high linearity (≈0.99), and good durability (over 10 000 cycles). Furthermore, when used in flexible wearable electronics, the sensor demonstrates a good performance with an accuracy of ≈96% in classifying different human postures using a convolutional neural network. Finally, through embedded 3D printing with LMS ink and silicone ink, a somatosensory soft robotic gripper with complex cavity structures is designed and manufactured in one step, achieving the all‐in‐one integration of sensors and actuators. This study shows great application potential in flexible electronics and soft robotic systems. [ABSTRACT FROM AUTHOR]

This research introduces the Sparse Sensor Placement Optimization for Prediction algorithm and explores its use in bioinspired flight-by-feel control system design. Flying animals have velocity-sensing structures on their wings and are capable of highly agile flight in unsteady conditions, a proof-of-concept that artificial flight-by-feel control systems may be effective. Constrained by size, weight, and power, a flight-by-feel sensory system should have the fewest optimally placed sensors which capture enough information to predict the flight state. Flow datasets, such as from computational fluid dynamics, are discrete, often highly discontinuous, and ill-suited for conventional sensor placement optimization techniques. The data-driven Sparse Sensor Placement Optimization for Prediction approach reduces high-dimensional flow data to a low-dimensional sparse approximation containing nearly all of the original information, thereby identifying a near-optimal placement for any number of sensors. For two or more airflow velocity magnitude sensors, this algorithm finds a placement solution (design point) which predicts angle of attack of airfoils to within 0.10° and ranks within the top 1% of all possible design points validated by combinatorial search. The scalability and adaptability of this algorithm is demonstrated on several 2D model variations in clean and noisy data, and model sensitivities are evaluated and compared against conventional optimization techniques. Applications for this sensor placement algorithm are explored for aircraft design, flight control, and beyond. [ABSTRACT FROM AUTHOR]

A circular capacitive rainfall sensor can operate from non-touch mode to touch mode; that is, under the action of enough rainwater, its movable electrode plate can form a circular contact area with its fixed electrode plate. Therefore, the weight of rainwater is borne by only its movable electrode plate in non-touch mode operation but by both its movable and fixed electrode plates in touch mode operation, and the total capacitance of its touch mode operation is much larger than that of its non-touch mode operation. Essential to its numerical design and calibration is the ability to predict the deflection shape of its moveable electrode plate to determine its total capacitance. This requires the analytical solution to the fluid–structure interaction problem of its movable electrode plate under rainwater. In our previous work, only the analytical solution for the fluid–structure interaction problem before its movable electrode plate touches its fixed electrode plate was obtained, and how to numerically design and calibrate a circular non-touch mode capacitive rainfall sensor was illustrated. In this paper, the analytical solution for the fluid–structure interaction problem after its movable electrode plate touches its fixed electrode plate is obtained, and how to numerically design and calibrate a circular touch mode capacitive rainfall sensor is illustrated for the first time. The numerical results show that the total capacitance and rainwater volume when the circular capacitive rainfall sensor operates in touch mode is indeed much larger than that when the same circular capacitive rainfall sensor operates in non-touch mode, and that the average increase in the maximum membrane stress per unit rainwater volume when the circular capacitive rainfall sensor operates in touch mode can be about 20 times smaller than that when the same circular capacitive rainfall sensor operates in non-touch mode. This is where the circular touch mode capacitive rainfall sensor excels. [ABSTRACT FROM AUTHOR]

The conventional medical assistive strategy for wearable devices involves sensing physiological signals and subsequent analysis while it lacks the capability for visual interaction and often necessitates the use of additional bulky devices for display. Herein, a perceptual and interaction strategy based on electroluminescent and triboelectric sensing 3D stacked wearable device (ETD) is proposed to address current issues. The ETD utilizes a bottom triboelectric sensor for tactile physiological information, which is processed and analyzed, ultimately triggering the selective illumination of the top electroluminescent array through external power circuitry. The ETD possesses the capability to generate a 100 V with 1.5 cm2 and stable visual interaction ability at 30 V drive voltage. Based on outstanding features of the ETD, two medical assistance scenarios are explored. I) The ETD captures physiological signals, such as pulse rate, respiratory rate, and special joint flexion, and in situ displays the corresponding physiological information, effectively assisting the patients/elders in self‐monitoring. II) A bidirectional interactive telemedicine assistive system utilizing ETDs is implemented, which not only gathers real‐time activity status from patients/elders but also realizes that remote physician assesses the safety status and the assessment results are wirelessly back‐communicated to the ETD, enabling real‐time interaction of the physician advice. [ABSTRACT FROM AUTHOR]

Tactile sensing in the human body is achieved via the skin. This has inspired the fabrication of synthetic skins with pressure sensors for potential applications in robotics, bio‐medicine, and human–machine interfaces. Tactile sensors based on magnetic elements are promising as they provide high sensitivity and a wide dynamic range. However, current magnetic tactile sensors mostly detect pressures of solid objects and operate at relatively high forces about 100 mN. Herein, these limitations are addressed by manufacturing soft, stretchable, and hair‐like structures that are permanently magnetized to achieve high‐resolution, cost‐effective, and high‐resolution pressure sensing. Combining these hair‐like structures with advances in 3D magnetic‐field measurements allows us to monitor directional tactile pressures without solid contact. To prove the concept of this technology, a bio‐inspired soft device is built with a hairy structure that senses and reports environmental mechanical stresses, similar to that of human skin. Simple self‐assembly of the soft magnetic hair structure makes our approach easy to scale for large‐area applications. [ABSTRACT FROM AUTHOR]

