All-Printed Finger-Inspired Tactile Sensor Array for Microscale Texture Detection and 3D Reconstruction

Electronic skins are expected to replicate a human-like tactile sense, which significantly detects surface information, including geometry, material, and temperature. Although most texture features can be sensed in the horizontal direction, the lack of effective approaches for detecting vertical properties limits the development of artificial skin based on tactile sensors. In this study, an all-printed finger-inspired tactile sensor array is developed to realize the 3D detection and reconstruction of microscale structures. A beam structure with a suspended multilayer membrane is proposed, and a tactile sensor array of 12 units arranged in a dual-column layout is developed. This architecture enables the tactile sensor array to obtain comprehensive geometric information of micro-textures, including 3D morphology and clearance characteristics, and optimizes the 3D reconstruction patterns by self-calibration. Moreover, an innovative screen-printing technology incorporating multilayer printing and sacrificial-layer techniques is adopted to print the entire device. In additon, a Braille recognition system utilizing this tactile sensor array is developed to interpret Shakespeare's quotes printed in Grade 2 Braille. The abovementioned demonstrations reveal an attractive future vision for endowing bioinspired robots with the unique capability of touching and feeling the microscale real world and reconstructing it in the cyber world.

Recent Advances in Tactile Sensory Systems: Mechanisms, Fabrication, and Applications

Flexible electronics is a cutting-edge field that has paved the way for artificial tactile systems that mimic biological functions of sensing mechanical stimuli. These systems have an immense potential to enhance human-machine interactions (HMIs). However, tactile sensing still faces formidable challenges in delivering precise and nuanced feedback, such as achieving a high sensitivity to emulate human touch, coping with environmental variability, and devising algorithms that can effectively interpret tactile data for meaningful interactions in diverse contexts. In this review, we summarize the recent advances of tactile sensory systems, such as piezoresistive, capacitive, piezoelectric, and triboelectric tactile sensors. We also review the state-of-the-art fabrication techniques for artificial tactile sensors. Next, we focus on the potential applications of HMIs, such as intelligent robotics, wearable devices, prosthetics, and medical healthcare. Finally, we conclude with the challenges and future development trends of tactile sensors.

Split-Type Magnetic Soft Tactile Sensor with 3D Force Decoupling

Tactile sensory organs for sensing 3D force, such as human skin and fish lateral lines, are indispensable for organisms. With their sensory properties enhanced by layered structures, typical sensory organs can achieve excellent perception as well as protection under frequent mechanical contact. Here, inspired by these layered structures, a split-type magnetic soft tactile sensor with wireless 3D force sensing and a high accuracy (1.33%) fabricated by developing a centripetal magnetization arrangement and theoretical decoupling model is introduced. The 3D force decoupling capability enables it to achieve a perception close to that of human skin in multiple dimensions without complex calibration. Benefiting from the 3D force decoupling capability and split design with a long effective distance (>20 mm), several sensors are assembled in air and water to achieve delicate robotic operation and water flow-based navigation with an offset <1.03%, illustrating the extensive potential of magnetic tactile sensors in flexible electronics, human-machine interactions, and bionic robots.

From CySkin to ProxySKIN: Design, Implementation and Testing of a Multi-Modal Robotic Skin for Human-Robot Interaction

The Industry 5.0 paradigm has a human-centered vision of the industrial scenario and foresees a close collaboration between humans and robots. Industrial manufacturing environments must be easily adaptable to different task requirements, possibly taking into account the ergonomics and production line flexibility. Therefore, external sensing infrastructures such as cameras and motion capture systems may not be sufficient or suitable as they limit the shop floor reconfigurability and increase setup costs. In this paper, we present the technological advancements leading to the realization of ProxySKIN, a skin-like sensory system based on networks of distributed proximity sensors and tactile sensors. This technology is designed to cover large areas of the robot body and to provide a comprehensive perception of the surrounding space. ProxySKIN architecture is built on top of CySkin, a flexible artificial skin conceived to provide robots with the sense of touch, and arrays of Time-of-Flight (ToF) sensors. We provide a characterization of the arrays of proximity sensors and we motivate the design choices that lead to ProxySKIN, analyzing the effects of light interference on a ToF, due to the activity of other sensing devices. The obtained results show that a large number of proximity sensors can be embedded in our distributed sensing architecture and incorporated onto the body of a robotic platform, opening new scenarios for complex applications.

Increasing the sensor channels: a solution for the pressing offsets that cause the physiological parameter inaccuracy in radial artery pulse signal acquisition

Introduction: In studies of pulse wave analysis, single-channel sensors only adopt single temporal pulse signals without spatial information to show pulse-feeling patterns. Multi-channel arterial pulse signals, also named as three-dimensional pulse images (3DPIs), provide the spatial and temporal characteristics of radial pulse signals. When involving single or few-channel sensors, pressing offsets have substantial impacts on obtaining inaccurate physiological parameters like tidal peak (P2). Methods: This study discovers the pressing offsets in multi-channel pulse signals and analyzes the relationship between the pressing offsets and time of P2 (T2) by qualifying the pressing offsets. First, we employ a data acquisition system to capture 3DPIs. Subsequently, the errorT2 is developed to qualify the pressing offsets. Results: The outcomes display a central low and peripheral high pattern. Additionally, the errorT2 increase as the distances from the artery increase, particularly at the radial ends of the blood flow direction. For every 1 mm increase in distances between sensing elements and center sensing elements, the errorT2 in the radial direction escalates by 4.87%. When the distance is greater than 3.42 mm, the errorT2 experiences a sudden increase. Discussion: The results show that increasing the sensor channels can overcome the pressing offsets in radial pulse signal acquisition.

High-Performance Dual-Responsive Sensing Skin Enabled by Bioinspired Transduction of Coplanar Square-Loop Electrodes

Advancements in intelligent robots and human-machine interaction necessitate a shift in artificial skins toward multimodal perception. Dual-responsive skins that can detect proximity and pressure information are significant to establishing continuous sensing of interaction processes and extending interactive application scenarios. To address the current limitations of inadequate dual-mode performance, such as limited proximal response change and low tactile sensitivity, this paper presents a bioinspired complementary gradient architecture-enabled (CGA) transduction design and a high-performance dual-responsive skin based on coplanar square-loop electrodes. Through systematic investigation into the transduction of various electrode configurations, comparative results reveal the remarkable potential of coplanar electrodes to deliver superior dual-mode performance without compromise. Simulations and experiments prove that the proposed CGA response mechanism can capture local interface deformation and overall compression signals, further enhancing response sensitivity. The final developed artificial skin is sensitive to external pressure and the approach of objects simultaneously, exhibiting a long detection distance (∼40 mm), a high proximity response (>0.4), and outstanding touch sensitivity (0.131 kPa-1). Furthermore, we demonstrate proof-of-concept applications for the proposed sensing skin in a dual-mode teleoperation interface and adaptive grasping interactions.

