Nanocellulose and multi-walled carbon nanotubes reinforced polyacrylamide/sodium alginate conductive hydrogel as flexible sensor.

Conductive hydrogels have been widely applied in human-computer interaction, tactile sensing, and sustainable green energy harvesting. Herein, a double cross-linked network composite hydrogel (MWCNTs/CNWs/PAM/SA) by constructing dual enhancers acting together with PAM/SA was constructed. By systematically optimizing the compositions, the hydrogel displayed features advantages of good mechanical adaptability, high conductivity sensitivity (GF = 5.65, 53 ms), low hysteresis (<11 %), and shape memory of water molecules and temperature. The nanocellulose crystals (CNWs) were bent and entangled with the backbone of the polyacrylamide/ sodium alginate (PAM/SA) hydrogel network, which effectively transferred the external mechanical forces to the entire physical and chemical cross-linking domains. Multi-walled carbon nanotubes (MWCNTs) were filled into the cross-linking network of the hydrogel to enhance the conductivity of the hydrogel effectively. Notably, hydrogels are designed as flexible tactile sensors that can accurately recognize and monitor electrical signals from different gesture movements and temperature changes. It was also assembled as a friction nanogenerator (TENG) that continuously generates a stable open circuit voltage (28 V) for self-powered small electronic devices. This research provides a new prospect for designing nanocellulose and MWCNTs reinforced conductive hydrogels via a facile method.

Recent Progress on Flexible Self-Powered Tactile Sensing Platforms for Health Monitoring and Robotics.

Over the past decades, tactile sensing technology has made significant advances in the fields of health monitoring and robotics. Compared to conventional sensors, self-powered tactile sensors do not require an external power source to drive, which makes the entire system more flexible and lightweight. Therefore, they are excellent candidates for mimicking the tactile perception functions for wearable health monitoring and ideal electronic skin (e-skin) for intelligent robots. Herein, the working principles, materials, and device fabrication strategies of various self-powered tactile sensing platforms are introduced first. Then their applications in health monitoring and robotics are presented. Finally, the future prospects of self-powered tactile sensing systems are discussed.

Detecting Transitions from Stability to Instability in Robotic Grasping Based on Tactile Perception.

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.

Morse Code Recognition Based on a Flexible Tactile Sensor with Carbon Nanotube/Polyurethane Sponge Material by the Long Short-Term Memory Model.

Morse code recognition plays a very important role in the application of human-machine interaction. In this paper, based on the carbon nanotube (CNT) and polyurethane sponge (PUS) composite material, a flexible tactile CNT/PUS sensor with great piezoresistive characteristic is developed for detecting Morse code precisely. Thirty-six types of Morse code, including 26 letters (A-Z) and 10 numbers (0-9), are applied to the sensor. Each Morse code was repeated 60 times, and 2160 (36 × 60) groups of voltage time-sequential signals were collected to construct the dataset. Then, smoothing and normalization methods are used to preprocess and optimize the raw data. Based on that, the long short-term memory (LSTM) model with excellent feature extraction and self-adaptive ability is constructed to precisely recognize different types of Morse code detected by the sensor. The recognition accuracies of the 10-number Morse code, the 26-letter Morse code, and the whole 36-type Morse code are 99.17%, 95.37%, and 93.98%, respectively. Meanwhile, the Gated Recurrent Unit (GRU), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) models are built to distinguish the 36-type Morse code (letters of A-Z and numbers of 0-9) based on the same dataset and achieve the accuracies of 91.37%, 88.88%, 87.04%, and 90.97%, respectively, which are all lower than the accuracy of 93.98% based on the LSTM model. All the experimental results show that the CNT/PUS sensor can detect the Morse code's tactile feature precisely, and the LSTM model has a very efficient property in recognizing Morse code detected by the CNT/PUS sensor.

In-Sensor Tactile Fusion and Logic for Accurate Intention Recognition.

