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The development of pressure sensor arrays capable of distinguishing the shape and texture details of objects is of considerable interest in the emerging fields of smart robots, prostheses, human-machine interfaces, and artificial intelligence (AI). Here we report an integrated pressure sensor array, by combining solution-processed two-dimensional (2D) MoS 2 van der Waals (vdW) thin film transistor (TFT) active matrix and conductive micropyramidal pressure-sensitive rubber (PSR) electrodes made of polydimethylsiloxane/carbon nanotube composites, to achieve spatially revolved pressure mapping with excellent contrast and low power consumption. We demonstrate a 10 × 10 active matrix by using the 2D MoS 2 vdW-TFTs with high on-off ratio > 10 6 , minimal hysteresis, and excellent device-to-device uniformity. The combination of the vdW-TFT active matrix with the highly uniform micropyramidal PSR electrodes creates an integrated pressure sensing array for spatially resolved pressure mapping. This study demonstrates that the solution-processed 2D vdW-TFTs offer a solution for active-matrix control of pressure sensor arrays, and could be extended for other active-matrix arrays of electronic or optoelectronic devices.

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.

Artificial tactile systems play a pivotal role in advancing human‐machine interaction technology by enabling precise physical interaction with objects and environments. Tactile information, such as pressure and temperature, allows robots to manipulate objects accurately and interact safely with humans. To facilitate this, a robotic skin integrating flexible pressure and temperature sensor arrays has been developed. The capacitive pressure sensor, inspired by human skin and utilizing a micro‐dome structure, demonstrates fast, stable, and sensitive performance under applied pressure. Also, the resistive temperature sensor, based on reduced graphene oxide, exhibits highly sensitive responses to temperature changes, characterized by rapid and linear behavior. These sensors are vertically integrated into a multilayered system capable of simultaneously detecting real‐time pressure and temperature distribution. This integrated sensor system, when incorporated into a robotic gripper, enables accurate identification of object shapes and surface temperatures during manipulation tasks. By pairing the sensor system with a camera that captures macroscopic visual information, including areas not directly visible, robots achieve enhanced manipulation capabilities through the synergy of visual context and detailed tactile input. This development represents a fundamental technology for multimodal tactile recognition and highlights its potential applications in artificial intelligence‐driven visual‐tactile fusion technologies. Temperature and pressure sensing are critical for human‐machine interaction applications, allowing robots to identify objects and surface characteristics for safe and precise manipulation. A multilayered robotic skin integrating pressure and temperature sensors is developed to enable simultaneous real‐time detection. This technology is fundamental for artificial intelligence‐vision‐tactile convergence, advancing the development of human‐like robotic systems.

Human hands can envelop the surface of an object and recognize its shape through touch. However, existing stretchable haptic sensors exhibit limited flexibility and stability to detect pressure during deformation, while also solely achieving recognition of planar objects. Inspired by the structure of skin tissue, an embedded construction‐enabled liquid metal‐based e‐skin composed of a liquid metal microstructured electrode (LM‐ME) array is fabricated for curved pressure mapping. The embedded LM‐ME‐based sensor elements are fabricated by using femtosecond laser‐induced micro/nanostructures and water/hydrogel assisted patterning method, which enables high sensitivity (7.42 kPa−1 in the range of 0–0.1 kPa) and high stability through an interlinked support isolation structure for the sensor units. The sensor array with a high interfacial toughness of 1328 J m−2 can maintain pressure sensation under bending and stretching conditions. Additionally, the embedded construction and laser‐induced bumps effectively reduce crosstalk from 58 to 7.8% compared to conventional flexible sensors with shared surfaces. The stretchable and mechanically stable sensor arrays possess shape‐adaptability that enables pressure mapping on non‐flat surfaces, which has great potential for object recognition in robotic skins and human‐computer interaction. An all‐soft pressure sensor array is presented by femtosecond laser‐induced micro/nanostructures and developing the formation method of liquid metal film on a rough surface of PDMS. The sensor array exhibits stability, high sensitivity, and low cross‐talk by optimizing the structure design, enabling conformal and shape‐adaptable attachment to curved surfaces for object recognition.

There is a rapid growing demand for highly sensitive, easy adaptive and low-cost pressure sensing solutions in the fields of health monitoring, wearable electronics and home care. Here, we report a novel flexible inductive pressure sensor array with ultrahigh sensitivity and a simple construction, for large-area contact pressure measurements. In general, the device consists of three layers: a planar spiral inductor layer and ferrite film units attached on a polyethylene terephthalate (PET) membrane, which are separated by an array of elastic pillars. Importantly, by introducing the ferrite film with an excellent magnetic permeability, the effective permeability around the inductor is greatly influenced by the separation distance between the inductor and the ferrite film. As a result, the value of the inductance changes largely as the separation distance varies as an external load applies. Our device has achieved an ultrahigh sensitivity of 1.60 kPa−1 with a resolution of 13.61 Pa in the pressure range of 0–0.18 kPa, which is comparable to the current state-of-the-art flexible pressure sensors. More remarkably, our device shows an outstanding stability when exposed to environmental interferences, e.g., electrical noises from skin surfaces (within 0.08% variations) and a constant pressure load for more than 32 h (within 0.3% variations). In addition, the device exhibits a fast response time of 111 ms and a good repeatability under cyclic pressures varying from 38.45 to 177.82 Pa. To demonstrate its practical usage, we have successfully developed a 4 × 4 inductive pressure sensor array into a wearable keyboard for a smart electronic calendar application.

