Tactile Sensor Research Articles

Case report: a safe laparoscopic technique for complicated appendicitis

A case of complicated appendicitis is presented to illustrate a safe laparoscopic appendectomy technique. What makes extirpation so difficult in complicated appendicitis? Infection and tissue injury initiate the release of cytokines, which attract the omentum and cause contiguous loops of bowel to adhere, effectively isolating the inflammatory locus. Surgical dissection must reverse this process. Visualization is excellent in laparoscopy; however, operators lack tactile sensation; and when organs are fused together, touch is a valuable aid to accurate dissection. Injury to the adjacent organs (small bowel, colon, fallopian tubes, or ureter) may occur and require repair or resection (cecectomy or hemicolectomy). What is needed is an operative technique that is safe and effective in these challenging situations, especially when the appendix is adherent to adjacent structures and encased in a cocoon of (highly vascularized) fibrous tissue, a phlegmon. The technique presented is derived from open surgery. It is utilized to avoid injuring vulnerable structures in the pelvis, when performing a proctectomy for ulcerative colitis or Hirschsprung’s Disease. This goal is accomplished by maintaining a plane of dissection that abuts the rectal wall. The same technique is applied in complicated appendicitis to avoid injuring adjacent organs. This procedure is contrasted with an alternate (simpler) technique applicable to uncomplicated appendicitis.

EStatiG: Wearable Haptic Feedback with Multi-Phalanx Electrostatic Brake for Enhanced Object Perception in VR

Haptic gloves have enabled the immersive and realistic sense of interacting with objects in virtual reality (VR) by providing coordinated haptic sensation with visual information. However, previous approaches mainly focused on providing feedback on the fingertips or distal phalanges, with minimal attention paid to the other phalanges. We propose EStatiG, a haptic glove that delivers force feedback over all finger regions from the fingertip to the proximal phalanges. Here, we enrich the perception of object shape during grasping tasks in VR. To do this, we developed the double layer and multi-stacked electrostatic clutches (ES clutches) to form an electrostatic brake (ES brake) for each phalanx. With the lightweight structure (130 grams), we enabled high-resolution force feedback while maintaining wearability and usability. We validated the user perception performance when using the proposed device. Our results showed a significant improvement in the perception of phalanx angle positions and the overall experience of realism and immersion when interacting with various object shapes in VR.

AirTac: A Contactless Digital Tactile Receptor for Detecting Material and Roughness via Terahertz Sensing

Tactile sensing is an indispensable capability for humans or intelligent devices to engage in complex physical interactions. Mainstream tactile sensors are contact-based, which show limitation in measuring deformable objects and demanding high maintenance effort. As a complementary solution, a new paradigm of contactless tactile sensing is attracting much interest. While promising, they can only identify coarse-grained single tactile perception properties, either material type or surface roughness. In this paper, we propose airTac, which includes a novel contactless digital tactile receptor model, capable of simultaneously extracting these two basic tactile properties in a fine-grained resolution, by harnessing the massive bandwidth of terahertz(THz) frequency band. airTac is designed based on our key finding: while the impact of material type and surface roughness on THz signal intertwine with each other, they are actually separable, i.e., roughness manifests a certain pattern in distorting relative high-frequency components of the whole THz spectrum. Therefore, we custom-design a bio-inspired deep neural network model to decouple the intertwined THz signal, and distill the tactile perception properties embedded underlying the signal. We prototype airTac using a THz time domain spectroscopy, and perform extensive evaluation over 9 different daily material types with 39 different surface roughness. airTac achieves a material identification accuracy of 97.43% and a roughness classification accuracy of 91.46%, which demonstrates that airTac can identify common materials in daily life and exceed a fingertip spatial resolution of 1mm.

