Human tactile perception as a standard for artificial tactile sensing—a review

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

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

An optoionic hydrogel with UV-regulated ion conductivity for reprogrammable iontronics: Logic processing and image sensing

The development of smart hydrogels capable of actively controlling ion conductivity is of paramount importance for iontronics. Most current work in this field focuses on enhancing the hydrogels' ion conductivity. Few successes have been seen in achieving spatial regulation of ion flow through external control. Among various controls, light gives the best spatial and temporal resolution for practical iontronic applications. However, developing hydrogels that can generate drastic ion concentration change upon photoirradiation for tunable conductivity is challenging. Very few molecules can enable photoion generation, and most of them are hydrophobic and low quantum yield. Here, we present an optoionic hydrogel that uses triphenylmethane leuconitrile (TPMLN) for ultraviolet-regulated ion conductivity. Through postpolymerization TPMLN synthesizing, we can incorporate high concentration of the hydrophobic TPMLN in hydrogels without compromising the hydrogel's mechanical integrity. Upon light irradiation, the hydrogel's local conductivity can change an unprecedented 10-fold. We also demonstrated soft optoionic devices that are capable of logic processing and photo imaging.

Skin-Inspired Multi-Modal Mechanoreceptors for Dynamic Haptic Exploration

Active sensing is a fundamental aspect of human and animal interactions with the environment, providing essential information about the hardness, texture, and tackiness of objects. This ability stems from the presence of diverse mechanoreceptors in the skin, capable of detecting a wide range of stimuli and from the sensorimotor control of biological mechanisms. In contrast, existing tactile sensors for robotic applications typically excel in identifying only limited types of information, lacking the versatility of biological mechanoreceptors and the requisite sensing strategies to extract tactile information proactively. Here, inspired by human haptic perception, a skin-inspired artificial 3D mechanoreceptor (SENS) capable of detecting multiple mechanical stimuli is developed to bridge sensing and action in a closed-loop sensorimotor system for dynamic haptic exploration. A tensor-based non-linear theoretical model is established to characterize the 3D deformation (e.g., tensile, compressive, and shear deformation) of SENS, providing guidance for the design and optimization of multimode sensing properties with high fidelity. Based on SENS, a closed-loop robotic system capable of recognizing objects with improved accuracy (≈96%) is further demonstrated. This dynamic haptic exploration approach shows promise for a wide range of applications such as autonomous learning, healthcare, and space and deep-sea exploration.

Soft mechanical sensors for wearable and implantable applications

Wearable and implantable sensing of biomechanical signals such as pressure, strain, shear, and vibration can enable a multitude of human-integrated applications, including on-skin monitoring of vital signs, motion tracking, monitoring of internal organ condition, restoration of lost/impaired mechanoreception, among many others. The mechanical conformability of such sensors to the human skin and tissue is critical to enhancing their biocompatibility and sensing accuracy. As such, in the recent decade, significant efforts have been made in the development of soft mechanical sensors. To satisfy the requirements of different wearable and implantable applications, such sensors have been imparted with various additional properties to make them better suited for the varied contexts of human-integrated applications. In this review, focusing on the four major types of soft mechanical sensors for pressure, strain, shear, and vibration, we discussed the recent material and device design innovations for achieving several important properties, including flexibility and stretchability, bioresorbability and biodegradability, self-healing properties, breathability, transparency, wireless communication capabilities, and high-density integration. We then went on to discuss the current research state of the use of such novel soft mechanical sensors in wearable and implantable applications, based on which future research needs were further discussed. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > Diagnostic Nanodevices Implantable Materials and Surgical Technologies > Nanomaterials and Implants.

Encoding contact size using static and dynamic electrotactile finger stimulation: natural decoding vs. trained cues

Electrotactile stimulation through matrix electrodes is a promising technology to restore high-resolution tactile feedback in extended reality applications. One of the fundamental tactile effects that should be simulated is the change in the size of the contact between the finger and a virtual object. The present study investigated how participants perceive the increase of stimulation area when stimulating the index finger using static or dynamic (moving) stimuli produced by activating 1 to 6 electrode pads. To assess the ability to interpret the stimulation from the natural cues (natural decoding), without any prior training, the participants were instructed to draw the size of the stimulated area and identify the size difference when comparing two consecutive stimulations. To investigate if other "non-natural" cues can improve the size estimation, the participants were asked to enumerate the number of active pads following a training protocol. The results demonstrated that participants could perceive the change in size without prior training (e.g., the estimated area correlated with the stimulated area, p < 0.001; ≥ two-pad difference recognized with > 80% success rate). However, natural decoding was also challenging, as the response area changed gradually and sometimes in complex patterns when increasing the number of active pads (e.g., four extra pads needed for the statistically significant difference). Nevertheless, by training the participants to utilize additional cues the limitations of natural perception could be compensated. After the training, the mismatch in the activated and estimated number of pads was less than one pad regardless of the stimulus size. Finally, introducing the movement of the stimulus substantially improved discrimination (e.g., 100% median success rate to recognize ≥ one-pad difference). The present study, therefore, provides insights into stimulation size perception, and practical guidelines on how to modulate pad activation to change the perceived size in static and dynamic scenarios.