Touch control intention recognition is an important direction for the future development of human-machine interactions (HMIs). However, the implementation of parallel-sensing functional modules generally requires a combination of different logical blocks and control circuits, which results in regional redundancy, redundant data, and low efficiency. Here, a location-and-pressure intelligent tactile sensor (LPI tactile sensor) unprecedentedly combined with sensing, computing, and logic is proposed, enabling efficient and ultrahigh-resolution action-intention interaction. The LPI tactile sensor eliminates the need for data transfer among the functional units through the core integration design of the layered structure. It actuates in-sensor perception through feature transmission, fusion, and differentiation, thereby revolutionizing the traditional von Neumann architecture. While greatly simplifying the data dimensionality, the LPI tactile sensor achieves outstanding resolution sensing in both location (<400 µm) and pressure (75 Pa). Synchronous feature fusion and decoding support the high-fidelity recognition of action and combinatorial logic intentions. Benefiting from location and pressure synergy, the LPI tactile sensor demonstrates robust privacy as an encrypted password device and interaction intelligence through pressure enhancement. It can recognize continuous touch actions in real time, map real intentions to target events, and promote accurate and efficient intention-driven HMIs.

Healing Function for Abraded Fingerprint Ridges in Tactile Texture Sensors.

Tactile texture sensors are designed to evaluate the sensations felt when a human touches an object. Prior studies have demonstrated the necessity for these sensors to have compliant ridges on their surfaces that mimic human fingerprints. These features enable the simulation of contact phenomena, especially friction and vibration, between human fingertips and objects, enhancing the tactile sensation evaluation. However, the ridges on tactile sensors are susceptible to abrasion damage from repeated use. To date, the healing function of abraded ridges has not been proposed, and its effectiveness needs to be demonstrated. In this study, we investigated whether the signal detection capabilities of a sensor with abraded epidermal ridges could be restored by healing the ridges using polyvinyl chloride plastisol as the sensor material. We developed a prototype tactile sensor with an embedded strain gauge, which was used to repeatedly scan roughness specimens. After more than 1000 measurements, we observed significant deterioration in the sensor's output signal level. The ridges were then reshaped using a mold with a heating function, allowing the sensor to partially regain its original signal levels. This method shows potential for extending the operational lifespan of tactile texture sensors with compliant ridges.

Design and Evaluation of a Rapid Monolithic Manufacturing Technique for a Novel Vision-Based Tactile Sensor: C-Sight.

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.

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

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

A Prospective Study on a Suture Force Feedback Device for Training and Evaluating Junior Surgeons in Anastomotic Surgical Closure.

BACKGROUND: Surgical reconstruction is a crucial stage in various surgeries, including pancreaticoduodenectomy, as it can significantly affect the surgical results. The objective was to design a suture force feedback (SFF) device that can precisely measure the suture force during surgical closures. Afterward, the device was used to train junior surgeons in surgical closure techniques. METHODS: The SFF was used to capture the suture force data of experienced surgeons. This data was utilized to train and assess junior surgeons. The SFF device had 2 tactile-based force sensors that measured the applied force. Whenever the applied force was not within the optimal force range, the device provided feedback to the surgeon. A workshop was conducted to train junior surgeons in surgical closure techniques to improve their suturing skills. RESULTS: Thirty-seven junior surgeons were enrolled in this training, of whom only 24 completed the 30-day training program. The pre-assessment results revealed that the force exerted by junior surgeons during suture knot-tying was uneven compared with that of the experienced surgeons, with a significant difference in the force exerted per knot throw (P = 0.005. Before the training program, junior surgeons applied a force of 3.89 ± 0.43 N, which was more than twice the force applied by experienced surgeons (1.75 ± 0.12 N). However, after completing the 30-day training program, their force improved to 2.35 ± 0.13 N. CONCLUSIONS: The SFF device was shown to be an encouraging training tool for improving the surgical closure dexterity and technique of the participating junior surgeons.

A Skin-Inspired High-Performance Tactile Sensor for Accurate Recognition of Object Softness.

