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Real‐time detection and differentiation of diverse external stimuli with one tactile senor remains a huge challenge and largely restricts the development of electronic skins. Although different sensors have been described based on piezoresistivity, capacitance, and triboelectricity, and these devices are promising for tactile systems, there are few, if any, piezoelectric sensors to be able to distinguish diverse stimuli in real time. Here, a human skin‐inspired piezoelectric tactile sensor array constructed with a multilayer structure and row+column electrodes is reported. Integrated with a signal processor and a logical algorithm, the tactile sensor array achieves to sense and distinguish the magnitude, positions, and modes of diverse external stimuli, including gentle slipping, touching, and bending, in real time. Besides, the unique design overcomes the crosstalk issues existing in other sensors. Pressure sensing and bending sensing tests show that the proposed tactile sensor array possesses the characteristics of high sensitivity (7.7 mV kPa−1), long‐term durability (80 000 cycles), and rapid response time (10 ms) (less than human skin). The tactile sensor array also shows a superior scalability and ease of massive fabrication. Its ability of real‐time detection and differentiation of diverse stimuli for health monitoring, detection of animal movements, and robots is demonstrated. Human skin‐inspired piezoelectric tactile sensor array can sense and distinguish the magnitude, positions, and modes of diverse external stimuli in real time. The dual‐layer comb structures of the sensor array with row+column electrodes eliminate crosstalk and reduce the number of connection wires. It excavates enormous applications in various settings, such as health monitoring, detection of animal movements, and robots.

PurposeThis paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.Design/methodology/approachThis paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.FindingsThis research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.Originality/valueThe paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

Flexible stress sensor arrays, comprising multiple flexible stress sensor units, enable accurate quantification and analysis of spatial stress distribution. Nevertheless, the current implementation of flexible stress sensor arrays faces the challenge of excessive signal wires, resulting in reduced deformability, stability, reliability, and increased costs. The primary obstacle lies in the electric amplitude modulation nature of the sensor unit’s signal (e.g., resistance and capacitance), allowing only one signal per wire. To overcome this challenge, the single-line multi-channel signal (SLMC) measurement has been developed, enabling simultaneous detection of multiple sensor signals through one or two signal wires, which effectively reduces the number of signal wires, thereby enhancing stability, deformability, and reliability. This review offers a general knowledge of SLMC measurement beginning with flexible stress sensors and their piezoresistive, capacitive, piezoelectric, and triboelectric sensing mechanisms. A further discussion is given on different arraying methods and their corresponding advantages and disadvantages. Finally, this review categorizes existing SLMC measurement methods into RLC series resonant sensing, transmission line sensing, ionic conductor sensing, triboelectric sensing, piezoresistive sensing, and distributed fiber optic sensing based on their mechanisms, describes the mechanisms and characteristics of each method and summarizes the research status of SLMC measurement.

In this study, a facile and scalable fabrication process of the flexible and transparent tactile sensor array where its operation is based on the triboelectric effect is proposed. The overall process is assisted by the solution based screen printing method, which is well-known as advantageous in the batch fabrication of the electronic devices. The introduction of the batch fabrication process to the tactile sensor array enables the realization of high functionality such as transparency and flexibility. The performance of the fabricated tactile sensor array is systematically investigated through the parametric studies and the discernibility to multiple external physical stimuli such as vertical contact and horizontal sliding is verified. It is confirmed that although the present self-triggered tactile sensor array could be easily fabricated and scaled-up without cumbersome processes, the functionalities such as sensitivity, resolution, transparency, flexibility and even peak power density, are comparable to previously developed tactile sensors. Consequently, the scalable and design-flexible fabrication methodology with cost-effect and high production speed in this study effectively broadens the applicability of the sensor array. As proof-of-concept demonstration of potential applications, a stand-alone sensory lighting system as well as a wearable transparent code generator are developed and their performances are investigated.

Flexible piezoresistive sensor array has broad application prospects in human-computer interaction. However, due to the complexity of reality, it is difficult to balance flexibility and perceptual ability in the process of tactile perception. Presented herein is a 4 × 4 matrix of a piezoresistive tactile sensor (TS) that is both pliable and composed of a porous blend of multi-walled carbon nanotubes (MWCNTs) and polydimethylsiloxane (PDMS). This sensor matrix is endowed with characteristics like pliability, consistency, and acute sensitivity, which facilitate its adherence to various shaped surface profiles. It boasts a peak sensitivity of 0.6 kPa−1 and is capable of detecting pressures within a broad spectrum from 0 to 640 kPa. An in-depth examination has been undertaken to assess the TS array's response to pressure, encompassing aspects such as hysteresis and repeatability. In addition to this, a scanning system for the array has been constructed to promptly detect, digitize, and present the pressure applied. A neural network model for three-dimensional force decoupling has been established to analyze the real-time data emanating from the sensor matrix, thereby enabling the precise forecasting of the three-dimensional force exerted upon the array.