Ultrafast piezocapacitive soft pressure sensors with over 10 kHz bandwidth via bonded microstructured interfaces

Flexible pressure sensors can convert mechanical stimuli to electrical signals to interact with the surroundings, mimicking the functionality of the human skins. Piezocapacitive pressure sensors, a class of most widely used devices for artificial skins, however, often suffer from slow response-relaxation speed (tens of milliseconds) and thus fail to detect dynamic stimuli or high-frequency vibrations. Here, we show that the contact-separation behavior of the electrode-dielectric interface is an energy dissipation process that substantially determines the response-relaxation time of the sensors. We thus reduce the response and relaxation time to ~0.04 ms using a bonded microstructured interface that effectively diminishes interfacial friction and energy dissipation. The high response-relaxation speed allows the sensor to detect vibrations over 10 kHz, which enables not only dynamic force detection, but also acoustic applications. This sensor also shows negligible hysteresis to precisely track dynamic stimuli. Our work opens a path that can substantially promote the response-relaxation speed of piezocapacitive pressure sensors into submillisecond range and extend their applications in acoustic range.

Towards high performance and durable soft tactile actuators

Soft actuators are gaining significant attention due to their ability to provide realistic tactile sensations in various applications. However, their soft nature makes them vulnerable to damage from external factors, limiting actuation stability and device lifespan. The susceptibility to damage becomes higher with these actuators often in direct contact with their surroundings to generate tactile feedback. Upon onset of damage, the stability or repeatability of the device will be undermined. Eventually, when complete failure occurs, these actuators are disposed of, accumulating waste and driving the consumption of natural resources. This emphasizes the need to enhance the durability of soft tactile actuators for continued operation. This review presents the principles of tactile feedback of actuators, followed by a discussion of the mechanisms, advancements, and challenges faced by soft tactile actuators to realize high actuation performance, categorized by their driving stimuli. Diverse approaches to achieve durability are evaluated, including self-healing, damage resistance, self-cleaning, and temperature stability for soft actuators. In these sections, current challenges and potential material designs are identified, paving the way for developing durable soft tactile actuators.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Do robots outperform humans in human-centered domains?

The incessant progress of robotic technology and rationalization of human manpower induces high expectations in society, but also resentment and even fear. In this paper, we present a quantitative normalized comparison of performance, to shine a light onto the pressing question, "How close is the current state of humanoid robotics to outperforming humans in their typical functions (e.g., locomotion, manipulation), and their underlying structures (e.g., actuators/muscles) in human-centered domains?" This is the most comprehensive comparison of the literature so far. Most state-of-the-art robotic structures required for visual, tactile, or vestibular perception outperform human structures at the cost of slightly higher mass and volume. Electromagnetic and fluidic actuation outperform human muscles w.r.t. speed, endurance, force density, and power density, excluding components for energy storage and conversion. Artificial joints and links can compete with the human skeleton. In contrast, the comparison of locomotion functions shows that robots are trailing behind in energy efficiency, operational time, and transportation costs. Robots are capable of obstacle negotiation, object manipulation, swimming, playing soccer, or vehicle operation. Despite the impressive advances of humanoid robots in the last two decades, current robots are not yet reaching the dexterity and versatility to cope with more complex manipulation and locomotion tasks (e.g., in confined spaces). We conclude that state-of-the-art humanoid robotics is far from matching the dexterity and versatility of human beings. Despite the outperforming technical structures, robot functions are inferior to human ones, even with tethered robots that could place heavy auxiliary components off-board. The persistent advances in robotics let us anticipate the diminishing of the gap.

Polysaccharides, proteins, and synthetic polymers based multimodal hydrogels for various biomedical applications: A review

Nature-derived or biologically encouraged hydrogels have attracted considerable interest in numerous biomedical applications owing to their multidimensional utility and effectiveness. The internal architecture of a hydrogel network, the chemistry of the raw materials involved, interaction across the interface of counter ions, and the ability to mimic the extracellular matrix (ECM) govern the clinical efficacy of the designed hydrogels. This review focuses on the mechanistic viewpoint of different biologically driven/inspired biomacromolecules that encourages the architectural development of hydrogel networks. In addition, the advantage of hydrogels by mimicking the ECM and the significance of the raw material selection as an indicator of bioinertness is deeply elaborated in the review. Furthermore, the article reviews and describes the application of polysaccharides, proteins, and synthetic polymer-based multimodal hydrogels inspired by or derived from nature in different biomedical areas. The review discusses the challenges and opportunities in biomaterials along with future prospects in terms of their applications in biodevices or functional components for human health issues. This review provides information on the strategy and inspiration from nature that can be used to develop a link between multimodal hydrogels as the main frame and its utility in biomedical applications as the primary target.