Electromechanically stable and flexible poro-hyperelastic composites for multimodal robotic tactile sensing

The expansion of automated production places increased demands on intelligent sensory networks for collecting and processing various input signals, which necessitate higher standards for multimodal detection, sensitivity, and sensing response speed. Here, this work developed a poro-hyperelastic composites-based multimodal tactile sensor capable of triboelectric, piezoresistive, and capacitive modes across multiple strains and frequency domains. Such the multilayered flexible multimodal mechano-electronic sensor (MMES) is constructed from a carbon nanotubes porous sponge (CNPS), facilitating switching between trimodal sensing modes integrated within a single sensor structure, effectively enhancing the electromechanical robustness of the sensory system. The present MMES sensor exhibits excellent pressure sensing performances, with a wide range (0–20 kPa), fast response (less than 102 ms in piezoresistive mode and less than 62 ms in capacitive mode), high sensitivity (up to 0.12 V/kPa⁻1 in triboelectric mode), and long-term durability (exceeding 10,000 cycles). Moreover, a micro-CT generated 3D model was established using finite element analysis (FEA) to investigate porous hyperelastic behaviours of the CNPS composites, in agreement with experimental data. The results demonstrated that the MMES sensor is particularly suitable for dynamic feedback in robotic arms, allowing production lines to make accurate distinguishments and operations by sensing distinct materials and resistances encountered during assembly in real time. The outcomes offer promising applications of multimodal robotic tactile sensing for human-machine interaction.

Interference haptic stimulation and consistent quantitative tactility in transparent electrotactile screen with pressure-sensitive transistors

Integrating tactile feedback through haptic interfaces enhances experiences in virtual and augmented reality. However, electrotactile systems, which stimulate mechanoreceptors directly, often yield inconsistent tactile results due to variations in pressure between the device and the finger. In this study, we present the integration of a transparent electrotactile screen with pressure-sensitive transistors, ensuring highly consistent quantitative haptic sensations. These transistors effectively calibrate tactile variations caused by touch pressure. Additionally, we explore remote-distance tactile stimulations achieved through the interference of electromagnetic waves. We validated tactile perception using somatosensory evoked potentials, monitoring the somatosensory cortex response. Our haptic screen can stimulate diverse electrotactile sensations and demonstrate various tactile patterns, including Morse code and Braille, when integrated with portable smart devices, delivering a more immersive experience. Furthermore, interference of electric fields allows haptic stimulation to facilitate diverse stimulus positioning at lower current densities, extending the reach beyond direct contact with electrodes of our screen.

Characterization of Catheter-Type Tactile Sensor Using Polyvinylidene Fluoride (PVDF) Film

To enable quantitative palpation in vivo, we previously developed a catheter-type tactile sensor with an outer diameter of 2 mm composed of a polyvinylidene fluoride (PVDF) film for minimally invasive surgery. However, our previous studies did not evaluate the effect of the PVDF film shape on the sensor output. In this study, we fabricated three types of prototype sensors with different PVDF film shapes and sizes using a simple cutting method. One of the films had the same shape as that used in one of our previous studies. We also prepared two types of PVDF film with a wide base and a narrow tip because we assumed that the deformation of the sensor gradually decreases from the root to the tip, similar to the first mode of the natural frequency. We evaluated the frequency response of the proposed sensors by vibrating the sensor tip and compared the results with the theoretical values. It was confirmed that the sensor output increased with PVDF film size. Although this tendency was observed for both the measured and theoretical values, the measured values were smaller than the theoretical values. Moreover, the effect of film size was larger than that of film shape. Improvements in the sensor structure and the theoretical equation and better evaluation methods are required in order to optimize the film shape and size.

Decoupled Temperature–Pressure Sensing System for Deep Learning Assisted Human–Machine Interaction

AbstractWith 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.

Mechanoluminescence of ZnS: Cu@Al2O3 enabled optical fiber microlens passive tactile sensor for hardness recognition.