Endowing tactile feedback for prosthetic hands is profound for upper‐limb amputees. However, existing prosthetic hands are generally not in possession of the embedded sensory feedback. Herein, a flexible tactile sensor array which can be integrated into an untethered soft prosthetic hand to achieve static and dynamic discrimination tasks is presented. The flexible piezoresistive sensory arrays with 25 sensor units which can be arranged on five fingers of the soft prosthetic hand are fabricated. According to the collected large‐scale tactile dataset (including pressure distribution and pressure magnitude) during different grasping tasks, a learning‐based classification model that can reveal the correspondences between tactile information and object attributes while interacting with touched objects is developed. To transfer tactile information extracted from tactile sensor arrays, a wearable vibrotactile feedback band with a spatial coding feedback strategy is implemented by selectively activating vibrotactile motors located on the skin of the upper arm. In a set of tests performed by an individual with transradial amputation and eight able‐bodied subjects, the soft prosthetic hand integrated with tactile sensor arrays can help the users regain finger tactile sensation, discriminate grasped objects, and achieve real‐time dynamic rolling detection. [ABSTRACT FROM AUTHOR]

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In robotic-assisted surgery (RAS), traditional surgical instruments without sensing capability cannot perceive accurate operational forces during the task, and such drawbacks can be largely intensified when sophisticated tasks involving flexible and slender arms with small end-effectors, such as in gastrointestinal endoscopic surgery (GES). In this study, we propose a microelectromechanical system (MEMS) piezoresistive 3-axial tactile sensor for GES forceps, which can intuitively provide surgeons with online force feedback during robotic surgery. The MEMS fabrication process facilitates sensor chips with miniaturized dimensions. The fully encapsulated tactile sensors can be effortlessly integrated into miniature GES forceps, which feature a slender diameter of just 3.5 mm and undergo meticulous calibration procedures via the least squares method. Through experiments, the sensor's ability to accurately measure directional forces up to 1.2 N in the Z axis was validated, demonstrating an average relative error of only 1.18% compared with the full-scale output. The results indicate that this tactile sensor can provide effective 3-axial force sensing during surgical operations, such as grasping and pulling, and in ex vivo testing with a porcine stomach. The compact size, high precision, and integrability of the sensor establish solid foundations for clinical application in the operating theater. [ABSTRACT FROM AUTHOR]

Developing multimodal sensors with human‐like tactile perception is highly desirable for wearable devices, electronic skins (e‐skins), and human‐machine interfaces. However, realizing decoupled signal output and high‐precision measurement remains challenging. Superelastic conductive aerogels are ideal materials for fabricating multimodal sensors as they can convert pressure and temperature stimuli into different electrical signals. Herein, inspired by the microstructure of lightweight and robust avian bones, a biomimetic lamellar silica nanofiber/MXene aerogel (LSMA) sensor for decoupled pressure and temperature sensing is first developed. The avian bone‐like lamellae‐strut structure endows the ultralight LSMA with superb fatigue resistance of 99.1% height retention after 10 000 compression cycles, which is second to none in the reported MXene‐based aerogels. Meanwhile, benefiting from the advantages of the aerogel structure, the LSMA sensor integrating piezoresistive and thermoelectric effects has an ultrahigh temperature resolution of 0.07 K and the lowest pressure detection limit of 0.20 Pa in the reported pressure‐temperature sensors. The unique performance renders it a promising platform for wearable physiological monitoring and tactile e‐skin. Furthermore, an innovative multilevel encryption protection system assisted by machine learning is designed based on the LSMA sensing array as the interactive terminal. This study provides novel insights into the design and application of multimodal sensors. [ABSTRACT FROM AUTHOR]

Introduction: This study investigates the multifaceted nature of motor learning in a complex bimanual task by examining the interplay between mental representation structures, biomechanics, tactile pressure, and performance. We developed a novel maze game requiring participants to maneuver a rolling sphere through a maze, exemplifying complex sequential coordination of vision and haptic control using both hands. A key component of this study is the introduction of cognitive primitives, fundamental units of cognitive and motor actions that represent specific movement patterns and strategies. Methods: Participants were divided into two groups based on initial performance: poor performers (PPG) and good performers (GPG). The experimental setup employed motion capture and innovative tactile sensors to capture a detailed multimodal picture of the interaction process. Our primary aims were to (1) assess the effects of daily practice on task performance, biomechanics, and tactile pressure, (2) examine the relationship between changes in mental representation structures and skill performance, and (3) explore the interplay between biomechanics, tactile pressure, and cognitive representation in motor learning. Results: Performance analysis showed that motor skills improved with practice, with the GPG outperforming the PPG in maze navigation efficiency. Biomechanical analysis revealed that the GPG demonstrated superiormovement strategies, as indicated by higher peak velocities and fewer velocity peaks during task execution. Tactile feedback analysis showed that GPG participants applied more precise and focused pressure with their right-hand thumb, suggesting enhanced motor control. Cognitively, both groups refined their mental representation structures over time, but the GPG exhibited a more structured and sophisticated cognitive mapping of the task post-practice. Discussion: The findings highlight the intertwined nature of biomechanical control, tactile feedback, and cognitive processing in motor skill acquisition. The results support established theories, such as the cognitive action architecture approach, emphasizing the role of mental representation in planning and executing motor actions. The integration of cognitive primitives in our analysis provides a theoretical framework that connects observable behaviors to underlying cognitive strategies, enhancing the understanding of motor learning across various contexts. Our study underscores the necessity of a holistic approach to motor learning research, recognizing the complex interaction between cognitive and motor processes in skill acquisition. [ABSTRACT FROM AUTHOR]