A mechanically validated open-source silicone model for the training of gastric perforation sewing

BACKGROUND

Gastrointestinal perforation is commonly seen in emergency departments. The perforation of the stomach is an emergency situation that requires immediate surgical treatment. The necessary surgical skills require regular practical training. Owing to patient`s safety, in vivo training opportunities in medicine are restricted. Animal tissue especially porcine tissue, is commonly used for surgical training. Due to its limiting factors, artificial training models are often to be preferred. Many artificial models are on the market but to our knowledge, none that mimic the haptic- and sewing properties of a stomach wall at the same time. In this study, an open source silicone model of a gastric perforation for training of gastric sewing was developed that attempts to provide realistic haptic- and sewing behaviour.

METHODS

To simulate the layered structure of the human stomach, different silicone materials were used to produce three different model layups. The production process was kept as simple as possible to make it easily reproducible. A needle penetration setup as well as a systematic haptic evaluation were developed to compare these silicone models to a real porcine stomach in order to identify the most realistic model.

RESULTS

A silicone model consisting of three layers was identified as being the most promising and was tested by clinical surgeons.

CONCLUSIONS

The presented model simulates the sewing characteristics of a human stomach wall, is easily reproducible at low-costs and can be used for practicing gastric suturing techniques.

TRIAL REGISTRATIONS

Not applicable.

Effects of Sensing Tactile Arrays, Shear Force, and Proprioception of Robot on Texture Recognition

In robotics, tactile perception is important for fine control using robot grippers and hands. To effectively incorporate tactile perception in robots, it is essential to understand how humans use mechanoreceptors and proprioceptors to perceive texture. Thus, our study aimed to investigate the impact of tactile sensor arrays, shear force, and the positional information of the robot's end effector on its ability to recognize texture. A deep learning network was employed to classify tactile data from 24 different textures that were explored by a robot. The input values of the deep learning network were modified based on variations in the number of channels of the tactile signal, the arrangement of the tactile sensor, the presence or absence of shear force, and the positional information of the robot. By comparing the accuracy of texture recognition, our analysis revealed that tactile sensor arrays more accurately recognized the texture compared to a single tactile sensor. The utilization of shear force and positional information of the robot resulted in an improved accuracy of texture recognition when using a single tactile sensor. Furthermore, an equal number of sensors placed in a vertical arrangement led to a more accurate distinction of textures during exploration when compared to sensors placed in a horizontal arrangement. The results of this study indicate that the implementation of a tactile sensor array should be prioritized over a single sensor for enhanced accuracy in tactile sensing, and the use of integrated data should be considered for single tactile sensing.

High-Precision 3D Reconstruction Study with Emphasis on Refractive Calibration of GelStereo-Type Sensors

GelStereo sensing technology is capable of performing three-dimensional (3D) contact shape measurement under various contact structures such as bionic curved surfaces, which has promising advantages in the field of visuotactile sensing. However, due to multi-medium ray refraction in the imaging system, robust and high-precision tactile 3D reconstruction remains a challenging problem for GelStereo-type sensors with different structures. In this paper, we first propose a universal Refractive Stereo Ray Tracing (RSRT) model for GelStereo-type sensing systems to realize 3D reconstruction of the contact surface. Moreover, a relative geometry-based optimization method is presented to calibrate multiple parameters of the proposed RSRT model, such as the refractive indices and structural dimensions. Furthermore, extensive quantitative calibration experiments are performed on four different GelStereo sensing platforms; the experimental results show that the proposed calibration pipeline can achieve less than 0.35 mm in Euclidean distance error, based on which we believe that the proposed refractive calibration method can be further applied in more complex GelStereo-type and other similar visuotactile sensing systems. Such high-precision visuotactile sensors can facilitate the study of robotic dexterous manipulation.

The 0-3 Lead Zirconate-Titanate (PZT)/Polyvinyl-Butyral (PVB) Composite for Tactile Sensing

In this study, a 0-3 piezoelectric composite based on lead zirconate-titanate (PZT)/polyvinyl-butyral (PVB) was fabricated and characterized for its potential application in tactile sensing. The 0-3 composite was developed to incorporate the advantages of both ceramic and polymer. The paste of 0-3 PZT-PVB composite was printed using a conventional screen-printing technique on alumina and mylar substrates. The thickness of the prepared composite was approximately 80 μm. After printing the top electrode of the silver paste, 10 kV/mm of DC field was applied at 25 °C, 120 °C, and 150 °C for 10 min to align the electric dipoles in the composite. The piezoelectric charge coefficient of d33 and the piezoelectric voltage coefficient of g33 were improved by increasing the temperature of the poling process. The maximum values of d33 and g33 were 14.3 pC/N and 44.2 mV·m/N, respectively, at 150 °C. The sensor's sensitivity to the impact force was measured by a ball drop test. The sensors showed a linear behavior in the output voltage with increasing impact force. The sensitivity of the sensor on the alumina and mylar substrates was 1.368 V/N and 0.815 V/N, respectively. The rising time of the sensor to the finger touch was 43 ms on the alumina substrate and 35 ms on the mylar substrate. Consequently, the high sensitivity and fast response time of the sensor make the 0-3 PZT-PVB composite a good candidate for tactile sensors.