Soft Biomimetic Fiber-Optic Tactile Sensors Capable of Discriminating Temperature and Pressure

Tactile sensors with high softness and multisensory functions are highly desirable for applications in humanoid robotics, smart prosthetics, and human-machine interfaces. Here, we report a soft biomimetic fiber-optic tactile (SBFT) sensor that offers skin-like tactile sensing abilities to perceive and discriminate temperature and pressure. The SBFT sensor is fabricated by encapsulating a macrobent fiber Bragg grating (FBG) in an elastomeric droplet-shaped structure that results in two optical resonances associated with the FBG and excited whispering gallery modes (WGMs) propagating along the bent region. Benefiting from the different thermo-optic and stress-optic effects of FBG and WGM resonances, the pressure and temperature can be fully decoupled with a high precision of 0.2 °C and 0.8 mN, respectively. To achieve a compact system for signal demodulation, a single-cavity dual-comb fiber laser is developed to interrogate the SBFT sensor based on dual-comb spectroscopy, which enables fast spectral sampling with a single photodiode. We show that the SBFT sensor is capable of perceiving pressure, temperature, and hardness in touching soft tissues and human skins, demonstrating great promise for soft tissue palpation and human-like robotic perception.

Templated Laser-Induced-Graphene-Based Tactile Sensors Enable Wearable Health Monitoring and Texture Recognition via Deep Neural Network

Flexible tactile sensors show great potential for portable healthcare and environmental monitoring applications. However, challenges persist in scaling up the manufacturing of stable tactile sensors with real-time feedback. This work demonstrates a robust approach to fabricating templated laser-induced graphene (TLIG)-based tactile sensors via laser scribing, elastomer hot-pressing transfer, and 3D printing of the Ag electrode. With different mesh sandpapers as templates, TLIG sensors with adjustable sensing properties were achieved. The tactile sensor obtains excellent sensitivity (52260.2 kPa-1 at a range of 0-7 kPa), a broad detection range (up to 1000 kPa), a low limit of detection (65 Pa), a rapid response (response/recovery time of 12/46 ms), and excellent working stability (10000 cycles). Benefiting from TLIG's high performance and waterproofness, TLIG sensors can be used as health monitors and even in underwater scenarios. TLIG sensors can also be integrated into arrays acting as receptors of the soft robotic gripper. Furthermore, a deep neural network based on the convolutional neural network was employed for texture recognition via a soft TLIG tactile sensing array, achieving an overall classification rate of 94.51% on objects with varying surface roughness, thus offering high accuracy in real-time practical scenarios.

Visualizing tactile feedback: an overview of current technologies with a focus on ultrasound elastography

Tissue elasticity remains an essential biomarker of health and is indicative of irregularities such as tumors or infection. The timely detection of such abnormalities is crucial for the prevention of disease progression and complications that arise from late-stage illnesses. However, at both the bedside and the operating table, there is a distinct lack of tactile feedback for deep-seated tissue. As surgical techniques advance toward remote or minimally invasive options to reduce infection risk and hasten healing time, surgeons lose the ability to manually palpate tissue. Furthermore, palpation of deep structures results in decreased accuracy, with the additional barrier of needing years of experience for adequate confidence of diagnoses. This review delves into the current modalities used to fulfill the clinical need of quantifying physical touch. It covers research efforts involving tactile sensing for remote or minimally invasive surgeries, as well as the potential of ultrasound elastography to further this field with non-invasive real-time imaging of the organ's biomechanical properties. Elastography monitors tissue response to acoustic or mechanical energy and reconstructs an image representative of the elastic profile in the region of interest. This intuitive visualization of tissue elasticity surpasses the tactile information provided by sensors currently used to augment or supplement manual palpation. Focusing on common ultrasound elastography modalities, we evaluate various sensing mechanisms used for measuring tactile information and describe their emerging use in clinical settings where palpation is insufficient or restricted. With the ongoing advancements in ultrasound technology, particularly the emergence of micromachined ultrasound transducers, these devices hold great potential in facilitating early detection of tissue abnormalities and providing an objective measure of patient health.

3D-Printed Artificial Cilia Arrays: A Versatile Tool for Customizable Mechanosensing

Bio-inspired cilium-based mechanosensors offer a high level of responsiveness, making them suitable for a wide range of industrial, environmental, and biomedical applications. Despite great promise, the development of sensors with multifunctionality, scalability, customizability, and sensing linearity presents challenges due to the complex sensing mechanisms and fabrication methods involved. To this end, high-aspect-ratio polycaprolactone/graphene cilia structures with high conductivity, and facile fabrication are employed to address these challenges. For these 3D-printed structures, an "inter-cilium contact" sensing mechanism that enables the sensor to function akin to an on-off switch, significantly enhancing sensitivity and reducing ambiguity in detection, is proposed. The cilia structures exhibit high levels of customizability, including thickness, height, spacing, and arrangement, while maintaining mechanical robustness. The simplicity of the sensor design enables highly sensitive detection in diverse applications, encompassing airflow and water flow monitoring, braille detection, and debris recognition. Overall, the unique conductive cilia-based sensing mechanism that is proposed brings several advantages, advancing the development of multi-sensing capabilities and flexible electronic skin applications in smart robotics and human prosthetics.

Flexible Optoelectronic Multimodal Proximity/Pressure/Temperature Sensors with Low Signal Interference

Multimodal tactile sensors are a crucial part of intelligent human-machine interaction and collaboration. Simultaneous detection of proximity, pressure, and temperature on a single sensor can greatly promote the safety, interactivity, and compactness of interaction systems. However, severe signal interference and complex decoupling algorithms hinder the actual applications. Here, this work reports a flexible optoelectronic multimodal sensor capable of detecting and decoupling proximity/pressure/temperature by integrating a light waveguide and an interdigital electrode (IDE) into a compact fibrous sensor. Negligible signal interference is realized by combining heterogeneous sensing mechanisms of optics and electronics, which encodes proximity into capacitance, pressure into light intensity and temperature into resistance. The sensor exhibits a large sensing distance of 225 mm with fast responses for proximity detection, a pressure sensitivity of 0.42 N-1 , and a temperature sensitivity of 7% °C-1 . As a proof of concept, a doll equipped with the sensor can accurately discriminate and detect various stimuli, thus achieving safe and immersive interactions with the user. This work opens up promising paths for self-decoupled multimodal sensors and related human/machine/environment interaction applications.

Skin-Inspired Highly Sensitive Tactile Sensors with Ultrahigh Resolution over a Broad Sensing Range

Flexible tactile sensors with high sensitivity, a broad pressure detection range, and high resolution are highly desired for the applications of health monitoring, robots, and the human-machine interface. However, it is still challenging to realize a tactile sensor with high sensitivity and resolution over a wide detection range. Herein, to solve the abovementioned problem, we demonstrate a universal route to develop a highly sensitive tactile sensor with high resolution and a wide pressure range. The tactile sensor is composed of two layers of microstructured flexible electrodes with high modulus and conductive cotton fabric with low modulus. By optimizing the sensing films, the fabricated tactile sensor shows a high sensitivity of 8.9 × 10⁴ kPa^-1 from 2 Pa to 250 kPa because of the high structural compressibility and stress adaptation of the multilayered composite films. Meanwhile, a fast response speed of 18 ms, an ultrahigh resolution of 100 Pa over 100 kPa, and excellent durability over 20 000 loading/unloading cycles are demonstrated. Moreover, a 6 × 6 tactile sensor array is fabricated and shows promising potential application in electronic skin (e-skin). Therefore, employing multilayered composite films for tactile sensors is a novel strategy to achieve high-performance tactile perception in real-time health monitoring and artificial intelligence.