Flexible electronic skin (e-skin) can enable robots to have sensory forms similar to human skin, enhancing their ability to obtain more information from touch. The non-invasive nature of electrical impedance tomography (EIT) technology allows electrodes to be arranged only at the edges of the skin, ensuring the stretchability and elasticity of the skin's interior. However, the image quality reconstructed by EIT technology has deteriorated in multi-touch identification, where it is challenging to clearly reflect the number of touchpoints and accurately size the touch areas. This paper proposed an EIT-based flexible tactile sensor that employs self-made hydrogel material as the primary sensing medium. The sensor's structure, fabrication process, and tactile imaging principle were elaborated. To improve the quality of image reconstruction, the fast iterative shrinkage-thresholding algorithm (FISTA) was embedded into the EIDORS toolkit. The performances of the e-skin in aspects of assessing the touching area, quantitative force sensing and multi-touch identification were examined. Results showed that the mean intersection over union (MIoU) of the reconstructed images was improved up to 0.84, and the tactile position can be accurately imaged in the case of the number of the touchpoints up to seven (larger than two to four touchpoints in existing studies), proving that the combination of the proposed sensor and imaging algorithm has high sensitivity and accuracy in multi-touch tactile sensing. The presented e-skin shows potential promise for the application in complex human–robot interaction (HRI) environments, such as prosthetics and wearable devices. [ABSTRACT FROM AUTHOR]

Cooperative robots that perform safety functions as part of cooperative work with humans have been recently drawing attention. Proximity and tactile sensors that can be retrofitted to a robot's surface are important safety measures for existing robots that are not manufactured as cooperative robots to be used as cooperative robots. In this paper, a string‐like self‐capacitance‐based proximity and tactile sensor that can be easily wrapped around various types of existing robots is proposed. The novel and useful point in this paper is that it can be easily mounted on various robots by a flexible string‐like proximity and tactile sensor. The sensor module can be adjusted to the required length by connecting different numbers of modules depending on the mounting surface. As a demonstration, six prototype sensor modules were connected by each connector and wrapped around a robotic arm. The sensor modules on the robot's surface could detect an object within its proximity range and detect the rough pressure after contact. In addition, the robotic arm with the sensor modules could be controlled in real time using the data obtained from the sensor that are object detection data at proximity range. These results confirm that the proposed sensor module can be used in the proximity and tactile sensors of existing robots. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The dense mechanoreceptors in human fingertips enable texture discrimination. Recent advances in flexible electronics have created tactile sensors that effectively replicate slowly adapting (SA) and rapidly adapting (RA) mechanoreceptors. However, the influence of dermatoglyphic structures on tactile signal transmission, such as the effect of fingerprint ridge filtering on friction‐induced vibration frequencies, remains unexplored. A novel multi‐layer flexible sensor with an artificially synthesized skin surface capable of replicating arbitrary fingerprints is developed. This sensor simultaneously detects pressure (SA response) and vibration (RA response), enabling texture recognition. Fingerprint ridge patterns from notable historical figures – Rosa Parks, Richard Nixon, Martin Luther King Jr., and Ronald Reagan – are fabricated on the sensor surface. Vibration frequency responses to assorted fabric textures are measured and compared between fingerprint replicas. Results demonstrate that fingerprint topography substantially impacts skin‐surface vibrational transmission. Specifically, Parks' fingerprint structure conveyed higher frequencies more clearly than those of Nixon, King, or Reagan. This work suggests individual fingerprint ridge morphological variation influences tactile perception and can confer adaptive advantages for fine texture discrimination. The flexible bioinspired sensor provides new insights into human vibrotactile processing by modeling fingerprint‐filtered mechanical signals at the finger‐object interface. [ABSTRACT FROM AUTHOR]

In the era of Internet of Things (IoT) and Artificial Intelligence (AI), sensors have become an integral part of intelligent systems. Although the traditional sensing technology is very mature in long‐term development, there are remaining defects and limitations that make it difficult to meet the growing demands of current applications, such as high‐sensitivity detection and self‐supplied sensing. As a new type of sensor, array triboelectric nanogenerators (TENG)‐based tactile sensors can respond to wide dynamic range of mechanical stimuli in the surrounding environment and converting them into quantifiable electrical signals, thus realizing real‐time self‐supplied tactile sensing. The array structure allows for fine delineation of the sensing area and improved spatial resolution, resulting in accurate localization and quantification of the detected tactile signals, and have been widely used in wearable devices, smart interaction, medical and health detection, and other fields. In this paper, the latest research progress of functional tactile sensors based on arrayed triboelectric nanogenerators is systematically reviewed from the aspects of working mechanism, material selection, material processing, structural design, functional integration, and application. Finally, the challenges faced by arrayed triboelectric tactile sensors are summarized with a view to providing inspiration and guidance for the future development of tactile sensors. [ABSTRACT FROM AUTHOR]

Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting. A sensitive upconversion nanocrystal-based biometric optical tactile sensor instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. [ABSTRACT FROM AUTHOR]

Several gaps persist in haptic device development due to the multifaceted nature of the sense of touch. Existing gaps include challenges enhancing touch feedback fidelity, providing diverse haptic sensations, and ensuring wearability for delivering tactile stimuli to the fingertips. Here, we introduce the Bioinspired Adaptable Multiplanar Haptic system, offering mechanotactile/steady and vibrotactile pulse stimuli with adjustable intensity (up to 298.1 mN) and frequencies (up to 130 Hz). This system can deliver simultaneous stimuli across multiple fingertip areas. The paper includes a full characterisation of our system. As the device can play an important role in further understanding human touch, we performed human stimuli sensitivity and differentiation experiments to evaluate the capability of delivering mechano-vibrotactile, variable intensity, simultaneous, multiplanar and operator agnostic stimuli. Our system promises to accelerate the development of touch perception devices, providing painless, operator-independent data crucial for researching and diagnosing touch-related disorders. Challenges persist in haptic device development due to the complex nature of touch. The authors propose a Bioinspired Adaptable Multiplanar Haptic system providing multipoint and variable intensity pulse stimuli (0-130 Hz) across multiple fingertip areas, enabling deeper study of touch skin sensors. [ABSTRACT FROM AUTHOR]