High-performance tactile sensors with skin-sensing properties are crucial for intelligent perception in next-generation smart devices. However, previous studies have mainly focused on the sensitivity and response range of tactile sensation while neglecting the ability to recognize object softness. Therefore, achieving a precise perception of the softness remains a challenge. Here, we report an integrated tactile sensor consisting of a central hole gradient structure pressure sensor and a planar structure strain sensor. The recognition of softness and tactile perception is achieved through the synergistic effect of pressure sensors that sense the applied pressure and strain sensors that recognize the strain of the target object. The results indicate that the softness evaluation parameter (SC) of the integrated structural tactile sensor increases from 0.14 to 0.47 along with Young's modulus of the object decreasing from 2.74 to 0.45 MPa, demonstrating accurate softness recognition. It also exhibits a high sensitivity of 10.55 kPa-1 and an ultrawide linear range of 0-1000 kPa, showing an excellent tactile sensing capability. Further, an intelligent robotic hand system based on integrated structural tactile sensors was developed, which can identify the softness of soft foam and glass and grasp them accurately, indicating human skin-like sensing and grasping capabilities.

High Sensitivity Triboelectric Based Flexible Self-Powered Tactile Sensor with Bionic Fingerprint Ring Structure.

Flexible self-powered tactile sensors, with applications spanning wearable electronics, human-machine interaction, prosthetics, and soft robotics, offer real-time feedback on tactile interactions in diverse environments. Despite advances in their structural development, challenges persist in sensitivity and robustness, particularly when additional functionalities, such as high transparency and stretchability. In this study, we present a novel approach integrating a bionic fingerprint ring structure with a PVDF-HFP/AgNWs composite fiber electrode membrane, fabricated via 3D printing technology and electrospinning, respectively, yielding a triboelectric nanogenerator (TENG)-based self-powered tactile sensor. The sensor demonstrates high sensitivity (5.84 V/kPa in the 0-10 kPa range) and rapid response time (10 ms), attributed to the microring texture on its surface, and exhibits exceptional robustness, maintaining electrical output integrity even after 24,000 cycles of loading. These findings highlight the potential of the microring structures in addressing critical challenges in flexible sensor technology.

Synthesis of PDMS Chain Structure with Introduced Dynamic Covalent Bonding for High-Performance Rehealable Tactile Sensor Application.

In addressing the increasing demand for wearable sensing systems, the performance and lifespan of such devices must be improved by enhancing their sensitivity and healing capabilities. The present work introduces an innovative method for synthesizing a healable disulfide bond contained in a polydimethylsiloxane network (PDMS-SS) that incorporates ionic salts, which is designed to serve as a highly effective dielectric layer for capacitive tactile sensors. Within the polymer network structure, the cross-linking agent pentaerythritol tetrakis 3-mercaptopropionate (PTKPM) forms reversible disulfide bonds while simultaneously increasing polymer softness and the dielectric constant. The incorporation of dioctyl sulfosuccinate sodium salt (DOSS) significantly improves the capacitance and sensing properties by forming an electrical double-layer through interactions between the electrode charge and salt ions at the contact interface. The developed polymer material-based tactile sensor shows a strong response signal at low pressure (0.1 kPa) and maintains high sensitivity (0.175 kPa-1) over a wide pressure range (0.1-10 kPa). It also maintains the same sensitivity over 10 000 repeated applications of external pressure and is easily self-healed against mechanical deformation due to the dynamic disulfide covalent bonding, restoring ≈95% of its detection capacity.

High-Performance Four-Channel Tactile Sensor for Measuring the Magnitude and Orientation of Forces.

Flexible sensors have gained popularity in recent years. This study proposes a novel structure of a resistive four-channel tactile sensor capable of distinguishing the magnitude and direction of normal forces acting on its sensing surface. The sensor uses EcoflexTM00-30 as the substrate and EGaIn alloy as the conductive filler, featuring four mutually perpendicular and curved channels to enhance the sensor's dynamic responsiveness. Experiments and simulations show that the sensor has a large dynamic range (31.25-100 mΩ), high precision (deviation of repeated pressing below 0.1%), linearity (R2 above 0.97), fast response/recovery time (0.2 s/0.15 s), and robust stability (with fluctuations below 0.9%). This work uses an underactuated robotic hand equipped with a four-channel tactile sensor to grasp various objects. The sensor data collected effectively predicts the shapes of the objects grasped. Furthermore, the four-channel tactile sensor proposed in this work may be employed in smart wearables, medical diagnostics, and other industries.