Sensing, Actuating, and Interacting Through Passive Body Dynamics: A Framework for Soft Robotic Hand Design

Robotic hands have long strived to reach the performance of human hands. The physical complexity and extraordinary capabilities of the human hand, in terms of sensing, actuation, and cognitive abilities, make achieving this goal challenging. At the heart of the physical structure of the hand is its' passive behaviors. Seen most clearly in soft robotic hands, these behaviors influence and affect the mechanical, sensing, and control functionalities. With this perspective, we present a framework through which passivity in robot hands can be understood, by concretely identifying the role of passivity in the design, fabrication, and control of soft hands. In this framework we focus on the interactions between the physical hand and the: environment, internal actuation, sensor morphology, and wrist control. Taking these surrounding systems away, we are left with a passive soft hand whose behaviors emerge from external interactions. Inspired by the human hand, we define the role of these four key interacting pillars and review how state-of-the art robot hands utilize these four elements to aid functionality. We show how these pillars promote hybrid soft-rigid hands with rich behaviors, providing benefits in terms of the increased adaptability to uncertain environments, improved scalability and reduction in the cost of actuation, sensing, and control. This review provides a conceptual framework for approaching hand design and analysis through consideration of the passive behaviors. This highlights not only the advances that can be made by approaching the problem in this way but also the outstanding challenges that stem from this outlook.

Combining virtual reality and tactile stimulation to investigate embodied finger-based numerical representations

Finger-based representation of numbers is a high-level cognitive strategy to assist numerical and arithmetic processing in children and adults. It is unclear whether this paradigm builds on simple perceptual features or comprises several attributes through embodiment. Here we describe the development and initial testing of an experimental setup to study embodiment during a finger-based numerical task using Virtual Reality (VR) and a low-cost tactile stimulator that is easy to build. Using VR allows us to create new ways to study finger-based numerical representation using a virtual hand that can be manipulated in ways our hand cannot, such as decoupling tactile and visual stimuli. The goal is to present a new methodology that can allow researchers to study embodiment through this new approach, maybe shedding new light on the cognitive strategy behind the finger-based representation of numbers. In this case, a critical methodological requirement is delivering precisely targeted sensory stimuli to specific effectors while simultaneously recording their behavior and engaging the participant in a simulated experience. We tested the device's capability by stimulating users in different experimental configurations. Results indicate that our device delivers reliable tactile stimulation to all fingers of a participant's hand without losing motion tracking quality during an ongoing task. This is reflected by an accuracy of over 95% in participants detecting stimulation of a single finger or multiple fingers in sequential stimulation as indicated by experiments with sixteen participants. We discuss possible application scenarios, explain how to apply our methodology to study the embodiment of finger-based numerical representations and other high-level cognitive functions, and discuss potential further developments of the device based on the data obtained in our testing.

Clinical Assessment of Mechanical Allodynia in Youth With Complex Regional Pain Syndrome: Development and Preliminary Validation of the Pediatric Tactile Sensitivity Test of Allodynia (Pedi-Sense)

Youth with complex regional pain syndrome (CRPS) commonly experience mechanical allodynia and disability. Assessment of mechanical allodynia is typically binary (present or absent), making it difficult to assess the quality and degree of mechanical allodynia before and after treatment. This study developed and validated the Pediatric Tactile Sensitivity Test of Allodynia (Pedi-Sense) to provide an easy way for rehabilitation clinicians to evaluate mechanical allodynia before and after intensive interdisciplinary pain treatment. The 6 Pedi-Sense items demonstrated adequate internal consistency reliability (CR) at admission (CR = .956) and discharge (CR = .973), reasonably fit the hypothesized linear model of stimulus intensity (P < .0001), and significantly loaded onto a single latent factor, mechanical allodynia (P < .0001), at admission and discharge. Pedi-Sense scores significantly correlated with disability (r_s = .40; P = .004) and pain catastrophizing (r_s = .33; P = .017) at admission. The Pedi-Sense appeared responsive to intervention as participants' total scores improved by 1.44 points (95% CI: .72, 2.15) after IIPT interventions that included daily tactile desensitization. However, test-retest and interrater reliability and the specific contribution of desensitization treatment to the overall success of multi-modal pain rehabilitation still needs to be evaluated. PERSPECTIVE: This article presents the development and preliminary validation of a novel clinical assessment of static and dynamic mechanical allodynia. The Pediatric Tactile Sensitivity Test of Allodynia (Pedi-Sense) allows rehabilitation clinicians to easily evaluate mechanical allodynia at the bedside with minimal training and simple equipment to guide desensitization treatment in clinical settings.