Optimization of electrode positions for equalizing local spatial performance of a tomographic tactile sensor

A tomographic tactile sensor based on the contact resistance of conductors is a high sensitive pressure distribution imaging method and has advantages on the flexibility and scalability of device. While the addition of internal electrodes improves the sensor's spatial resolution, there still remain variations in resolution that depend on the contact position. In this study, we propose an optimization algorithm for electrode positions that improves entire spatial resolution by compensating for local variations in spatial resolution. Simulation results for sensors with 16 or 64 electrodes show that the proposed algorithm improves performance to 0.81 times and 0.93 times in the worst spatial resolution region of the detection area compared to equally spaced grid electrodes. The proposed methods enable tomographic tactile sensors to detect contact pressure distribution more accurately than the conventional methods, providing high-performance tactile sensing for many applications.

Proposal and Implementation of a Procedure for Compliance Recognition of Objects with Smart Tactile Sensors

This paper presents a procedure for classifying objects based on their compliance with information gathered using tactile sensors. Specifically, smart tactile sensors provide the raw moments of the tactile image when the object is squeezed and desqueezed. A set of simple parameters from moment-versus-time graphs are proposed as features, to build the input vector of a classifier. The extraction of these features was implemented in the field programmable gate array (FPGA) of a system on chip (SoC), while the classifier was implemented in its ARM core. Many different options were realized and analyzed, depending on their complexity and performance in terms of resource usage and accuracy of classification. A classification accuracy of over 94% was achieved for a set of 42 different classes. The proposed approach is intended for developing architectures with preprocessing on the embedded FPGA of smart tactile sensors, to obtain high performance in real-time complex robotic systems.

Toward Smart Sensing by MXene

The Internet of Things era has promoted enormous research on sensors, communications, data fusion, and actuators. Among them, sensors are a prerequisite for acquiring the environmental information for delivering to an artificial data center to make decisions. The MXene-based sensors have aroused tremendous interest because of their extraordinary performances. In this review, the electrical, electronic, and optical properties of MXenes are first introduced. Next, the MXene-based sensors are discussed according to the sensing mechanisms such as electronic, electrochemical, and optical methods. Initially, biosensors are introduced based on chemiresistors and field-effect transistors. Besides, the wearable pressure sensor is demonstrated with piezoresistive devices. Third, the electrochemical methods include amperometry and electrochemiluminescence as examples. In addition, the optical approaches refer to surface plasmonic resonance and fluorescence resonance energy transfer. Moreover, the prospects are delivered of multimodal data fusion toward complicated human-like senses. Eventually, future opportunities for MXene research are conveyed in the new material discovery, structure design, and proof-of-concept devices.

Cicada-Wing-Inspired Highly Sensitive Tactile Sensors Based on Elastic Carbon Foam with Nanotextured Surfaces

Electronic devices with tactile and pressure-sensing capabilities are becoming increasingly popular in the automatic industry, human motion/health monitoring, and artificial intelligence applications. Inspired by the natural nanotopography of the cicada wing, we propose here a straightforward strategy to fabricate a highly sensitive tactile sensor through nanotexturing of erected polyaniline (PANI) nanoneedles on a conductive and elastic three-dimensional (3D) carbon skeleton. The robust and compressible carbon networks offer a resilient and conducting matrix to catering complex scenarios; the biomimetic PANI nanoneedles firmly and densely anchored on a 3D carbon skeleton provide intimate electrical contact under subtle deformation. As a result, a piezoresistive tactile sensor with ultrahigh sensitivity (33.52 kPa^-1), fast response/recovery abilities (97/111 ms), and reproducible sensing performance (2500 cycles) is developed, which is capable of distinguishing motions in a wide pressure range from 4.66 Pa to 60 kPa, detecting spatial pressure distribution, and monitoring various gestures in a wireless manner. These excellent performances demonstrate the great potential of nature-inspired tactile sensors for practical human motion monitoring and artificial intelligence applications.

Electrospun Nanofibers Hybrid Wrinkled Micropyramidal Architectures for Elastic Self-Powered Tactile and Motion Sensors

Conformable, sensitive, long-lasting, external power supplies-free multifunctional electronics are highly desired for personal healthcare monitoring and artificial intelligence. Herein, we report a series of stretchable, skin-like, self-powered tactile and motion sensors based on single-electrode mode triboelectric nanogenerators. The triboelectric sensors were composed of ultraelastic polyacrylamide (PAAm)/(polyvinyl pyrrolidone) PVP/(calcium chloride) CaCl₂ conductive hydrogels and surface-modified silicon rubber thin films. The significant enhancement of electrospun polyvinylidene fluoride (PVDF) nanofiber-modified hierarchically wrinkled micropyramidal architectures for the friction layer was studied. The mechanism of the enhanced output performance of the electrospun PVDF nanofibers and the single-side/double-side wrinkled micropyramidal architectures-based sensors has been discussed in detail. The as-prepared devices exhibited excellent sensitivity of a maximum of 20.1 V/N (or 8.03 V/kPa) as tactile sensors to recognize a wide range of forces from 0.1 N to 30 N at low frequencies. In addition, multiple human motion monitoring was demonstrated, such as knee, finger, wrist, and neck movement and voice recognition. This work shows great potential for skin-like epidermal electronics in long-term medical monitoring and intelligent robot applications.

Recent Development of Self-Powered Tactile Sensors Based on Ionic Hydrogels

Hydrogels are three-dimensional polymer networks with excellent flexibility. In recent years, ionic hydrogels have attracted extensive attention in the development of tactile sensors owing to their unique properties, such as ionic conductivity and mechanical properties. These features enable ionic hydrogel-based tactile sensors with exceptional performance in detecting human body movement and identifying external stimuli. Currently, there is a pressing demand for the development of self-powered tactile sensors that integrate ionic conductors and portable power sources into a single device for practical applications. In this paper, we introduce the basic properties of ionic hydrogels and highlight their application in self-powered sensors working in triboelectric, piezoionic, ionic diode, battery, and thermoelectric modes. We also summarize the current difficulty and prospect the future development of ionic hydrogel self-powered sensors.

Skin-Inspired Tactile Sensor on Cellulose Fiber Substrates with Interfacial Microstructure for Health Monitoring and Guitar Posture Feedback

Skin-inspired flexible tactile sensors, with interfacial microstructure, are developed on cellulose fiber substrates for subtle pressure applications. Our device is made of two cellulose fiber substrates with conductive microscale structures, which emulate the randomly distributed spinosum in between the dermis and epidermis layers of the human skin. The microstructures not only permit a higher stress concentration at the tips but also generate electrical contact points and change contact resistance between the top and bottom substrates when the pressure is applied. Meanwhile, cellulose fibers possessing viscoelastic and biocompatible properties are utilized as substrates to mimic the dermis and epidermis layers of the skin. The electrical contact resistances (ECR) are then measured to quantify the tactile information. The microstructures and the substrate properties are studied to enhance the sensors' sensitivity. A very high sensitivity (14.4 kPa^-1) and fast recovery time (approx. 2.5 ms) are achieved in the subtle pressure range (approx. 0-0.05 kPa). The device can detect subtle pressures from the human body due to breathing patterns and voice activity showing its potential for healthcare. Further, the guitar strumming and chord progression of the players with different skill levels are assessed to monitor the muscle strain during guitar playing, showing its potential for posture feedback in playing guitar or another musical instrument.