In the case of a visually impaired person, literal communication often relies on braille, a system predominantly dependent on vision and touch. This study entailed the development of a visual and tactile perception technique for braille character recognition. In the visual perception approach, a braille character recognition was performed using a deep learning model (Faster R-CNN–FPN–ResNet-50), based on custom-made braille dataset collected through data augmentation and preprocessing. The attained performance was indicated by an mAP50 of 94.8 and mAP75 of 70.4 on the generated dataset. In the tactile perception approach, a braille character recognition was performed using a flexible capacitive pressure sensor array. The sensor size and density were designed according to braille standards, and a single sensor with a size of 1.5 mm × 1.5 mm was manufactured into a 5 × 5 sensor array by using a printing technique. Additionally, the sensitivity was improved by incorporating a pressure-sensitive micro dome-structured array layer. Finally, braille character recognition was visualized in the form of a video-based heatmap. These results will potentially be a cornerstone in developing assistive technology for the visually impaired through the fusion of visual-tactile sensing technology.

Background Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information. Methods In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of 17 children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe-in, toe-out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified tenfold cross-validation with recall, precision, and a time cost as metrics. Results The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% intra-subject recognition accuracy, and 85.78% and 78.81% inter-subject recognition accuracy, respectively, in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing. Conclusions In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

This study proposes a new structure for a pressure sensor module that can reduce errors caused by measurement position and direction in noninvasive radial artery pulse wave measurement, which is used for physiological monitoring. We have proposed a structure for a multi-array pressure sensor with a hexagonal arrangement and polydimethylsiloxane that easily fits to the structure of the radial artery, and evaluated the characteristics and pulse wave measurement of the developed sensor by finite element method simulation, a push–pull gauge test, and an actual pulse wave measurement experiment. The developed sensor has a measuring area of 17.6 × 17.6 mm2 and a modular structure with the analog front end embedded on the printed circuit board. The finite element method simulation shows that the developed sensor responds linearly to external pressure. According to the push–pull gauge test results for each channel, there were differences between the channels caused by the unit sensor characteristics and fabrication process. However, the correction formula can minimize the differences and ensure the linearity, and root-mean-squared error is 0.267 kPa in calibrated output. Although additional experiments and considerations on inter-individual differences are required, the results suggested that the proposed multiarray sensor could be used as a radial arterial pulse wave sensor.

Recent advances in flexible pressure sensors have fueled increasing attention as promising technologies with which to realize human epidermal pulse wave monitoring for the early diagnosis and prevention of cardiovascular diseases. However, strict requirements of a single sensor on the arterial position make it difficult to meet the practical application scenarios. Herein, based on three single-electrode sensors with small area, a 3 × 1 flexible pressure sensor array was developed to enable measurement of epidermal pulse waves at different local positions of radial artery. The designed single sensor holds an area of 6 × 6 mm2, which mainly consists of frosted microstructured Ecoflex film and thermoplastic polyurethane (TPU) nanofibers. The Ecoflex film was formed by spinning Ecoflex solution onto a sandpaper surface. Micropatterned TPU nanofibers were prepared on a fluorinated ethylene propylene (FEP) film surface using the electrospinning method. The combination of frosted microstructure and nanofibers provides an increase in the contact separation of the tribopair, which is of great benefit for improving sensor performance. Due to this structure design, the single small-area sensor was characterized by pressure sensitivity of 0.14 V/kPa, a response time of 22 ms, a wide frequency band ranging from 1 to 23 Hz, and stability up to 7000 cycles. Given this output performance, the fabricated sensor can detect subtle physiological signals (e.g., respiration, ballistocardiogram, and heartbeat) and body movement. More importantly, the sensor can be utilized in capturing human epidermal pulse waves with rich details, and the consistency of each cycle in the same measurement is as high as 0.9987. The 3 × 1 flexible sensor array is employed to acquire pulse waves at different local positions of the radial artery. In addition, the time domain parameters including pulse wave transmission time (PTT) and pulse wave velocity (PWV) can be obtained successfully, which holds promising potential in pulse-based cardiovascular system status monitoring.

This paper presents a system integration of a sense-based user interface (SUI) platform, comprised of flexible pressure and humidity sensor arrays with a commercial inertial measurement unit (IMU), to analyze behavioral patterns of young children. The pressure sensors utilize a sensor array created using flexible inkjet printing, with each sensor using a piezoresistive sensing layer. The humidity sensors employ an interdigitated capacitive sensor based on a polyimide humidity-sensitive layer and are also manufactured using the flexible inkjet printing technique. To achieve a wide measurement area, both the pressure and humidity sensors are expanded into 5 × 5 and 5 × 10 sensor arrays, respectively. Also, commercial IMU, including accelerometer/gyroscope sensors, is employed. Finally, the SUI platform is in the form of a cuboidal block model, with an IMU and circuits embedded within the block. Multilayered pressure and humidity sensor arrays are installed on the external surface of the block. Collected data from each sensor are visualized through heatmaps and 3D motion representation to create a platform that integrates fine-grained behavior as well as global behavior information of young children. This research would provide a foundation for the development of SUI technology, especially aimed at individuals who have difficulty with conventional forms of input–output devices.