BaroTac: Barometric Three-Axis Tactile Sensor with Slip Detection Capability

Tactile sensors for robotic applications enhance the performance of robotic end-effectors as they ca n provide tactile information to operate various tasks. In particular, tactile sensors can measure multi-axial force and detect slip can aid the end-effectors in grasping diverse objects in an unstructured environment. We propose BaroTac, which measures three-axial forces and detects slip with a barometric pressure sensor chip (BPSC) for robotic applications. A BPSC is an off-the-shelf commercial sensor that is inexpensive, easy to customize, robust, and simple to use. While a single BPSC-based tactile sensor can measure pressure, an array of BPSC-based tactile sensors can measure multi-axial force through the reactivity of each sensor and detect slip by observing high frequency due to slip vibration. We first experiment with defining the fundamental characteristics of a single-cell BPSC-based sensor to set the design parameters of our proposed sensor. Thereafter, we suggest the sensing method of BaroTac: calibration matrix for three-axis force measurement and discrete wavelet transform (DWT) for slip detection. Subsequently, we validate the three-axis force measuring ability and slip detectability of the fabricated multi-cell BPSC-based tactile sensor. The sensor measures three-axis force with low error (0.14, 0.18, and 0.3% in the X-, Y- and Z-axis, respectively) and discriminates slip in the high-frequency range (75-150 Hz). We finally show the practical applicability of BaroTac by installing them on the commercial robotic gripper and controlling the gripper to grasp common objects based on our sensor feedback.

Nonpatterned Soft Piezoresistive Films with Filamentous Conduction Paths for Mimicking Multiple-Resolution Receptors of Human Skin

Soft pressure sensors play key roles as input devices of electronic skin (E-skin) to imitate real human skin. For efficient data acquisition according to stimulus types such as detailed pressure images or macroscopic strength of stimuli, soft pressure sensors can have variable spatial resolution, just like the uneven spatial distribution of pressure-sensing receptors on the human body. However, previous methods on soft pressure sensors cannot achieve such tunability of spatial resolution because their sensor materials and read-out electrodes need to be elaborately patterned for a specific sensor density. Here, we report a universal soft pressure-sensitive platform based on anisotropically self-assembled ferromagnetic particles embedded in elastomer matrices whose spatial resolution can be facilely tuned. Various spatial densities of pressure-sensing receptors of human body parts can be implemented by simply sandwiching the film between soft electrodes with different pitches. Since the anisotropically aligned nickel particles form independent filamentous conductive paths, the pressure sensors show spatial sensing ability without crosstalk, whose spatial resolution up to 100 dpi can be achieved from a single platform. The sensor array shows a wide dynamic range capable of detecting various pressure levels, such as liquid drops (∼30 Pa) and plantar (∼300 kPa) pressures. Our universal soft pressure-sensing platform would be a key enabling technology for actually imitating the receptor systems of human skin in robot and biomedical applications.

A Wearable Textile-Embedded Dielectric Elastomer Actuator Haptic Display

With advances in mobile computing and virtual/augmented reality technologies, communicating through touch using wearable haptic devices is poised to enrich and augment current information delivery channels that typically rely on sight and hearing. To realize a wearable haptic device capable of effective data communication, both ergonomics and haptic performance (i.e., array size, bandwidth, and perception accuracy) are essential considerations. However, these goals often involve challenging and conflicting requirements. We present an integrated approach to address these conflicts, which includes incorporating multilayered dielectric elastomer actuators, a lumped-parameter model of the skin, and a wearable frame in the design loop. An antagonistic arrangement-consisting of an actuator deforming the skin-was used to achieve effective force transmission while maintaining a low profile, and the effect of the wearable frame and structure was investigated through lumped-model analysis and human perception studies.

MEMS-Based Tactile Sensors: Materials, Processes and Applications in Robotics

Commonly encountered problems in the manipulation of objects with robotic hands are the contact force control and the setting of approaching motion. Microelectromechanical systems (MEMS) sensors on robots offer several solutions to these problems along with new capabilities. In this review, we analyze tactile, force and/or pressure sensors produced by MEMS technologies including off-the-shelf products such as MEMS barometric sensors. Alone or in conjunction with other sensors, MEMS platforms are considered very promising for robots to detect the contact forces, slippage and the distance to the objects for effective dexterous manipulation. We briefly reviewed several sensing mechanisms and principles, such as capacitive, resistive, piezoresistive and triboelectric, combined with new flexible materials technologies including polymers processing and MEMS-embedded textiles for flexible and snake robots. We demonstrated that without taking up extra space and at the same time remaining lightweight, several MEMS sensors can be integrated into robotic hands to simulate human fingers, gripping, hardness and stiffness sensations. MEMS have high potential of enabling new generation microactuators, microsensors, micro miniature motion-systems (e.g., microrobots) that will be indispensable for health, security, safety and environmental protection.