ESPRESS.0: Eustachian Tube-Inspired Tactile Sensor Exploiting Pneumatics for Range Extension and SenSitivity Tuning

Optimising the sensitivity of a tactile sensor to a specific range of stimuli magnitude usually compromises the sensor's widespread usage. This paper presents a novel soft tactile sensor capable of dynamically tuning its stiffness for enhanced sensitivity across a range of applied forces, taking inspiration from the Eustachian tube in the mammalian ear. The sensor exploits an adjustable pneumatic back pressure to control the effective stiffness of its 20 mm diameter elastomer interface. An internally translocated fluid is coupled to the membrane and optically tracked to measure physical interactions at the interface. The sensor can be actuated by pneumatic pressure to dynamically adjust its stiffness. It is demonstrated to detect forces as small as 0.012 N, and to be sensitive to a difference of 0.006 N in the force range of 35 to 40 N. The sensor is demonstrated to be capable of detecting tactile cues on the surface of objects in the sub-millimetre scale. It is able to adapt its compliance to increase its ability for distinguishing between stimuli with similar stiffnesses (0.181 N/mm difference) over a large range (0.1 to 1.1 N/mm) from only a 0.6 mm deep palpation. The sensor is intended to interact comfortably with skin, and the feasibility of its use in palpating tissue in search of hard inclusions is demonstrated by locating and estimating the size of a synthetic hard node embedded 20 mm deep in a soft silicone sample. The results suggest that the sensor is a good candidate for tactile tasks involving unpredictable or unknown stimuli.

Porous-Structure-Promoted Tribo-Induced High-Performance Self-Powered Tactile Sensor toward Remote Human-Machine Interaction

Self-powered tactile sensor with versatile functions plays a significant role in the development of an intelligent human-machine interaction (HMI) system. Herein, a hybrid self-powered porous-structured tactile sensor (SPTS) is proposed by monolithically integrating a porous triboelectrification-induced electroluminescence (TIEL) component and a single-electrode triboelectric nanogenerator with the high charge generation in the bulk volume. At a low pressure of 10 kPa, TIEL intensity can be significantly improved by three times, which is superior to that in previous reports, with enhanced triboelectricity. Based on the enhancement brought by the porous structure and optimized parameters, the SPTS achieves significant sensing performance in both optical and electrical modes. To demonstrate the potential of practical applications, a programmable optical and electrical dual-mode HMI system is established based on SPTS to remotely control an intelligent vehicle and operate a computer game through identifying finger touch trajectories. This work not only contributes a new economical-effective methodology toward a high-performance tribo-induced self-powered tactile sensor but also facilitates the remote control of HMI with dual-mode functionality, which has broad potential applications in the fields of intelligent robots, augmented reality, flexible wearable electronics, and smart home.

Dual-Scale Porous Composite for Tactile Sensor with High Sensitivity over an Ultrawide Sensing Range

Porous structures have been utilized in tactile sensors to improve sensitivity owing to their excellent deformability. Recently, tactile sensors using porous structures have been used in practical applications, such as bio-signal monitoring. However, highly sensitive responses are limited to the low-pressure range, and their sensitivity significantly decreases in a higher-pressure range. Several approaches for developing tactile sensors with high sensitivity overing a wide pressure range have been proposed; however, achieving high sensitivity and wide sensing range remains a crucial challenge. This report presents a carbon nanotube (CNT)-coated CNT-polydimethylsiloxane (PDMS) composite having dual-scale pores for tactile sensors with high sensitivity over a wide pressure range. The porous polymer frame formed with dense pores of dual sizes facilitates the closure of large and small pores at low and high pressures, respectively. This results in an apparent increase in the number of contact points between the CNT-CNT at the pores even under a wide pressure range. Furthermore, the piezoresistivity of the CNT-PDMS composite contributes to achieving a high sensitivity of the tactile sensor over a wide pressure range. Based on these mechanisms, various human movements over a broad pressure spectrum are monitored to investigate the practical usefulness of the sensor.

Full-hand electrotactile feedback using electronic skin and matrix electrodes for high-bandwidth human-machine interfacing

Tactile feedback is relevant in a broad range of human-machine interaction systems (e.g. teleoperation, virtual reality and prosthetics). The available tactile feedback interfaces comprise few sensing and stimulation units, which limits the amount of information conveyed to the user. The present study describes a novel technology that relies on distributed sensing and stimulation to convey comprehensive tactile feedback to the user of a robotic end effector. The system comprises six flexible sensing arrays (57 sensors) integrated on the fingers and palm of a robotic hand, embedded electronics (64 recording channels), a multichannel stimulator and seven flexible electrodes (64 stimulation pads) placed on the volar side of the subject's hand. The system was tested in seven subjects asked to recognize contact positions and identify contact sliding on the electronic skin, using distributed anode configuration (DAC) and single dedicated anode configuration. The experiments demonstrated that DAC resulted in substantially better performance. Using DAC, the system successfully translated the contact patterns into electrotactile profiles that the subjects could recognize with satisfactory accuracy ([Formula: see text] for static and [Formula: see text] for dynamic patterns). The proposed system is an important step towards the development of a high-density human-machine interfacing between the user and a robotic hand. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

HiVTac: A High-Speed Vision-Based Tactile Sensor for Precise and Real-Time Force Reconstruction with Fewer Markers

Although they have been under development for years and are attracting a lot of attention, vision-based tactile sensors still have common defects—the use of such devices to infer the direction of external forces is poorly investigated, and the operating frequency is too low for them to be applied in practical scenarios. Moreover, discussion of the deformation of elastomers used in vision-based tactile sensors remains insufficient. This research focuses on analyzing the deformation of a thin elastic layer on a vision-based tactile sensor by establishing a simplified deformation model, which is cross-validated using the finite element method. Further, this model suggests a reduction in the number of markers required by a vision-based tactile sensor. In subsequent testing, a prototype HiVTac is fabricated, and it demonstrates superior accuracy to its vision-based tactile sensor counterparts in reconstructing an external force. The average error of inferring the direction of external force is 0.32∘, and the root mean squared error of inferring the magnitude of the external force is 0.0098 N. The prototype was capable of working at a sampling rate of 100 Hz and a processing frequency of 1.3 kHz, even on a general PC, allowing for real-time reconstructions of not only the direction but also the magnitude of an external force.

Endowing a NAO Robot With Practical Social-Touch Perception

Social touch is essential to everyday interactions, but current socially assistive robots have limited touch-perception capabilities. Rather than build entirely new robotic systems, we propose to augment existing rigid-bodied robots with an external touch-perception system. This practical approach can enable researchers and caregivers to continue to use robotic technology they have already purchased and learned about, but with a myriad of new social-touch interactions possible. This paper presents a low-cost, easy-to-build, soft tactile-perception system that we created for the NAO robot, as well as participants’ feedback on touching this system. We installed four of our fabric-and-foam-based resistive sensors on the curved surfaces of a NAO’s left arm, including its hand, lower arm, upper arm, and shoulder. Fifteen adults then performed five types of affective touch-communication gestures (hitting, poking, squeezing, stroking, and tickling) at two force intensities (gentle and energetic) on the four sensor locations; we share this dataset of four time-varying resistances, our sensor patterns, and a characterization of the sensors’ physical performance. After training, a gesture-classification algorithm based on a random forest identified the correct combined touch gesture and force intensity on windows of held-out test data with an average accuracy of 74.1%, which is more than eight times better than chance. Participants rated the sensor-equipped arm as pleasant to touch and liked the robot’s presence significantly more after touch interactions. Our promising results show that this type of tactile-perception system can detect necessary social-touch communication cues from users, can be tailored to a variety of robot body parts, and can provide HRI researchers with the tools needed to implement social touch in their own systems.