We propose an optical fiber microlens (OFM) passive tactile sensor (OFMPTS) for hardness recognition. The sensor features a core-shell structure, with an OFM as the core and an elastic mechanoluminescence (ML) matrix shell, which is composed of ZnS: Cu@Al2O3 doped with 10 nm SiO2 particles and polydimethylsiloxane (PDMS). Utilizing the Hertz model, the ML intensity of the sensor is correlated to the elastic modulus of the sample, which enables precise hardness detection. The microlens fiber structure significantly enhances photon collection efficiency, thereby allowing for effective coupling of the ML signal. In press mode, OFMPTS differentiates between five PDMS hardness levels. It can also operate in scan or tap mode, identifying hidden foreign bodies and tissue masses through response curve analysis. The sensor, which requires no external light source, expands the capabilities of optical hardness measurement.

Highly tough, crack‐resistant and self‐healable piezo‐ionic skin enabled by dynamic hard domains with mechanosensitive ionic channel

AbstractRobust and reliable piezo‐ionic materials that are both crack resistant and self‐healable like biological skin hold great promise for applications inflexible electronics and intelligent systems with prolonged service lives. However, such a combination of high toughness, superior crack resistance, autonomous self‐healing and effective control of ion dynamics is rarely seen in artificial iontronic skin because these features are seemingly incompatible in materials design. Here, we resolve this perennial mismatch through a molecularly engineered strategy of implanting carboxyl‐functionalized groups into the dynamic hard domain structure of synthesized poly(urethane‐urea). This design provides an ultra‐high fracture energy of 211.27 kJ m−2 that is over 123.54 times that of tough human skin, while maintaining skin‐like stretchability, elasticity, and autonomous self‐healing with a 96.40% healing efficiency. Moreover, the carboxyl anion group allows the dynamic confinement of ionic fluids though electrostatic interaction, thereby ensuring a remarkable pressure sensitivity of 7.03 kPa−1 for the tactile sensors. As such, we successfully demonstrated the enormous potential ability of this skin‐like piezo‐ionic sensor for biomedical monitoring and robotic item identification, which indicates promising future uses in flexible electronics and human–machine interactions.

Microwall array tactile sensor fabricated by transferring CNT/PDMS composite material into high aspect metal mold

This paper proposes a slip sensor that assists an industrial robot to grasp an object with the minimum force required. The sensor developed in this study can detect whether an object is being pressed or sliding inside the robot hand. The developed sensor was fabricated using a custom-made metal mold, which is a block of 20 mm square and 3 mm thick with lines and spaces of 0.4 mm pitch and 2 mm depth, forming a vertical wall array (50 in total) with finely high aspect ratio of 10, i.e., the height of each wall is 2 mm and the thickness is 0.2 mm. The flexible sensor body is obtained by pouring a composite of polydimethylsiloxane (PDMS) and carbon nanotubes (CNTs) into this mold, heating it to solidify it, and then releasing it from the mold. This sensor is intended to attach to the grasping surface of a robot hand. This sensor is a resistive one, i.e., changes in the sensor's internal circuitry when the robot hand grasps an object are expressed as changes in the sensor resistance. The sensor can both estimate the pressing force and detect the object slippage, since the resistance rapidly changes when a slippage occurs. This sensor allows the robot to detect the contact state with a handling object.

A palm-like 3D tactile sensor based on liquid-metal triboelectric nanogenerator for underwater robot gripper

A palm-like 3D tactile sensor based on liquid-metal triboelectric nanogenerator for underwater robot gripper

Skin-inspired multimodal tactile sensor aiming at smart space extravehicular multi-finger operations