Gelatin hydrogel with excellent biodegradability and biocompatibility is widely used in drug delivery, electronic devices, and flexible sensors. However, it is prone to dehydration, which severely restricts its applications. The incorporation of hydrated ions, for example choline chloride (ChCl), can not only improve its water retention capacity but also keep its biocompatibility. Unfortunately, the improvement in water retention is realized at the expense of mechanical properties because the ChCl can suppress the crystallization of gelatin. Here, ChCl is introduced into gelatin hydrogel using a simple post‐treatment to simultaneously improve its water retention capacity and mechanical properties. As a result, the resultant gelatin gel possesses a maximum fracture strength of 5.4 MPa with a strain of 623% and can maintain its weight for at least 30 days. In addition, the gelatin gel exhibits intriguing properties, such as self‐healing, anti‐freezing, anti‐drying, recycling, conductivity, and moldability, which can be successfully served as wearable sensors to monitor human movements. Further, a sensitive touch sensor based on electrode potential is established for the first time using the obtained gel, which can transmit messages through the touch potential signal. [ABSTRACT FROM AUTHOR]

Process microminiaturization is in need not only in chip field, but also in sensor‐related industries. Touch panel sensors as an example have their man‐machine interactive performance in high relation with the circuit resolution. Traditional etchings by yellow light or laser encounter their resolution limit of ≈50 µm; moreover, former pollution using chemical etchants and latter damage to flexible substrates are inevitable. This paper demonstrates an efficient and green patterning technology for flexible silver nanowire (AgNW) transparent and conductive films, which can enable complicated patterns on various types of substrates with high resolution of a 30 µm line width and 40 µm line spacing. The approach uses a water‐soluble photosensitive polymer as the selective protection layer to facilitate the removal of unwanted AgNWs through simple water washing. Due to its good water resistance and mechanical properties, the patterned electrodes exhibit excellent flexibility and environmental stability. As a proof of concept, a capacitive proximity sensor is designed using the patterned AgNW electrodes of the micro feature size, which exhibits excellent proximity sensing performance. The developed patterning technology paves the way to miniaturized feature sizes of different optoelectronic devices for wide applications in fields like new‐style displays, man‐machine interaction, IoT sensing and intelligent robots. [ABSTRACT FROM AUTHOR]

Multimodal tactile sensors, as key information input channel in human‐machine interactions, have faced the significant challenges including high power‐consumption, multimodal data fusion, and wireless transmission. In this work, a battery‐free multimodal wireless tactile sensor (TC‐MWTS) based on tribo‐capacitive coupled effect for normal and shear force fusion sensing is proposed, which is enabled by a 3D structure combining a triboelectric sensor and a capacitive sensor coupled with an inductive coil. A triboelectric sensor equipped with contact‐discharge structures exhibits 25‐fold wireless signal enhancement compared to conventional triboelectric sensors. Based on the characteristics of dual time‐frequency domain information existing in the wireless signals, both normal and shear forces can simultaneously be converted into voltage amplitude V and eigenfrequency f, respectively, without crosstalk and complex decoupling signals. The TC‐MWTS exhibits a maximum sensitivity of 2.47 V kPa−1 for normal force from 2 to 30 kPa and a sensitivity of 0.28 MHz N−1 for shear force between 0.3 and 1.0 N. Finally, the excellent sensing capability of TC‐MWTS to sense complex multidimensional forces in human‐machine interaction is demonstrated. This work innovatively proposes a new mechanism and methodology for effectively fusing and processing multimodal tactile information, which may drive the tremendous development of low‐power multimodal tactile sensing system. [ABSTRACT FROM AUTHOR]

Tactile intent recognition systems, which are highly desired to satisfy human's needs and humanized services, shall be accurately understanding and identifying human's intent. They generally utilize time‐driven sensor arrays to achieve high spatiotemporal resolution, however, which encounter inevitable challenges of low scalability, huge data volumes, and complex processing. Here, an event‐driven intent recognition touch sensor (IR touch sensor) with in‐sensor computing capability is presented. The merit of event‐driven and in‐sensor computing enables the IR touch sensor to achieve ultrahigh resolution and obtain complete intent information with intrinsic concise data. It achieves critical signal extraction of action trajectories with a rapid response time of 0.4 ms and excellent durability of >10 000 cycles, bringing an important breakthrough of tactile intent recognition. Versatile applications prove the integrated functions of the IR touch sensor for great interactive potential in all‐weather environments regardless of shading, dynamics, darkness, and noise. Unconscious and even hidden action features can be perfectly extracted with the ultrahigh recognition accuracy of 98.4% for intent recognition. The further auxiliary diagnostic test demonstrates the practicability of the IR touch sensor in telemedicine palpation and therapy. This groundbreaking integration of sensing, data reduction, and ultrahigh‐accuracy recognition will propel the leapfrog development for conscious machine intelligence. [ABSTRACT FROM AUTHOR]

A cleanroom free optimized fabrication of a low-cost facile tungsten diselenide (WSe2) combined with chitosan-based hydrogel device is reported for multifunctional applications including tactile sensing, pulse rate monitoring, respiratory rate monitoring, human body movements detection, and human electrophysiological signal detection. Chitosan being a natural biodegradable, non-toxic compound serves as a substrate to the semiconducting WSe2 electrode which is synthesized using a single step hydrothermal technique. Elaborate characterization studies are performed to confirm the morphological, structural, and electrical properties of the fabricated chitosan/WSe2 device. Chitosan/WSe2 sensor with copper contacts on each side is put directly on skin to capture human body motions. The resistivity of the sample was calculated as 26 kΩ m−1. The device behaves as an ultrasensitive pressure sensor for tactile and arterial pulse sensing with response time of 0.9 s and sensitivity of around 0.02 kPa−1. It is also capable for strain sensing with a gauge factor of 54 which is significantly higher than similar other reported electrodes. The human body movements sensing can be attributed to the piezoresistive character of WSe2 that originates from its non-centrosymmetric structure. Further, the sensor is employed for monitoring respiratory rate which measures to 13 counts/min for healthy individual and electrophysiological signals like ECG and EOG which can be used later for detecting numerous pathological conditions in humans. Electrophysiological signal sensing is carried out using a bio-signal amplifier (Bio-Amp EXG Pill) connected to Arduino. The skin-friendly, low toxic WSe2/chitosan dry electrodes pave the way for replacing wet electrodes and find numerous applications in personalized healthcare. [ABSTRACT FROM AUTHOR]