A Highly Conducting Polymer for Self-Healable, Printable, and Stretchable Organic Electrochemical Transistor Arrays and Near Hysteresis-Free Soft Tactile Sensors

A stretchable and self-healable conductive material with high conductivity is critical to high-performance wearable electronics and integrated devices for applications where large mechanical deformation is involved. While there has been great progress in developing stretchable and self-healable conducting materials, it remains challenging to concurrently maintain and recover such functionalities before and after healing. Here, a highly stretchable and autonomic self-healable conducting film consisting of a conducting polymer (poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate), PEDOT:PSS) and a soft-polymer (poly(2-acrylamido-2-methyl-1-propanesulfonic acid), PAAMPSA) is reported. The optimal film exhibits outstanding stretchability as high as 630% and high electrical conductivity of 320 S cm^-1 , while possessing the ability to repair both mechanical and electrical breakdowns when undergoing severe damage at ambient conditions. This polymer composite film is further utilized in a tactile sensor, which exhibits good pressure sensitivity of 164.5 kPa^-1 , near hysteresis-free, an ultrafast response time of 19 ms, and excellent endurance over 1500 consecutive presses. Additionally, an integrated 5 × 4 stretchable and self-healable organic electrochemical transistor (OECT) array with great device performance is successfully demonstrated. The developed stretchable and autonomic self-healable conducting film significantly increases the practicality and shelf life of wearable electronics, which in turn, reduces maintenance costs and build-up of electronic waste.

Review of Recent Bio-Inspired Design and Manufacturing of Whisker Tactile Sensors

Whisker sensors are a class of tactile sensors that have recently attracted attention. Inspired by mammals' whiskers known as mystacial vibrissae, they have displayed tremendous potential in a variety of applications e.g., robotics, underwater vehicles, minimally invasive surgeries, and leak detection. This paper provides a supplement to the recent tactile sensing techniques' designs of whiskers that only sense at their base, as well as the materials employed, and manufacturing techniques. The article delves into the technical specifications of these sensors, such as the resolution, measurement range, sensitivity, durability, and recovery time, which determine their performance. The sensors' sensitivity varies depending on the measured physical quantity; for example, the pressure sensors had an intermediate sensitivity of 58%/Pa and a response time of around 90 ms, whereas the force sensors that function based on piezoelectric effects exhibited good linearity in the measurements with a resolution of 3 µN and sensitivity of 0.1682 mV/µN. Some sensors were used to perform spatial mapping and the identification of the geometry and roughness of objects with a reported resolution of 25 nm. The durability and recovery time showed a wide range of values, with the maximum durability being 10,000 cycles and the shortest recovery time being 5 ms. Furthermore, the paper examines the fabrication of whiskers at the micro- and nanoscales, as well as their contributions to mechanical and thermal behavior. The commonly used manufacturing techniques of 3D printing, PDMS casting, and screen printing were used in addition to several micro and nanofabrication techniques such as photolithography, etching, and chemical vapor deposition. Lastly, the paper discusses the main potential applications of these sensors and potential research gaps in this field. In particular, the operation of whisker sensors under high temperatures or high pressure requires further investigation, as does the design of sensors to explore larger topologies.

An Atlas for the Inkjet Printing of Large-Area Tactile Sensors

This review aims to discuss the inkjet printing technique as a fabrication method for the development of large-area tactile sensors. The paper focuses on the manufacturing techniques and various system-level sensor design aspects related to the inkjet manufacturing processes. The goal is to assess how printed electronics simplify the fabrication process of tactile sensors with respect to conventional fabrication methods and how these contribute to overcoming the difficulties arising in the development of tactile sensors for real robot applications. To this aim, a comparative analysis among different inkjet printing technologies and processes is performed, including a quantitative analysis of the design parameters, such as the costs, processing times, sensor layout, and general system-level constraints. The goal of the survey is to provide a complete map of the state of the art of inkjet printing, focusing on the most effective topics for the implementation of large-area tactile sensors and a view of the most relevant open problems that should be addressed to improve the effectiveness of these processes.

Highly Sensitive Flexible Tactile Sensors in Wide Sensing Range Enabled by Hierarchical Topography of Biaxially Strained and Capillary-Densified Carbon Nanotube Bundles

Flexible tactile sensors with high sensitivity have received considerable attention for their use in wearable electronics, human-machine interfaces, and health-monitoring devices. Although various micro/nanostructured materials are introduced for high-performance tactile sensors, simultaneously obtaining high sensitivity and a wide sensing range remains challenging. Here, a resistive tactile sensor is presented based on the hierarchical topography of carbon nanotubes (CNTs) prepared by a low-cost and straightforward manufacturing process. The 3D hierarchical structure of the CNTs over large areas is formed by transferring vertically aligned CNT bundles to a prestrained elastomer substrate and subsequently densifying them through capillary forming, providing a monotonic increase in the contact area as applied pressure. The deformable and hierarchical structure of CNTs allows the sensor to exhibit a wide sensing range (0-100 kPa), high sensitivity (141.72 kPa^-1 ), and low detection limit (10 Pa). Additionally, the capillary-formed CNT structure results in increased durability of the sensor over repeated pressures. Based on these advantages, meaningful applications of tactile sensors, such as object recognition gloves and multidirectional force perceptions, are successfully realized. Given the scalable fabrication method, 3D hierarchically structured CNTs provide an essential step toward next-generation wearable devices.

Anisotropic Shear-Sensitive Tactile Sensors with Programmable Elastomers for Robotic Manipulations

High-performance tactile sensors are urgently demanded in various intensive interactive scenarios, e.g., texture detection, robotic interaction with fragile objects, and motion direction recognition, where dynamic conditions are involved with complex tangential forces or vibrations. Although many microstructured/porous sensors can perceive tangential forces, their isotropic structures that lack programmability lead them to be incapable of sensing the direction of forces and restrain their tunability for complex situations, e.g., a wide sensing range for large forces and high sensitivity for gentle forces. Here, by tuning the programmable microstructures (microcolumns and microfilms) of an elastomeric active layer, we propose a simple principle to flexibly tune the shear sensitivity of an anisotropic porous sensor and bring a 10-fold distinction of anisotropy with a wide range of shear sensitivity (from 0.07 to 0.7 N^-1). The fabricated tactile sensors can be used in various robotic manipulations resiliently, for instance, morphology and topology identification of curved surfaces, delicate interactive manipulations, and recognizing the relative motion of two contacting objects. Our work introduces a simple and effective strategy for tailoring flexible shear-sensitive sensors for diverse dexterous robotic manipulations during complex interactions.

Recent Progress in Flexible Tactile Sensors for Human-Interactive Systems: From Sensors to Advanced Applications

Flexible tactile sensors capable of measuring mechanical stimuli via physical contact have attracted significant attention in the field of human-interactive systems. The utilization of tactile information can complement vision and/or sound interaction and provide new functionalities. Recent advancements in micro/nanotechnology, material science, and information technology have resulted in the development of high-performance tactile sensors that reach and even surpass the tactile sensing ability of human skin. Here, important advances in flexible tactile sensors over recent years are summarized, from sensor designs to system-level applications. This review focuses on the representative strategies based on design and material configurations for improving key performance parameters including sensitivity, detection range/linearity, response time/hysteresis, spatial resolution/crosstalk, multidirectional force detection, and insensitivity to other stimuli. System-level integration for practical applications beyond conceptual prototypes and promising applications, such as artificial electronic skin for robotics and prosthetics, wearable controllers for electronics, and bidirectional communication tools, are also discussed. Finally, perspectives on issues regarding further advances are provided.