Tactile sensing of skin shapes the interactions between hand and the surrounding world, owing to the remarkable natural sensory system. But for astronauts, tactile feedback cannot be obtained biologically due to the very thick protective gloves, which seriously hinders the flexibility of the extravehicular activity. In this work, inspired by human skin, we develop a biomimetic multi-layer tactile sensor (BMLTS) that can work like the fast adaptive and slow adaptive receptors of fingertip to achieve the multimodal tactile perception based on the fusion of thermosensitive, piezoelectric, triboelectric and piezoresistive materials. Based on the BMLTS, the tactile perception for temperature, surface roughness discrimination and smart object grasping is successfully achieved, which fully simulates the basic tactile perceptions in typical extravehicular operations. Furthermore, by the combination of BMLTS and deep learning, a biomimetic intelligent perception system (BIPS) that can make decisions to the tactile signal just like human brain is constructed, which can provide real-time tactile information for astronauts. Intelligent real-time object material identification, writing and recording are conducted using BIPS, reaching the recognition accuracy rate higher than 95%. This work lays the foundation for the systematization and integration of tactile sensors, and paves the way for the development of dexterous tactile perception for smart extravehicular activities of astronauts.

Guide Dog AR: A Tactile and Auditory Assisting Device Design with the Motif of a Guide Dog for the Visually Impaired

This study introduces “Guide Dog AR,” an AR device inspired by the handle of a guide dog’s harness. The device and its associated content aim to provide visually impaired individuals with the experience of walking with a guide dog, serving as both a practical tool and a foundation for entertainment. The device offers an augmented walking experience through tactile and auditory sensations. The device was assessed with the factors "effectiveness" and "immersiveness." The evaluation for effectiveness involved creating a simulated virtual path. The level of immersion was evaluated based on how deeply users were engrossed in the augmented content provided by the device. The success rate was determined by how well users navigated this virtual path using the device’s feedback. In assessing immersion, in-depth interviews were conducted with visually impaired participants who experienced the virtual path. These interviews compared their experiences to actual walks with guide dogs, discussing similarities, differences, and overall immersion.

A Novel Soft and Inflatable Strain-based Tactile Sensing Balloon for Enhanced Diagnosis of Colorectal Cancer Polyps Via Colonoscopy.

In this paper, with the goal of addressing the lack of tactile feedback in colorectal cancer (CRC) polyps diagnosis using a colonoscopy procedure, we propose the design and fabrication of a novel soft and inflatable strain-based tactile sensing balloon (SI-STSB). The proposed soft sensor features a unique stretchable sensing layer - that utilizes a liquid metal injected within spiral-shape microchannels of a stretchable substrate - and is integrated with a unique inflatable balloon mechanism. The proposed SI-STSB has been thoroughly characterized through different calibration experiments. Results demonstrate a phenomenal adjustable sensitivity with low hysteresis behavior under different experimental conditions for this sensor making it a great candidate for enhancing the existing diagnosis procedures.

“Spicy Touch”: Cross-modal associations between hand-feel touch and capsaicin-induced oral irritation

The influence of extrinsic hand-feel touch cues on consumer experiences in food and beverage consumption is well established. However, their impact on trigeminal perception, particularly the oral irritation caused by capsaicin or spicy foods, is less understood. This study aimed to determine the existence of cross-modal associations between hand-feel touch and capsaicin-induced oral irritation. This study investigated whether these potential associations were driven by the sensory contributions of the hand-feel tactile materials (measured by instrumental physical parameters) or by affective responses (evaluated through hedonic scales and the self-reported emotion questionnaire, EsSense Profile®, by consumers). In our study, 96 participants tasted a capsaicin solution while engaging with nine hand-feel tactile materials, i.e., cardboard, linen, rattan, silicone, stainless steel, sandpaper (fine), sandpaper (rough), sponge, and towel. They subsequently rated their liking and emotional responses, perceived intensity of oral irritation, and the congruency between hand-feel tactile sensation and oral irritation. Instrumental measurements characterized the surface texture of the hand-feel tactile materials, which were correlated with the collected sensory data. The results revealed that unique cross-modal associations between hand-feel touch and capsaicin-induced oral irritation. Specifically, while sandpapers demonstrated high congruence with the sensation of oral irritation, stainless steel was found to be least congruent. These associations were influenced by both the common emotional responses (“active,” “aggressive,” “daring,” “energetic,” “guilty,” and “worried”) evoked by the hand-feel tactile materials and the capsaicin, as well as by participants’ liking for the hand-feel tactile materials and the characteristics of the surface textures. This study provides empirical evidence of the cross-modality between hand-feel tactile sensations and capsaicin-induced oral irritation, opening new avenues for future research in this area.