Flexible sensors have been applied to accurately identify the basic features of various solid objects. However, a significant obstacle is the accurate identification of liquids, due to it is impossible to exert force directly on those. Here, an optical‐thermal flexible tactile sensor is designed for liquid recognition, which integrates PEDOT:PSS/SrTiO3 thermoelectric foam and ZnS‐CaZnOS mechanoluminescent (ML) film coated with thermoplastic polyurethane (TPU), enabling the simultaneous detection of thermal and optical changes with low interference, via its exceptional mechanical stimuli‐caused light and superior thermoelectric voltage, respectively. In addition, the TPU coating imparts the device with excellent hydrophobic and oleophobic properties, enhancing its durability in complex conditions. This flexible sensor, possessing high stability under thermal and mechanical cycles, can distinguish different pure solvents and turbid suspensions, achieving a high‐fine recognition accuracy of 95% and 97.5%, respectively, with the aid of the fusion algorithm of multimodal sensory data based on the thermal and optical outputs. This capability of the device can underscore the practical utility of humanoid robots to identify hazardous liquids in some scenarios like safety inspection and kitchen condiments, which addresses the critical gaps in current sensory technologies by providing a robust, sensitive, and multifunctional strategy for accurate liquid recognition. [ABSTRACT FROM AUTHOR]

Bioinspired approaches tend to mimic some biological functions for the purpose of creating more efficient and robust systems. These can be implemented in both software and hardware designs. A neuromorphic software part can include, for example, Spiking Neural Networks (SNNs) or event-based representations. Regarding the hardware part, we can find different sensory systems, such as Dynamic Vision Sensors, touch sensors, and actuators, which are linked together through specific interface boards. To run real-time SNN models, specialised hardware such as SpiNNaker, Loihi, and TrueNorth have been implemented. However, neuromorphic computing is still in development, and neuromorphic platforms are still not easily accessible to researchers. In addition, for Neuromorphic Robotics, we often need specially designed and fabricated PCBs for communication with peripheral components and sensors. Therefore, we developed an all-in-one neuromorphic system that emulates neuromorphic computing by running a Virtual Machine on a conventional low-power CPU. The Virtual Machine includes Python and Brian2 simulation packages, which allow the running of SNNs, emulating neuromorphic hardware. An additional, significant advantage of using conventional hardware such as Raspberry Pi in comparison to purpose-built neuromorphic hardware is that we can utilise the built-in physical input–output (GPIO) and USB ports to directly communicate with sensors. As a proof of concept platform, a robotic goalkeeper has been implemented, using a Raspberry Pi 5 board and SNN model in Brian2. All the sensors, namely DVS128, with an infrared module as the touch sensor and Futaba S9257 as the actuator, were linked to a Raspberry Pi 5 board. We show that it is possible to simulate SNNs on a conventional low-power CPU running real-time tasks for low-latency and low-power robotic applications. Furthermore, the system excels in the goalkeeper task, achieving an overall accuracy of 84% across various environmental conditions while maintaining a maximum power consumption of 20 W. Additionally, it reaches 88% accuracy in the online controlled setup and 80% in the offline setup, marking an improvement over previous results. This work demonstrates that the combination of a conventional low-power CPU running a Virtual Machine with only selected software is a viable competitor to neuromorphic computing hardware for robotic applications. [ABSTRACT FROM AUTHOR]

Thermoelectrics is the simplest technology applicable for direct energy conversion between heat and electricity. After over 60 years of fruitful research efforts, recent boom in flexible electronics has promoted the rapid development of flexible thermoelectrics with rising performances, discovery of new materials and concepts, unconventional device configuration, and emerging applications not possible for traditional thermoelectric (TE) semiconductors. In this Perspective, we first overview representative flexible TE materials, then discuss recent breakthroughs for flexible TE devices assembled from various types of TE materials employing different technical routes. They exhibit promising power generation and sensing performances, and aim for applications in wearable electronics, such as the power supply harvesting heat from body for low-power electronics, temperature sensors for tactile e-skin, and newly emerged application as a thermo-haptic device in an extended reality system. [ABSTRACT FROM AUTHOR]

As a combination of fiber optics and nanotechnology, optical micro/nanofiber (MNF) is considered as an important multifunctional building block for fabricating various miniaturized photonic devices. With the rapid progress in flexible opto-electronics, MNF has been emerging as a promising candidate for assembling tactile sensors and soft actuators owing to its unique optical and mechanical properties. This review discusses the advances in MNF enabled tactile sensors and soft actuators, specifically, focusing on the latest research results over the past 5 years and the applications in health monitoring, human-machine interfaces, and robotics. Future prospects and challenges in developing flexible MNF devices are also presented.