Experimental Evaluation of Tactile Sensors for Compliant Robotic Hands

The sense of touch is a key aspect in the human capability to robustly grasp and manipulate a wide variety of objects. Despite many years of development, there is still no preferred solution for tactile sensing in robotic hands: multiple technologies are available, each one with different benefits depending on the application. This study compares the performance of different tactile sensors mounted on the variable stiffness gripper CLASH 2F, including three commercial sensors: a single taxel sensor from the companies Tacterion and Kinfinity, the Robotic Finger Sensor v2 from Sparkfun, plus a self-built resistive 3 × 3 sensor array, and two self-built magnetic 3-DoF touch sensors, one with four taxels and one with one taxel. We verify the minimal force detectable by the sensors, test if slip detection is possible with the available taxels on each sensor, and use the sensors for edge detection to obtain the orientation of the grasped object. To evaluate the benefits obtained with each technology and to assess which sensor fits better the control loop in a variable stiffness hand, we use the CLASH gripper to grasp fruits and vegetables following a published benchmark for pick and place operations. To facilitate the repetition of tests, the CLASH hand is endowed with tactile buttons that ease human–robot interactions, including execution of a predefined program, resetting errors, or commanding the full robot to move in gravity compensation mode.

Micro-Nano Processing of Active Layers in Flexible Tactile Sensors via Template Methods: A Review

Template methods are regarded as an important method for micro-nano processing in the active layer of flexible tactile sensors. These template methods use physical/chemical processes to introduce micro-nano structures on the active layer, which improves many properties including sensitivity, response/recovery time, and detection limit. However, since the processing process and applicable conditions of the template method have not yet formed a perfect system, the development and commercialization of flexible tactile sensors based on the template method are still at a relatively slow stage. Despite the above obstacles, advances in microelectronics, materials science, nanoscience, and other disciplines have laid the foundation for various template methods, enabling the continuous development of flexible tactile sensors. Therefore, a comprehensive and systematic review of flexible tactile sensors based on the template method is needed to further promote progress in this field. Here, the unique advantages and shortcomings of various template methods are summarized in detail and discuss the research progress and challenges in this field. It is believed that this review will have a significant impact on many fields of flexible electronics, which is beneficial to promote the cross-integration of multiple fields and accelerate the development of flexible electronic devices.

Online Morphological Adaptation for Tactile Sensing Augmentation

Sensor morphology and structure has the ability to significantly aid and improve tactile sensing capabilities, through mechanisms such as improved sensitivity or morphological computation. However, different tactile tasks require different morphologies posing a challenge as to how to best design sensors, and also how to enable sensor morphology to be varied. We introduce a jamming filter which, when placed over a tactile sensor, allows the filter to be shaped and molded online, thus varying the sensor structure. We demonstrate how this is beneficial for sensory tasks analyzing how the change in sensor structure varies the information that is gained using the sensor. Moreover, we show that appropriate morphology can significantly influence discrimination, and observe how the selection of an appropriate filter can increase the object classification accuracy when using standard classifiers by up to 28%.

Bio-Inspired Hybrid Dielectric for Capacitive and Triboelectric Tactile Sensors with High Sensitivity and Ultrawide Linearity Range

The trade-off between sensitivity and linearity is critical for preserving the high pressure-resolution over a broad range and simplifying the signal processing/conversion of flexible tactile sensors. Conventional dielectrics suffer from the difficulty of quantitatively controlling the interacted mechanical and dielectric properties, thus causing the restricted sensitivity and linearity of capacitive sensors. Herein, inspired by human skin, a novel hybrid dielectric composed of a low-permittivity (low-k) micro-cilia array, a high-permittivity (high-k) rough surface, and micro-dome array is developed. The pressure-induced series-parallel conversion between the low-k and high-k components of the hybrid dielectric enables the linear effective dielectric constant and controllable initial/resultant capacitance. The gradient compressibility of the hybrid dielectric enables the linear behavior of elastic modulus with pressures, which derives the capacitance variation determined by the effective dielectric constant. Therefore, an ultrawide linearity range up to 1000 kPa and a high sensitivity of 0.314 kPa^-1 are simultaneously achieved by the optimized hybrid dielectric. The design is also applicable for triboelectric tactile sensors, which realizes the similar linear behavior of output voltage and enhanced sensitivity. With the high pressure-resolution across a broad range, potential applications such as healthcare monitoring in diverse scenarios and control command conversion via a single sensor are demonstrated.

Multimodal Digital X-ray Scanners with Synchronous Mapping of Tactile Pressure Distributions using Perovskites

Visual and tactile information are the key intuitive perceptions in sensory systems, and the synchronized detection of these two sensory modalities can enhance accuracy of object recognition by providing complementary information between them. Herein, multimodal integration of flexible, high-resolution X-ray detectors with a synchronous mapping of tactile pressure distributions for visualizing internal structures and morphologies of an object simultaneously is reported. As a visual-inspection method, perovskite materials that convert X-rays into charge carriers directly are synthesized. By incorporating pressure-sensitive air-dielectric transistors in the perovskite components, X-ray detectors with dual modalities (i.e., vision and touch) are attained as an active-matrix platform for digital visuotactile examinations. Also, in vivo X-ray imaging and pressure sensing are demonstrated using a live rat. This multiplexed platform has high spatial resolution and good flexibility, thereby providing highly accurate inspection and diagnoses even for the distorted images of nonplanar objects.

Fabrication of Pressure Sensor Using Electrospinning Method for Robotic Tactile Sensing Application

Tactile sensors are widely used by the robotics industries over decades to measure force or pressure produced by external stimuli. Piezoelectric-based pressure sensors have intensively been investigated as promising candidates for tactile sensing applications. In contrast, piezoelectric-based pressure sensors are expensive due to their high cost of manufacturing and expensive base materials. Recently, an effect similar to the piezoelectric effect has been identified in non-piezoelectric polymers such as poly(d,l-lactic acid (PDLLA), poly(methyl methacrylate) (PMMA) and polystyrene. Hence investigations were conducted on alternative materials to find their suitability. In this article, we used inexpensive atactic polystyrene (aPS) as the base polymer and fabricated functional fibers using an electrospinning method. Fiber morphologies were studied using a field-emission scanning electron microscope and proposed a unique pressure sensor fabrication method. A fabricated pressure sensor was subjected to different pressures and corresponding electrical and mechanical characteristics were analyzed. An open circuit voltage of 3.1 V was generated at 19.9 kPa applied pressure, followed by an integral output charge (ΔQ), which was measured to calculate the average apparent piezoelectric constant d_app and was found to be 12.9 ± 1.8 pC N⁻¹. A fabricated pressure sensor was attached to a commercially available robotic arm to mimic the tactile sensing.