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.

Multimodal tactile sensing fused with vision for dexterous robotic housekeeping

As robots are increasingly participating in our daily lives, the quests to mimic human abilities have driven the advancements of robotic multimodal senses. However, current perceptual technologies still unsatisfied robotic needs for home tasks/environments, particularly facing great challenges in multisensory integration and fusion, rapid response capability, and highly sensitive perception. Here, we report a flexible tactile sensor utilizing thin-film thermistors to implement multimodal perceptions of pressure, temperature, matter thermal property, texture, and slippage. Notably, the tactile sensor is endowed with an ultrasensitive (0.05 mm/s) and ultrafast (4 ms) slip sensing that is indispensable for dexterous and reliable grasping control to avoid crushing fragile objects or dropping slippery objects. We further propose and develop a robotic tactile-visual fusion architecture that seamlessly encompasses multimodal sensations from the bottom level to robotic decision-making at the top level. A series of intelligent grasping strategies with rapid slip feedback control and a tactile-visual fusion recognition strategy ensure dexterous robotic grasping and accurate recognition of daily objects, handling various challenging tasks, for instance grabbing a paper cup containing liquid. Furthermore, we showcase a robotic desktop-cleaning task, the robot autonomously accomplishes multi-item sorting and cleaning desktop, demonstrating its promising potential for smart housekeeping.

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.

Zero-Biased Bionic Fingertip E-Skin with Multimodal Tactile Perception and Artificial Intelligence for Augmented Touch Awareness.

Electronic skins (E-Skins) are crucial for future robotics and wearable devices to interact with and perceive the real world. Prior research faces challenges in achieving comprehensive tactile perception and versatile functionality while keeping system simplicity for lack of multimodal sensing capability in a single sensor. Two kinds of tactile sensors, transient voltage artificial neuron (TVAN) and sustained potential artificial neuron (SPAN), featuring self-generated zero-biased signals are developed to realize synergistic sensing of multimodal information (vibration, material, texture, pressure, and temperature) in a single device instead of complex sensor arrays. Simultaneously, machine learning with feature fusion is applied to fully decode their output information and compensate for the inevitable instability of applied force, speed, etc, in real applications. Integrating TVAN and SPAN, the formed E-Skin achieves holistic touch awareness in only a single unit. It can thoroughly perceive an object through a simple touch without strictly controlled testing conditions, realize the capability to discern surface roughness from 0.8 to 1600µm, hardness from 6HA to 85HD, and correctly distinguish 16 objects with temperature variance from 0 to 80°C. The E-skin also features a simple and scalable fabrication process, which can be integrated into various devices for broad applications.

Human tactile sensing and sensorimotor mechanism: from afferent tactile signals to efferent motor control

In tactile sensing, decoding the journey from afferent tactile signals to efferent motor commands is a significant challenge primarily due to the difficulty in capturing population-level afferent nerve signals during active touch. This study integrates a finite element hand model with a neural dynamic model by using microneurography data to predict neural responses based on contact biomechanics and membrane transduction dynamics. This research focuses specifically on tactile sensation and its direct translation into motor actions. Evaluations of muscle synergy during in -vivo experiments revealed transduction functions linking tactile signals and muscle activation. These functions suggest similar sensorimotor strategies for grasping influenced by object size and weight. The decoded transduction mechanism was validated by restoring human-like sensorimotor performance on a tendon-driven biomimetic hand. This research advances our understanding of translating tactile sensation into motor actions, offering valuable insights into prosthetic design, robotics, and the development of next-generation prosthetics with neuromorphic tactile feedback.