Sensors and Sensing Devices Utilizing Electrorheological Fluids and Magnetorheological Materials-A Review.

This paper comprehensively reviews sensors and sensing devices developed or/and proposed so far utilizing two smart materials: electrorheological fluids (ERFs) and magnetorheological materials (MRMs) whose rheological characteristics such as stiffness and damping can be controlled by external stimuli; an electrical voltage for ERFs and a magnetic field for MRMs, respectively. In this review article, the MRMs are classified into magnetorheological fluids (MRF), magnetorheological elastomers (MRE) and magnetorheological plastomers (MRP). To easily understand the history of sensing research using these two smart materials, the order of this review article is organized in a chronological manner of ERF sensors, MRF sensors, MRE sensors and MRP sensors. Among many sensors fabricated from each smart material, one or two sensors or sensing devices are adopted to discuss the sensing configuration, working principle and specifications such as accuracy and sensitivity. Some sensors adopted in this article include force sensors, tactile devices, strain sensors, wearable bending sensors, magnetometers, display devices and flux measurement sensors. After briefly describing what has been reviewed in a conclusion, several challenging future works, which should be undertaken for the practical applications of sensors or/and sensing devices, are discussed in terms of response time and new technologies integrating with artificial intelligence neural networks in which several parameters affecting the sensor signals can be precisely and optimally tuned. It is sure that this review article is very helpful to potential readers who are interested in creative sensors using not only the proposed smart materials but also different types of smart materials such as shape memory alloys and active polymers.

Underwater Gesture Recognition Meta-Gloves for Marine Immersive Communication.

Rapid advancements in immersive communications and artificial intelligence have created a pressing demand for high-performance tactile sensing gloves capable of delivering high sensitivity and a wide sensing range. Unfortunately, existing tactile sensing gloves fall short in terms of user comfort and are ill-suited for underwater applications. To address these limitations, we propose a flexible hand gesture recognition glove (GRG) that contains high-performance micropillar tactile sensors (MPTSs) inspired by the flexible tube foot of a starfish. The as-prepared flexible sensors offer a wide working range (5 Pa to 450 kPa), superfast response time (23 ms), reliable repeatability (∼10000 cycles), and a low limit of detection. Furthermore, these MPTSs are waterproof, which makes them well-suited for underwater applications. By integrating the high-performance MPTSs with a machine learning algorithm, the proposed GRG system achieves intelligent recognition of 16 hand gestures under water, which significantly extends real-time and effective communication capabilities for divers. The GRG system holds tremendous potential for a wide range of applications in the field of underwater communications.

Performance-Enhanced Flexible Self-Powered Tactile Sensor Arrays Based on Lotus Root-Derived Porous Carbon for Real-Time Human-Machine Interaction of the Robotic Snake.

Flexible tactile sensors play an important role in the development of wearable electronics and human-machine interaction (HMI) systems. However, poor sensing abilities, an indispensable external energy supply, and limited material selection have significantly constrained their advancement. Herein, a self-powered flexible triboelectric sensor (TES) is proposed by integrating lotus-root-derived porous carbon (PC) into polydimethylsiloxane (PDMS). Owing to the superior charge capturing capability of PC, the PDMS/PC (PPC)-based TES exhibits an open-circuit voltage (Voc) of 22.8 V when it is periodically patted by skin at the pressure of 2 N and the frequency of 1 Hz, which is 5 times higher than that of a pristine PDMS-based TES. Furthermore, the as-prepared self-powered TES exhibits a high sensitivity of 3.24 V kPa-1 below 15 kPa for detecting human motion signals, such as finger clicks, joint bends, etc. Last but not the least, after the assembly of a PPC-based TES array and construction of an HMI system, the robotic snake can be controlled remotely by recognizing finger touching signals. This work shows broad potential applications for the self-powered TES in the fields of intelligent robotics, flexible electronics, disaster relief, and intelligence spying.

Magnetic Tactile Sensor with Bionic Hair Array for Sliding Sensing and Object Recognition.