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. A flexible piezoresistive sensory array with 25 sensor units is presented and integrated into an unthethered soft prosthetic hand. Based on the learning‐based algorithm, tactile information can be extracted when the sensory soft hand interacts with environments, which can help users restore tactile sensation in both static and dynamic tasks.

As an essential perceptual device, the tactile sensor can efficiently improve robot intelligence by providing contact force perception to develop algorithms based on contact force feedback. However, current tactile grasping technology lacks high-performance sensors and high-precision grasping prediction models, which limits its broad application. Herein, an intelligent robot grasping system that combines a highly sensitive tactile sensor array was constructed. A dataset that can reflect the grasping contact force of various objects was set up by multiple grasping operation feedback from a tactile sensor array. The stability state of each grasping operation was also recorded. On this basis, grasp stability prediction models with good performance in grasp state judgment were proposed. By feeding training data into different machine learning algorithms and comparing the judgment results, the best grasp prediction model for different scenes can be obtained. The model was validated to be efficient, and the judgment accuracy was over 98% in grasp stability prediction with limited training data. Further, experiments prove that the real-time contact force input based on the feedback of the tactile sensor array can periodically control robots to realize stable grasping according to the real-time grasping state of the prediction model.

Wearable sensors are gaining in popularity because they enable outdoor experimental monitoring. This paper presents a cost-effective sensorised insole based on a mesh of tactile capacitive sensors. Each sensor’s spatial resolution is about 4 taxels/cm 2 in order to have an accurate reconstruction of the contact pressure distribution. As a consequence, the insole provides information such as contact forces, moments, and centre of pressure. To retrieve this information, a calibration technique that fuses measurements from a vacuum chamber and shoes equipped with force/torque sensors is proposed. The validation analysis shows that the best performance achieved a root mean square error (RMSE) of about 7   N for the contact forces and 2   N m for the contact moments when using the force/torque shoe data as ground truth. Thus, the insole may be an alternative to force/torque sensors for certain applications, with a considerably more cost-effective and less invasive hardware.

The past several decades have witnessed great progress in high‐performance field effect transistors (FET) as one of the most important electronic components. At the same time, due to their intrinsic advantages, such as multiparameter accessibility, excellent electric signal amplification function, and ease of large‐scale manufacturing, FET as tactile sensors for flexible wearable devices, artificial intelligence, Internet of Things, and other fields to perceive external stimuli has also attracted great attention and become a significant field of general concern. More importantly, FET has a unique three‐terminal structure, which enables its different components to detect external mechanics through different sensing mechanisms. On one hand, it provides an important platform to shed deep insights into the underlying mechanisms of the tactile sensors. On the other hand, these properties could in turn endow excellent components for the construction of tactile matrix sensor arrays with high quality. With special emphasis on the configuration of FETs, this review classified and summarized structure‐optimized FET tactile sensors with gate, dielectric layer, semiconductor layer, and source/drain electrodes as sensing active components, respectively. The working principles and the state‐of‐the‐art protocols in terms of high‐performance tactile sensors are detail discussed and highlighted, the innovative pixel distribution and integration analysis of the transistor sensor matrix array concerning flexible electronics are also introduced. We hope that the introduction of this review can provide some inspiration for future researchers to design and fabricate high‐performance FET‐based tactile sensor chips for flexible electronics and other fields. This review focuses on FET‐based tactile pressure sensors. The working principles of this kind of tactile sensors are discussed in detail, the state‐of‐the‐art protocols for high‐performance tactile sensing are highlighted, and the major advances in large‐scale tactile sensor arrays and their applications in robotics, health care, and smart manufacturing in terms of transistor matrix are also introduced.

Ever since the development of digital devices, the recognition of human gestures has played an important role in many Human-Computer interface applications. Various wearable devices have been developed, and inertial sensors, magnetic sensors, gyro sensors, electromyography, force-sensitive resistors, and other types of sensors have been used to identify gestures. However, there are different drawbacks for each sensor, which affect the detection of gestures. In this paper, we present a new gesture recognition method using a Flexible Epidermal Tactile Sensor based on strain gauges to sense deformation. Such deformations are transduced to electric signals. By measuring the electric signals, the sensor can estimate the degree of deformation, including compression, tension, and twist, caused by movements of the wrist. The proposed sensor array was demonstrated to be capable of analyzing the eight motions of the wrist, and showed robustness, stability, and repeatability throughout a range of experiments aimed at testing the sensor array. We compared the performance of the prototype device with those of previous studies, under the same experimental conditions. The result shows our recognition method significantly outperformed existing methods.