Fabrication and Characterization of a Highly Sensitive and Flexible Tactile Sensor Based on Indium Zinc Oxide (IZO) with Imprecise Data Analysis

Tactile sensors are widely used in the electronic industry. In the following research work, we proposed a tactile sensor based on indium zinc oxide (IZO) electrodes and used neutrosophic statistics to analyze the capacitance and resistance of the tactile sensor. The tactile sensor was fabricated by depositing the IZO electrodes on a polycarbonate substrate (a thin layer). The IZO was characterized through X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and ultraviolet-visible (UV-vis) spectroscopy techniques. The sensor's electrical properties were characterized using an LCR meter, i.e., capacitance and resistance were measured in intervals with respect to changes in the applied force on the sensor at 1 kHz operational frequency. The sensor expressed high sensitivity with quick response and recovery times. The sensor also expressed long-term stability. For the analysis of capacitance and resistance, two statistical approaches, i.e., classical and neutrosophic approaches, were applied, and the better analysis approach for the sensor was found.

Partial Unweighting in Obese Persons Enhances Tactile Transmission From the Periphery to Cortical Areas: Impact on Postural Adjustments

Tactile plantar information is known to play an important role in balance maintenance and to contribute to the setting of anticipatory postural adjustments (APAs) prior to stepping. Previous studies have suggested that somatosensory processes do not function optimally for obese individuals due to the increased pressure of the plantar sole resulting in balance issues. Here, we investigated whether decreasing the compression of the mechanoreceptors by unweighting the plantar sole would enhance tactile sensory processes leading to an increased stability and an accurate setting of the APAs in obese individuals. More specifically, we tested the hypothesis that the somatosensory cortex response to electric stimulation (SEP) of the plantar sole in standing obese persons will be greater with reduced body weight than with their effective weight. The level of unweighting was calculated for each participant to correspond to a healthy body mass index. We showed an increase SEP amplitude in the unweighted condition compared to the effective body weight for all participants. This increase can be explained by the reduction of weight itself but also by the modified distribution of the pressure exerted onto the foot sole. Indeed, in the unweighted condition, the vertical ground reaction forces are evenly distributed over the surface of the foot. This suggests that decreasing and equalizing the pressure applied on the plantar mechanoreceptors results in an increase in somatosensory transmission and sensory processes for obese persons when unweighted. These sensory processes are crucial prior to step initiation and for setting the anticipatory postural adjustments (i.e., thrust). These cortical changes could have contributed to the observed changes in the spatiotemporal characteristics of the thrust prior to step initiation.

Machine-Learning-Assisted Recognition on Bioinspired Soft Sensor Arrays

Soft interfaces with self-sensing capabilities play an essential role in environment awareness and reaction. The growing overlap between materials and sensory systems has created a myriad of challenges for sensor integration, including the design of a multimodal sensory, simplified system design capable of high spatiotemporal sensing resolution and efficient processing methods. Here we report a bioinspired soft sensor array (BOSSA) that integrates pressure and material sensing capabilities based on the triboelectric effect. Cascaded row + column electrodes embedded in low-modulus porous silicone rubber allow rich information to be captured from the environment and further analyzed by data-driven algorithms (multilayer perceptrons) to extract higher level features. BOSSA demonstrates the ability to identify 10 users (98.9%) and identify the placement or extraction of 10 objects (98.6%). Moreover, its scalable fabrication facilitates large-area sensor arrays with high spatiotemporal resolution and multimodal sensing abilities yet with a less complex system architecture. These features may be promising in the development of immersive sensing networks for intelligent monitoring and stimuli response in smart home/industry applications.

Sensation to navigation: a computational neuroscience approach to magnetic field navigation

Diverse taxa use Earth's magnetic field (i.e., magnetoreception) as a guide during long-distance navigation. However, despite decades of research, specific sensory mechanisms of magnetoreception remain unconfirmed. Necessarily, this has led to theoretical and computational work developing hypotheses of how animals may navigate using magnetoreception. One hypothesized strategy relies on an animal using combinations of magnetic intensity and inclination as a kind of signature to identify a specific region or location. Using these signatures, animals could use a waypoint-based navigation strategy. We show that this navigation strategy is biologically plausible using a close approximation of neural processing to successfully guide an agent in a simulated magnetic field. Moreover, we accomplish this strategy using a processing approach previously utilized for mechanoreception, suggesting processing of Earth's magnetic field may share features with the processing of other, more well-understood sensory systems. Taken together, our results suggest that both for the engineering of novel navigation systems and the study of animal magnetoreception, we should take lessons from other sensory systems.