Humans can grab and identify objects by capturing the features of physical shape, temperature, and material. However, creating artificial sensors with finger‐like tactile capabilities is challenging due to the contradiction between structural compliance and multifunctional sensing. Here, a sensory system for a robot hand that can achieve object recognition is reported. The sensor with a simple horizontal sensing structure can respond to strain, material, and temperature stimuli using piezoresistive, triboelectric, and thermoelectric effects. A machine learning algorithm to train and classify tactile sensing signals, enabling a high accuracy of 98.5% in recognizing five objects is also developed. This work contributes to the development of sensory systems for object recognition in soft robotics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent wearable technology, multimodal tactile sensors capable of data acquisition, decoupling of intermixed signals, and information processing have attracted increasing attention. Herein, a decoupled temperature–pressure dual‐mode sensor is developed based on single‐walled carbon nanotubes (SWCNT) and poly(3,4‐ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS) decorated porous melamine foam (MF), integrating with a deep learning algorithm to obtain a multimodal input terminal. Importantly, the synergistic effect of PEDOT:PSS and SWCNT facilitates the sensor with ideal decoupling capability and sensitivity toward both temperature (38.2 µV K−1) and pressure (10.8% kPa−1) based on the thermoelectric and piezoresistive effects, respectively. Besides, the low thermal conductivity and excellent compressibility of MF also endow it with the merits of a low‐temperature detection limit (0.03 K), fast pressure response (120 ms), and long‐term stability. Benefiting from the outstanding sensing characteristics, the assembled sensor array showcases good capacity for identifying spatial distribution of temperature and pressure signals. With the assistance of a deep learning algorithm, it displays high recognition accuracy of 99% and 98% corresponding to “touch” and “press” actions, respectively, and realizes the encrypted transmission of information and accurate identification of random input sequences, providing a promising strategy for the design of high‐accuracy multimodal sensing platform in human–machine interaction. [ABSTRACT FROM AUTHOR]

The sense of touch is a property that allows humans to interact delicately with their physical environment. This article reports on a technological advancement in intuitive human-robot interaction that enables an intrinsic robotic sense of touch without the use of artificial skin or tactile instrumentation. On the basis of high-resolution joint-force-torque sensing in a redundant arrangement, we were able to let the robot sensitively feel the surrounding environment and accurately localize touch trajectories in space and time that were applied on its surface by a human. Through an intertwined combination of manifold learning techniques and artificial neural networks, the robot identified and interpreted those touch trajectories as machine-readable letters, symbols, or numbers. This opens up unexplored opportunities in terms of intuitive and flexible interaction between human and robot. Furthermore, we showed that our concept of so-called virtual buttons can be used to straightforwardly implement a tactile communication link, including switches and slider bars, which are complementary to speech, hardware buttons, and control panels. These interaction elements could be freely placed, moved, and configured in arbitrary locations on the robot structure. The intrinsic sense of touch we proposed in this work can serve as the basis for an advanced category of physical human-robot interaction that has not been possible yet, enabling a shift from conventional modalities toward adaptability, flexibility, and intuitive handling. Editor's summary: Tactile sensors and electronic skins are common devices that provide robots with a sense of physical interaction, but they become complex and costly when used to cover large portions of a robot. Using internal high-resolution joint-force-torque sensors, Iskandar et al. realized an intrinsic, full-body sense of touch in a robotic arm. Deep learning techniques and an artificial neural network allow the robot to sense the location, direction, and magnitude of a force applied anywhere on its surface. The robot can recognize and react to characters, such as a number drawn on its surface, and virtual buttons or sliders, providing users with an intuitive way to interact with a robot. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

The sustainable tactile electronics demonstrates huge potential in mimicking the functionality of human skin and satisfies with an eco‐friendly concept. However, on the premise of successfully introducing natural materials, such electronics are still not sufficient for improving the fatigue threshold in high‐frequency sensing scenarios. Here an eco‐ and user‐friendly cellulose integrated tactile array (CITA) is introduced that relies on laminated hierarchical architecture (LAHA) for alleviating the notorious structural vulnerability toward long‐term haptic evaluation. By cross‐validation with conventional bulky configuration, finite element simulation unveils that the LAHA leverages compact laminated adjacent layer for dramatically facilitating in‐plane stress distribution for diminishing the interfacial stress concentration, thus affords prolonged and reliable sensory augmentation. The CITA wireless monitoring system offers impeccable real‐time spatiotemporal haptic patterns on multi‐user interfaces and can substantially promote a record‐high durability (150000 cycles), showcasing low interfacial contact impedance (1.78 ± 0.4 ohm, 1 kHz), remarkably channel uniformity (97.2%), unparalleled sensitivity (12944 kPa−1), and sensing‐robustness against perturbations (e.g., humidity, temperature, and bending). It is envisioned that the proposed CITA system will open up new avenues for sustainable tactile electronics in continuous health surveillance. [ABSTRACT FROM AUTHOR]

Recent developments in robotics increasingly highlight the importance of sensing technology, especially tactile perception, in enabling robots to effectively engage with their environment and interpret physical interactions. Due to power efficiency and low cost, the triboelectric mechanism has been frequently studied for measuring pressure and identifying materials to enhance robot perception. Nevertheless, there has been limited exploration of using the triboelectric effect to detect curved surfaces, despite their prevalence in daily lives. Here, a triboelectric multimodal tactile sensor (TMTS) of multilayered structural design is proposed to recognize distinct materials, curvatures, and pressure simultaneously, thus decoupling different modalities to enable more accurate detection. By attaching sensors to robotic fingertips and leveraging deep learning analytics, the quantitative curvature measurement provides more precise insights into an object's detailed geometric characteristics rather than merely assessing its overall shape, hence achieving automatic recognition of 12 grasped objects with 99.2% accuracy. The sensor can be further used to accurately recognize the softness of objects under different touch gestures of a robotic hand, achieving a 94.1% accuracy, demonstrating its significant potential for wide‐ranging applications in a future robotic‐enabled intelligent society. [ABSTRACT FROM AUTHOR]