Flexible Piezoresistive Tactile Sensor Based on Polymeric Nanocomposites with Grid-Type Microstructure

Piezoresistive tactile sensors made using nanocomposite polymeric materials have been shown to possess good flexibility, electrical performance, and sensitivity. However, the sensing performance, especially in the low-pressure range, can be significantly improved by enabling uniform dispersion of the filler material and utilization of effective structural designs that improve the tactile sensing performance. In this study, a novel flexible piezoresistive tactile sensor with a grid-type microstructure was fabricated using polymer composites comprising multi-walled carbon nanotubes (MWCNTs) as the conductive filler and polydimethylsiloxane (PDMS) as the polymeric matrix. The research focused on improving the tactile sensor performance by enabling uniform dispersion of filler material and optimizing sensor design and structure. The doping weight ratio of MWCNTs in PDMS varied from 1 wt.% to 10 wt.% using the same grid structure-sensing layer (line width, line spacing, and thickness of 1 mm). The sensor with a 7 wt.% doping ratio had the most stable performance, with an observed sensitivity of 6.821 kPa⁻¹ in the lower pressure range of 10–20 kPa and 0.029 kPa⁻¹ in the saturation range of 30–200 kPa. Furthermore, the dimensions of the grid structure were optimized and the relationship between grid structure, sensitivity, and sensing range was correlated. The equation between pressure and resistance output was derived to validate the principle of piezoresistance. For the grid structure, dimensions with line width, line spacing, and thickness of 1, 1, and 0.5 mm were shown to have the most stable and improved response. The observed sensitivity was 0.2704 kPa⁻¹ in the lower pressure range of 50–130 kPa and 0.0968 kPa⁻¹ in the saturation range of 140–200 kPa. The piezoresistive response, which was mainly related to the quantum tunneling effect, can be optimized based on the dopant concentration and the grid microstructure. Furthermore, the tactile sensor showed a repeatable response, and the accuracy was not affected by temperature changes in the range of 10 to 40 °C and humidity variations from 50 to 80%. The maximum error fluctuation was about 5.6% with a response delay time of about 1.6 ms when cyclic loading tests were performed under a normal force of 1 N for 10,200 cycles. Consequently, the proposed tactile sensor shows practical feasibility for a wide range of wearable technologies and robotic applications such as touch detection and grasping.

Using a robotic teleoperation system for haptic exploration

Introduction

When children with physical impairments cannot perform hand movements for haptic exploration, they miss opportunities to learn about object properties. Robotics systems with haptic feedback may better enable object exploration.

Methods

Twenty-four adults and ten children without physical impairments, and one adult with physical impairments, explored tools to mix substances or transport different sized objects. All participants completed the tasks with both a robotic system and manual exploration. Exploratory procedures used to determine object properties were also observed.

Results

Adults and children accurately identified appropriate tools for each task using manual exploration, but they were less accurate using the robotic system. The adult with physical impairment identified appropriate tools for transport in both conditions, however had difficulty identifying tools used for mixing substances. A new exploratory procedure was observed, Tapping, when using the robotic system.

Conclusions

Adults and children could make judgements on tool utility for tasks using both manual exploration and the robotic system, however they experienced limitations in the robotics system that require more study. The adult with disabilities required less assistance to explore tools when using the robotic system. The robotic system may be a feasible way for individuals with physical disabilities to perform haptic exploration.

Large-Area, Crosstalk-Free, Flexible Tactile Sensor Matrix Pixelated by Mesh Layers

Tactile sensor arrays have attracted considerable attention for their use in diverse applications, such as advanced robotics and interactive human-machine interfaces. However, conventional tactile sensor arrays suffer from electrical crosstalk caused by current leakages between the tactile cells. The approaches that have been proposed thus far to overcome this issue require complex rectifier circuits or a serial fabrication process. This article reports a flexible tactile sensor array fabricated through a batch process using a mesh. A carbon nanotube-polydimethylsiloxane composite is used to form an array of sensing cells in the mesh through a simple "dip-coating" process and is cured into a concave shape. The contact area between the electrode and the composite changes significantly under pressure, resulting in an excellent sensitivity (5.61 kPa^-1) over a wide range of pressure up to 600 kPa. The mesh separates the composite into the arranged sensing cells to prevent the electrical connection between adjacent cells and simultaneously connects each cell mechanically. Additionally, the sensor shows superior durability compared with previously reported tactile sensors because the mesh acts as a support beam. Furthermore, the tactile sensor array is successfully utilized as a Braille reader via information processing based on machine learning.

Transfer of Learning from Vision to Touch: A Hybrid Deep Convolutional Neural Network for Visuo-Tactile 3D Object Recognition

Transfer of learning or leveraging a pre-trained network and fine-tuning it to perform new tasks has been successfully applied in a variety of machine intelligence fields, including computer vision, natural language processing and audio/speech recognition. Drawing inspiration from neuroscience research that suggests that both visual and tactile stimuli rouse similar neural networks in the human brain, in this work, we explore the idea of transferring learning from vision to touch in the context of 3D object recognition. In particular, deep convolutional neural networks (CNN) pre-trained on visual images are adapted and evaluated for the classification of tactile data sets. To do so, we ran experiments with five different pre-trained CNN architectures and on five different datasets acquired with different technologies of tactile sensors including BathTip, Gelsight, force-sensing resistor (FSR) array, a high-resolution virtual FSR sensor, and tactile sensors on the Barrett robotic hand. The results obtained confirm the transferability of learning from vision to touch to interpret 3D models. Due to its higher resolution, tactile data from optical tactile sensors was demonstrated to achieve higher classification rates based on visual features compared to other technologies relying on pressure measurements. Further analysis of the weight updates in the convolutional layer is performed to measure the similarity between visual and tactile features for each technology of tactile sensing. Comparing the weight updates in different convolutional layers suggests that by updating a few convolutional layers of a pre-trained CNN on visual data, it can be efficiently used to classify tactile data. Accordingly, we propose a hybrid architecture performing both visual and tactile 3D object recognition with a MobileNetV2 backbone. MobileNetV2 is chosen due to its smaller size and thus its capability to be implemented on mobile devices, such that the network can classify both visual and tactile data. An accuracy of 100% for visual and 77.63% for tactile data are achieved by the proposed architecture.

Tactile Perception Technologies and Their Applications in Minimally Invasive Surgery: A Review

Minimally invasive surgery (MIS) has been the preferred surgery approach owing to its advantages over conventional open surgery. As a major limitation, the lack of tactile perception impairs the ability of surgeons in tissue distinction and maneuvers. Many studies have been reported on industrial robots to perceive various tactile information. However, only force data are widely used to restore part of the surgeon’s sense of touch in MIS. In recent years, inspired by image classification technologies in computer vision, tactile data are represented as images, where a tactile element is treated as an image pixel. Processing raw data or features extracted from tactile images with artificial intelligence (AI) methods, including clustering, support vector machine (SVM), and deep learning, has been proven as effective methods in industrial robotic tactile perception tasks. This holds great promise for utilizing more tactile information in MIS. This review aims to provide potential tactile perception methods for MIS by reviewing literatures on tactile sensing in MIS and literatures on industrial robotic tactile perception technologies, especially AI methods on tactile images.

Interdigitated Sensor Based on a Silicone Foam for Subtle Robotic Manipulation

In this contribution, a soft sensor configuration based on silicone foam is developed to measure compressive forces in the range of 50 N with the aim of providing proprioceptive capabilities to conventional robotic manipulators based on hard materials. This then makes them capable of interacting with soft and fragile objects without damage. The concept relies on interdigitated electrodes that are patterned on the backside of the sensor to generate a fringing electric field into a soft compressible polymeric foam. The deformation of the foam causes changes to relative permittivity as the air-filled cells compress. The model in this article shows how the different parameters of the foam, such as air volume fraction, permittivity, and Young's modulus, affect the stiffness and electrical sensitivity of the sensor, and how controlling the porosity of the foam is key to optimizing the sensitivity of the sensor. This sensor is easy to fabricate and does not require compliant electrodes, while exhibiting high sensitivity values of 33% capacitance change for as little as 10 N applied force.