A Dielectric Elastomer-Based Multimodal Capacitive Sensor

Dielectric elastomer (DE) sensors have been widely used in a wide variety of applications, such as in robotic hands, wearable sensors, rehabilitation devices, etc. A unique dielectric elastomer-based multimodal capacitive sensor has been developed to quantify the pressure and the location of any touch simultaneously. This multimodal sensor is a soft, flexible, and stretchable dielectric elastomer (DE) capacitive pressure mat that is composed of a multi-layer soft and stretchy DE sensor. The top layer measures the applied pressure, while the underlying sensor array enables location identification. The sensor is placed on a passive elastomeric substrate in order to increase deformation and optimize the sensor's sensitivity. This DE multimodal capacitive sensor, with pressure and localization capability, paves the way for further development with potential applications in bio-mechatronics technology and other humanoid devices. The sensor design could be useful for robotic and other applications, such as fruit picking or as a bio-instrument for the diabetic insole.

Recent Development of Flexible Tactile Sensors and Their Applications

With the rapid development of society in recent decades, the wearable sensor has attracted attention for motion-based health care and artificial applications. However, there are still many limitations to applying them in real life, particularly the inconvenience that comes from their large size and non-flexible systems. To solve these problems, flexible small-sized sensors that use body motion as a stimulus are studied to directly collect more accurate and diverse signals. In particular, tactile sensors are applied directly on the skin and provide input signals of motion change for the flexible reading device. This review provides information about different types of tactile sensors and their working mechanisms that are piezoresistive, piezocapacitive, piezoelectric, and triboelectric. Moreover, this review presents not only the applications of the tactile sensor in motion sensing and health care monitoring, but also their contributions in the field of artificial intelligence in recent years. Other applications, such as human behavior studies, are also suggested.

On the Efficiency of Haptic Based Object Identification: Determining Where to Grasp to Get the Most Distinguishing Information

Haptic perception is one of the key modalities in obtaining physical information of objects and in object identification. Most existing literature focused on improving the accuracy of identification algorithms with less attention paid to the efficiency. This work aims to investigate the efficiency of haptic object identification to reduce the number of grasps required to correctly identify an object out of a given object set. Thus, in a case where multiple grasps are required to characterise an object, the proposed algorithm seeks to determine where the next grasp should be on the object to obtain the most amount of distinguishing information. As such, the paper proposes the construction of the object description that preserves the association of the spatial information and the haptic information on the object. A clustering technique is employed both to construct the description of the object in a data set and for the identification process. An information gain (IG) based method is then employed to determine which pose would yield the most distinguishing information among the remaining possible candidates in the object set to improve the efficiency of the identification process. This proposed algorithm is validated experimentally. A Reflex TakkTile robotic hand with integrated joint displacement and tactile sensors is used to perform both the data collection for the dataset and the object identification procedure. The proposed IG approach was found to require a significantly lower number of grasps to identify the objects compared to a baseline approach where the decision was made by random choice of grasps.

A Frequency Modulation-Based Taxel Array: A Bio-Inspired Architecture for Large-Scale Artificial Skin

This work introduces an array prototype based on a Frequency Modulation (FM) encoding architecture to transfer multiple sensor signals on a single wire. The use case presented adopts Hall-effect sensors as an example to represent a much larger range of sensor types (e.g., proximity and temperature). This work aims to contribute to large area artificial skin systems which are a key element to enhance robotic platforms. Artificial skin will allow robotic platforms to have spatial awareness which will make interaction with objects and users safe. The FM-based architecture has been developed to address limitations in large-scale artificial skin scalability. Scalability issues include power requirements; number of wires needed; as well as frequency, density, and sensitivity bottlenecks. In this work, eight sensor signals are simultaneously acquired, transferred on a single wire and decoded in real-time. The overall taxel array current consumption is 36 mA. The work experimentally validates and demonstrates that different input signals can be effectively transferred using this approach minimizing wiring and power consumption of the taxel array. Four different tests using single as well as multiple stimuli are presented. Observations on performances, noise, and taxel array behaviour are reported. The results show that the taxel array is reliable and effective in detecting the applied stimuli.

Electrotactile Feedback for the Discrimination of Different Surface Textures Using a Microphone

Most commercial prosthetic hands lack closed-loop feedback, thus, a lot of research has been focusing on implementing sensory feedback systems to provide the user with sensory information during activities of daily living. This study evaluates the possibilities of using a microphone and electrotactile feedback to identify different textures. A condenser microphone was used as a sensor to detect the friction sound generated from the contact between different textures and the microphone. The generated signal was processed to provide a characteristic electrical stimulation presented to the participants. The main goal of the processing was to derive a continuous and intuitive transfer function between the microphone signal and stimulation frequency. Twelve able-bodied volunteers participated in the study, in which they were asked to identify the stroked texture (among four used in this study: Felt, sponge, silicone rubber, and string mesh) using only electrotactile feedback. The experiments were done in three phases: (1) Training, (2) with-feedback, (3) without-feedback. Each texture was stroked 20 times each during all three phases. The results show that the participants were able to differentiate between different textures, with a median accuracy of 85%, by using only electrotactile feedback with the stimulation frequency being the only variable parameter.