Robots execute diverse load operations, including carrying, lifting, tilting, and moving objects, involving load changes or transfers. This dynamic process can result in the shift of interactive operations from stability to instability. In this paper, we respond to these dynamic changes by utilizing tactile images captured from tactile sensors during interactions, conducting a study on the dynamic stability and instability in operations, and propose a real-time dynamic state sensing network by integrating convolutional neural networks (CNNs) for spatial feature extraction and long short-term memory (LSTM) networks to capture temporal information. We collect a dataset capturing the entire transition from stable to unstable states during interaction. Employing a sliding window, we sample consecutive frames from the collected dataset and feed them into the network for the state change predictions of robots. The network achieves both real-time temporal sequence prediction at 31.84 ms per inference step and an average classification accuracy of 98.90%. Our experiments demonstrate the network's robustness, maintaining high accuracy even with previously unseen objects. [ABSTRACT FROM AUTHOR]

Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural processes. Major advances in the use of EGTs for neuromorphic sensory applications such as tactile sensors, visual neuromorphic systems, chemical neuromorphic systems, and multimode neuromorphic systems are carefully discussed. Furthermore, the challenges and future directions of the field are explored, highlighting the potential of EGT-based biomimetic systems to revolutionize neuromorphic prosthetics, robotics, and human–machine interfaces. Through a comprehensive analysis of the latest research, this review is intended to provide a detailed understanding of the current status and future prospects of biomimetic neuromorphic sensory systems via EGT sensing and integrated technologies. [ABSTRACT FROM AUTHOR]

Development of deformable soft tactile sensors that are conformable to 3D surfaces and recognize dynamic/static multiple touches from all directions are still great technological challenges. Furthermore, distinguishing material species of various objects and proactive sensing by non‐contact recognition have not been achieved in the deformable tactile sensors. This study suggests an induction‐based array sensor with only two signal terminals. The tactile sensor simultaneously solves the technological challenges mentioned above. The key to this success is novel interpretation on the inductance variation by material species of the objects depending on measurement frequency. This study discusses the design rule of the sensor and the operation variables for effective tactile sensing both in the contact and non‐contact modes. An artificial sensor covering the finger (including the finger tip) demonstrates recognition of dynamic and static multiple stimuli on the finger. [ABSTRACT FROM AUTHOR]

In order for a robot to manipulate an object, it is required to estimate the object's pose. The Manifold Particle Filter, an extension of the widely used Particle Filter as a state estimation method, was proposed for the object pose estimation. The Manifold Particle Filter can utilize tactile information, but it has only been applied to binary observations. In this paper, Continuous-MPF, which can use multidimensional and continuous observation information, is proposed. Additionally, we propose an extension of Continuous-MPF, which can store and utilize non-contact information in order to improve the efficiency of estimation. The proposed method was implemented in a robot system and experimentally validated. Good estimation results were obtained for most of the given conditions. [ABSTRACT FROM AUTHOR]

The capability to emulate skin‐like temperature and pressure sensing is fundamental for next‐generation artificial intelligence products. However, detecting temperature and pressure simultaneously with a single sensor without signal interference is challenging. Herein, a novel PCC aerogel sensor composed of PEDOT:PSS, CNTs, and CNF via directional freezing technology is developed. The PCC sensor can decouple temperature and pressure stimuli into individual voltage and resistance signals. It exhibits high‐precision temperature sensing capabilities, boasting an exceptionally high Seebeck coefficient of 30.4 µV K‐1 and the ability to detect temperature variations as low as 0.1 K. PCC sensors show excellent sensitivity and fast response times for detecting static and dynamic pressures, as well as high stability after 1000 cycles. Its maximum pressure sensitivity can reach 159.1% kPa−1, and the lowest detection limit is 10 Pa. Additionally, its excellent thermoelectric properties also enable to generating thermopower from human skin for self‐powered pressure sensing. A 3×3 PCC sensor array has been proposed to simulate the unique features of human skin in temperature and pressure recognition. This work provides a scalable manufacturing strategy for multi‐functional tactile sensors. [ABSTRACT FROM AUTHOR]

In traditional food industry, the assessment of mushy materials plays an important role in high‐quality food production, which still relies heavily on human tactile perception. In this work, to address this issue for enhancing food production efficiency, a flexible electret tactile sensor that can mimic expert touch is developed. The sensor consists of a pair of electrodes, a microstructural spacer, and a pre‐charged electret. Benefiting from the electrostatic induction‐based working mechanism, the sensor attains high sensitivity and is ideal for precise sensing in actions similar to those of skilled Baijiu distillers. Due to its hermetic and electromagnetic interference‐resistant encapsulation with polypropylene/aluminum/parylene films, the sensor remained durable in vinasse in the real distillery, with its high water and alcohol content, for over 21 days. This demonstrates its long‐term stability in harsh environment. Based on the proposed flexible electret tactile sensor, an automated and intelligent vinasse identification system is built, mimicking the actions of Baijiu distillers in vinasse assessment. Combining tactile sensing data with machine learning, the system can distinguish 8 kinds of vinasses with different ingredient ratios, achieving an accuracy of 98%. This work significantly demonstrates the practical potential of the sensor in the food industry. [ABSTRACT FROM AUTHOR]

Laser‐induced graphene (LIG) technology has provided a new manufacturing strategy for the rapid and scalable assembling of triboelectric nanogenerators (TENG). However, current LIG‐based TENG commonly rely on polymer films, e.g., polyimide (PI) as both friction material and carbon precursor of electrodes, which limit the structural diversity and performance escalation due to its incapability of folding and creasing. Using specialized PI paper composed of randomly distributed PI fibers to substantially enhance its foldability, this work creates a new type of TENG, which are structurally foldable and stackable, and performance tailorable. First, by systematically investigating the laser power‐regulated performance of single‐unit TENG, the open‐circuit voltage can be effectively improved. By further exploiting the folding process, multiple TENG units can be assembled together to form multi‐layered structures to continuously expand the open‐circuit voltage from 5.3 to 34.4 V cm−2, as the increase of friction units from 1 to 16. Last, by fully utilizing the unique structure and performance, representative energy‐harvesting and smart‐sensing applications are demonstrated, including a smart shoe to recognize running motions and power LEDs, a smart leaf to power a thermometer by wind, a matrix sensor to recognize writing trajectories, as well as a smart glove to recognize different objects. [ABSTRACT FROM AUTHOR]