Due to the high application value in intelligent robots, tactile sensors with large sensing area and multi-dimensional sensing ability have attracted the attention of researchers in recent years. Inspired by bionics of hairs on human skin, a flexible tactile sensor based on magnetic cilia array is developed, showing extremely high sensitivity and stability. The upper layers of the sensor are multiple magnetic cilia containing magnetic particles, while the lower layer is a serpentine flexible circuit board with a magnetic sensor array. When magnetic cilia are bent under force, the magnetic sensor array can detect changes in the magnetic field, thereby the magnitude and direction of external force can be obtained. The proposed sensor has a resolution of 0.2 mN with a working range of 0-19.5 mN and can distinguish the direction of external force. The large sensing area and short response time make this sensor suitable for sliding tactile detection, and experiments show that the sensor can be also applied in object recognition with a success accuracy of 97%. In addition to the shape of objects, the sensor can identify whether there is magnetism inside objects, making it of significant value in intelligent robots and modern medicine.

Skin-Inspired Capacitive Flexible Tactile Sensor with an Asymmetric Structure for Detecting Directional Shear Forces.

Flexible pressure sensors based on micro-/nanostructures can be integrated into robots to achieve sensitive tactile perception. However, conventional symmetric structures, such as pyramids or hemispheres, can sense only the magnitude of a force and not its direction. In this study, a capacitive flexible tactile sensor inspired by skin structures and based on an asymmetric microhair structure array to perceive directional shear force is designed. Asymmetric microhair structures are obtained by two-photon polymerization (TPP) and replication. Owing to the features of asymmetric microhair structures, different shear force directions result in different deformations. The designed device can determine the directions of both static and dynamic shear forces. Additionally, it exhibits large response scales ranging from 30 Pa to 300 kPa and maintains high stability even after 5000 cycles; the final relative capacitive change (ΔC/C0 ) is <2.5%. This flexible tactile sensor has the potential to improve the perception and manipulation ability of dexterous hands and enhance the intelligence of robots.

Crosstalk-Free Position Mapping for One-Step Reconstruction of Surface Topological Information via Eigenfrequency-Registered Wearable Interface.

Exploring flexible tactile sensors capable of recognizing surface information is significant for the development of virtual reality, artificial intelligence, soft robotics, and human-machine interactions (HMI). However, it is still a challenge for current tactile sensors to efficiently recognize the surface pattern information while maintaining the simplicity of the overall system. In this study, cantilever beam-like magnetized micropillars (MMPs) with height gradients are assembled as a position-registered array for rapid recognition of surface pattern information. After crossing the surface location with convex patterns, the deformed MMPs undergo an intrinsic oscillating process to induce damped electrical signals, which can then be converted to a frequency domain for eigenfrequency extraction. Via precisely defining the specific eigenfrequencies of different MMPs, position mapping is realized in crosstalk-free behavior even though all signals are processed by one communication channel and a pair of electrodes. With a customized LabVIEW program, the surface information (e.g., letters, numbers, and Braille) can be accurately reconstructed by the frequency sequence produced in a single scanning procedure. We expect that the proposed interface can be a convenient and powerful platform for intelligent surface information perception and an HMI system in the future.

A Multifunctional Flexible Tactile Sensor Based on Resistive Effect for Simultaneous Sensing of Pressure and Temperature.

Flexible tactile sensors with multifunctional sensing functions have attracted much attention due to their wide applications in artificial limbs, intelligent robots, human-machine interfaces, and health monitoring devices. Here, a multifunctional flexible tactile sensor based on resistive effect for simultaneous sensing of pressure and temperature is reported. The sensor features a simple design with patterned metal film on a soft substrate with cavities and protrusions. The decoupling of pressure and temperature sensing is achieved by the reasonable arrangement of metal layers in the patterned metal film. Systematically experimental and numerical studies are carried out to reveal the multifunctional sensing mechanism and show that the proposed sensor exhibits good linearity, fast response, high stability, good mechanical flexibility, and good microfabrication compatibility. Demonstrations of the multifunctional flexible tactile sensor to monitor touch, breathing, pulse and objects grabbing/releasing in various application scenarios involving coupled temperature/pressure stimuli illustrate its excellent capability of measuring pressure and temperature simultaneously. These results offer an effective tool for multifunctional sensing of pressure and temperature and create engineering opportunities for applications of wearable health monitoring and human-machine interfaces.