Polyethylene-Carbon Composite (Velostat®) Based Tactile Sensor

The progress observed in ‘soft robotics’ brought some promising research in flexible tactile, pressure and force sensors, which can be based on polymeric composite materials. Therefore, in this paper, we intend to evaluate the characteristics of a force-sensitive material—polyethylene-carbon composite (Velostat^®) by implementing this material into the design of the flexible tactile sensor. We have explored several possibilities to measure the electrical signal and assessed the mechanical and time-dependent properties of this tactile sensor. The response of the sensor was evaluated by performing tests in static, long-term load and cyclic modes. Experimental results of loading cycle measurements revealed the hysteresis and nonlinear properties of the sensor. The transverse resolution of the sensor was defined by measuring the response of the sensor at different distances from the loaded point. Obtained dependencies of the sensor’s sensitivity, hysteresis, response time, transversal resolution and deformation on applied compressive force promise a practical possibility to use the polyethylene-carbon composite as a sensitive material for sensors with a single electrode pair or its matrix. The results received from experimental research have defined the area of the possible implementation of the sensor based on a composite material—Velostat^®.

Deep-Learning-Based Deconvolution of Mechanical Stimuli with Ti3C2Tx MXene Electromagnetic Shield Architecture via Dual-Mode Wireless Signal Variation Mechanism

Passive component-based soft resonators have been spotlighted in the field of wearable and implantable devices due to their remote operation capability and tunable properties. As the output signal of the resonator-based wireless communication device is given in the form of a vector (i.e., a spectrum of reflection coefficient), multiple information can, in principle, be stored and interpreted. Herein, we introduce a device that can deconvolute mechanical stimuli from a single wireless signal using dual-mode operation, specifically enabled by the use of Ti₃C₂T_x MXene. MXene's strong electromagnetic shielding effect enables the resonator to simultaneously measure pressure and strain without overlapping its output signal, unlike other conductive counterparts that are deficient in shielding ability. Furthermore, convolutional neural-network-based deep learning was implemented to predict the pressure and strain values from unforeseen output wireless signals. Our MXene-integrated wireless device can also be utilized as an on-skin mechanical stimuli sensor for rehabilitation monitoring after orthopedic surgery. The dual-mode signal variation mechanism enabled by integration of MXene allows wireless communication systems to efficiently handle various information simultaneously, through which multistimuli sensing capability can be imparted into passive component-based wearable and implantable electrical devices.

A Non-Touchscreen Tactile Wearable Interface as an Alternative to Touchscreen-Based Wearable Devices

Current consumer wearable devices such as smartwatches mostly rely on touchscreen-based user interfaces. Even though touch-based user interfaces help smartphone users quickly adapt to wearable devices with touchscreens, there exist several limitations. In this paper, we propose a non-touchscreen tactile wearable interface as an alternative to touchscreens on wearable devices. We designed and implemented a joystick-integrated smartwatch prototype to demonstrate our non-touchscreen tactile wearable interface. We iteratively improved and updated our prototype to improve and polish interaction ideas and prototype integration. To show feasibility of our approach, we compared and contrasted form factors of our prototype against the latest nine commercial smartwatches in terms of their dimensions. We also show response time and accuracy of our wearable interface to discuss our rationale for an alternative and usable wearable UI. With the proposed tactile wearable user interface, we believe our approach may serve as a cohesive single interaction device to enable various cross-device interaction scenarios and applications.

Design and Evaluation of Magnetic Hall Effect Tactile Sensors for Use in Sensorized Splints

Splinting techniques are widely used in medicine to inhibit the movement of arthritic joints. Studies into the effectiveness of splinting as a method of pain reduction have generally yielded positive results, however, no significant difference has been found in clinical outcomes between splinting types. Tactile sensing has shown great promise for the integration into splinting devices and may offer further information into applied forces to find the most effective methods of splinting. Hall effect-based tactile sensors are of particular interest in this application owing to their low-cost, small size, and high robustness. One complexity of the sensors is the relationship between the elastomer geometry and the measurement range. This paper investigates the design parameters of Hall effect tactile sensors for use in hand splinting. Finite element simulations are used to locate the areas in which sensitivity is high in order to optimise the deflection range of the sensor. Further simulations then investigate the mechanical response and force ranges of the elastomer layer under loading which are validated with experimental data. A 4 mm radius, 3 mm-thick sensor is identified as meeting defined sensing requirements for range and sensitivity. A prototype sensor is produced which exhibits a pressure range of 45 kPa normal and 6 kPa shear. A proof of principle prototype demonstrates how this can be integrated to form an instrumented splint with multi-axis sensing capability and has the potential to inform clinical practice for improved splinting.

An Embedded, Multi-Modal Sensor System for Scalable Robotic and Prosthetic Hand Fingers

Grasping and manipulation with anthropomorphic robotic and prosthetic hands presents a scientific challenge regarding mechanical design, sensor system, and control. Apart from the mechanical design of such hands, embedding sensors needed for closed-loop control of grasping tasks remains a hard problem due to limited space and required high level of integration of different components. In this paper we present a scalable design model of artificial fingers, which combines mechanical design and embedded electronics with a sophisticated multi-modal sensor system consisting of sensors for sensing normal and shear force, distance, acceleration, temperature, and joint angles. The design is fully parametric, allowing automated scaling of the fingers to arbitrary dimensions in the human hand spectrum. To this end, the electronic parts are composed of interchangeable modules that facilitate the mechanical scaling of the fingers and are fully enclosed by the mechanical parts of the finger. The resulting design model allows deriving freely scalable and multimodally sensorised fingers for robotic and prosthetic hands. Four physical demonstrators are assembled and tested to evaluate the approach.

Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems

Neuroprosthetics and neuromodulation represent a promising field for several related applications in the central and peripheral nervous system, such as the treatment of neurological disorders, the control of external robotic devices, and the restoration of lost tactile functions. These actions are allowed by the neural interface, a miniaturized implantable device that most commonly exploits electrical energy to fulfill these operations. A neural interface must be biocompatible, stable over time, low invasive, and highly selective; the challenge is to develop a safe, compact, and reliable tool for clinical applications. In case of anatomical impairments, neuroprosthetics is bound to the need of exploring the surrounding environment by fast-responsive and highly sensitive artificial tactile sensors that mimic the natural sense of touch. Tactile sensors and neural interfaces are closely interconnected since the readouts from the first are required to convey information to the neural implantable apparatus. The role of these devices is pivotal hence technical improvements are essential to ensure a secure system to be eventually adopted in daily life. This review highlights the fundamental criteria for the design and microfabrication of neural interfaces and artificial tactile sensors, their use in clinical applications, and future enhancements for the release of a second generation of devices.

Experimental Assessment of the Interface Electronic System for PVDF-Based Piezoelectric Tactile Sensors

Tactile sensors are widely employed to enable the sense of touch for applications such as robotics and prosthetics. In addition to the selection of an appropriate sensing material, the performance of the tactile sensing system is conditioned by its interface electronic system. On the other hand, due to the need to embed the tactile sensing system into a prosthetic device, strict requirements such as small size and low power consumption are imposed on the system design. This paper presents the experimental assessment and characterization of an interface electronic system for piezoelectric tactile sensors for prosthetic applications. The interface electronic is proposed as part of a wearable system intended to be integrated into an upper limb prosthetic device. The system is based on a low power arm-microcontroller and a DDC232 device. Electrical and electromechanical setups have been implemented to assess the response of the interface electronic with PVDF-based piezoelectric sensors. The results of electrical and electromechanical tests validate the correct functionality of the proposed system.