Monitoring tactile pressure and recognizing action are important functionalities for artificial electronic skin (e‐skin). Furthermore, in order to create conformable coverings for 3D objects, an e‐skin needs to be stretchable, without sacrificing sensitivity to tactile pressure. However, stretching of sensors normally affects their output stability. In this study, a stretchable e‐skin is developed using laser‐induced graphene and a liquid metal alloy, GaInSn, in an elastic ecoflex polymer to create a stretchable, resistive‐type tactile pressure sensor. Furthermore, a pressure sensor array is fabricated as an e‐skin, and output is signal‐processed using machine learning. With this system, the e‐skin also monitors its stretching state, with the result that tactile pressure can be calculated regardless of the degree of stretching. With machine learning‐assisted e‐skin, actions such as patting, sliding, and grabbing are successfully recognized in the manner of human skin. [ABSTRACT FROM AUTHOR]

This research proposes a sensor for tracking the motion of a human head via optical tactile sensing. It implements the use of a fibrescope a non-metal alternative to a webcam. Previous works have included robotics grippers to mimic the sensory features of human skin, that used monochrome cameras and depth cameras. Tactile sensing has shown advantages in feedback-based interactions between robots and their environment. The methodology in this paper is utilised to track motion of objects in physical contact with these sensors to replace external camera based motion capture systems. Our immediate application is related to detection of human head motion during radiotherapy procedures. The motion was analysed in two degrees of freedom, respective to the tactile sensor (translational in z-axis, and rotational around y-axis), to produce repeatable and accurate results. The movements were stimulated by a robot arm, which also provided ground truth values from its end- effector. The fibrescope was implemented to ensure the device's compatibility with electromagnetic waves. The cameras and the ground truth values were time synchronised using robotics operating systems tools. Image processing methods were compared between grayscale and binary image sequences, followed by motion tracking estimation using deterministic approaches. These included Lukas-Kanade Optical Flow and Simple Blob Detection, by OpenCV. The results showed that the grayscale image processing along with the Lukas- Kanade algorithm for motion tracking can produce better tracking abilities, although further exploration to improve the accuracy is still required. [ABSTRACT FROM AUTHOR]

Tactile sensing has become indispensable for contact-rich dynamic robotic manipulation tasks. It provides robots with a better understanding of the physical environment, which is a vital supplement to robotic vision perception. Compared with other existing tactile sensors, vision-based tactile sensors (VBTSs) stand out for augmenting the tactile perception capabilities of robotic systems, owing to superior spatial resolution and cost-effectiveness. Despite their advantages, VBTS production faces challenges due to the lack of standardised manufacturing techniques and heavy reliance on manual labour. This limitation impedes scalability and widespread adoption. This paper introduces a rapid monolithic manufacturing technique and evaluates its performance quantitatively. We further develop and assess C-Sight, a novel VBTS sensor manufactured using this technique, focusing on its tactile reconstruction capabilities. Experimental results demonstrate that the monolithic manufacturing technique enhances VBTS production efficiency significantly. Also, the fabricated C-Sight sensor exhibits its reliable tactile perception and reconstruction capabilities, proofing the validity and feasibility of the monolithic manufacturing method. [ABSTRACT FROM AUTHOR]

In this work, the development of a flexible Double‐Gate (DG) organic thin film transistor (DG‐OTFT), and its employment is reported for the realization of multimodal tactile sensors. Due to the self‐encapsulation of the stacked DG architecture, highly stable organic transistors are obtained that show almost negligible degradation after 6 months. Moreover, such configuration is also very useful for the development of sensing devices. In the case, one of the two gates is used to bias and set the working point of the devices, whereas the second one is connected to a polyvinylidene fluoride(PVDF)‐capacitor, a pyro/piezoelectric material. It is demonstrated that the charge displacement induced by the PVDF capacitor due to an applied external pressure or due to a temperature variation led to a reproducible variation of the device's output current. Using this approach high‐performing multimodal tactile sensors are obtained with sensitivity to up to 241 nA N−1 and 442 nA °C−1 respectively. [ABSTRACT FROM AUTHOR]

Remote Patient Monitoring has enjoyed strong growth to new heights driven by several factors, such as the COVID-19 pandemic or advances in technology, allowing consumers and patients to continuously record health data by themselves. This does not come without its challenges, however. A literature review was completed and highlights usability gaps when using wearables or home use medical devices in a virtual environment. Based on these findings, the Pi-CON methodology was applied to close these gaps by utilizing a novel sensor that allows the acquisition of vital signs at a distance, without any sensors touching the patient. Pi-CON stands for passive, continuous and non-contact, and describes the ability to acquire vital signs continuously and passively, with limited user interaction. The preference of vital sign acquisition with a newly developed sensor was tested and compared to vital sign tests taken with patient generated health-data devices (ear thermometer, pulse oximeter) measuring heart rate, respiratory rate and body temperature. In addition, the amount of operator errors and the user interfaces were tested and compared. Results show that participants preferred vital signs acquisition with the novel sensor and the developed user interface of the sensor. Results also revealed that participants had a mean error of.85 per vital sign measurement with the patient-generated health data devices and.33 with the developed sensor, confirming the beneficial impact available when using the developed sensor based on the Pi-CON methodology. [ABSTRACT FROM AUTHOR]