Artificial Skin Perception

Skin is the largest organ, with the functionalities of protection, regulation, and sensation. The emulation of human skin via flexible and stretchable electronics gives rise to electronic skin (e-skin), which has realized artificial sensation and other functions that cannot be achieved by conventional electronics. To date, tremendous progress has been made in data acquisition and transmission for e-skin systems, while the implementation of perception within systems, that is, sensory data processing, is still in its infancy. Integrating the perception functionality into a flexible and stretchable sensing system, namely artificial skin perception, is critical to endow current e-skin systems with higher intelligence. Here, recent progress in the design and fabrication of artificial skin perception devices and systems is summarized, and challenges and prospects are discussed. The strategies for implementing artificial skin perception utilize either conventional silicon-based circuits or novel flexible computing devices such as memristive devices and synaptic transistors, which enable artificial skin to surpass human skin, with a distributed, low-latency, and energy-efficient information-processing ability. In future, artificial skin perception would be a new enabling technology to construct next-generation intelligent electronic devices and systems for advanced applications, such as robotic surgery, rehabilitation, and prosthetics.

Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition

As a surrogate for human tactile cognition, an artificial tactile perception and cognition system are proposed to produce smooth/soft and rough tactile sensations by its user's tactile feeling; and named this system as "tactile avatar". A piezoelectric tactile sensor is developed to record dynamically various physical information such as pressure, temperature, hardness, sliding velocity, and surface topography. For artificial tactile cognition, the tactile feeling of humans to various tactile materials ranging from smooth/soft to rough are assessed and found variation among participants. Because tactile responses vary among humans, a deep learning structure is designed to allow personalization through training based on individualized histograms of human tactile cognition and recording physical tactile information. The decision error in each avatar system is less than 2% when 42 materials are used to measure the tactile data with 100 trials for each material under 1.2N of contact force with 4cm s-1 of sliding velocity. As a tactile avatar, the machine categorizes newly experienced materials based on the tactile knowledge obtained from training data. The tactile sensation showed a high correlation with the specific user's tendency. This approach can be applied to electronic devices with tactile emotional exchange capabilities, as well as advanced digital experiences.

Electric-Field-Induced Gradient Ionogels for Highly Sensitive, Broad-Range-Response, and Freeze/Heat-Resistant Ionic Fingers

Human fingers exhibit both high sensitivity and wide tactile range. The finger skin structures are designed to display gradient microstructures and compressibility. Inspired by the gradient mechanical Young's modulus distribution, an electric-field-induced cationic crosslinker migration strategy is demonstrated to prepare gradient ionogels. Due to the gradient of the crosslinkers, the ionogels exhibit more than four orders of magnitude difference between the anode and the cathode side, enabling gradient ionogel-based flexible iontronic sensors having high-sensitivity and broader-range detection (from 3 × 10² to 2.5 × 10⁶  Pa) simultaneously. Moreover, owing to the remarkable properties of the gradient ionogels, the flexible iontronic sensors also show good long-time stability (even after 10 000 cycles loadings) and excellent performance over a wide temperature range (from -108 to 300 °C). The flexible iontronic sensors are further integrated on soft grips, exhibiting remarkable performance under various conditions. These attractive features demonstrate that gradient ionogels will be promising candidates for smart sensor applications in complex and extreme conditions.

Skin-inspired quadruple tactile sensors integrated on a robot hand enable object recognition

Robot hands with tactile perception can improve the safety of object manipulation and also improve the accuracy of object identification. Here, we report the integration of quadruple tactile sensors onto a robot hand to enable precise object recognition through grasping. Our quadruple tactile sensor consists of a skin-inspired multilayer microstructure. It works as thermoreceptor with the ability to perceive thermal conductivity of a material, measure contact pressure, as well as sense object temperature and environment temperature simultaneously and independently. By combining tactile sensing information and machine learning, our smart hand has the capability to precisely recognize different shapes, sizes, and materials in a diverse set of objects. We further apply our smart hand to the task of garbage sorting and demonstrate a classification accuracy of 94% in recognizing seven types of garbage.

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.

Study of the Human Stickiness Perception of Wet Fabric on the Volar Forearm via Two Contact Modes: Friction and Adhesion-Separation

Wet fabric clinging to human skin may cause stickiness perception and facilitate wearing discomfort. This study aims to investigate the stickiness perception of wet fabrics via two contact modes (friction and adhesion-separation) as well as the influences of fabric properties and contact features on stickiness perception. A set of dynamic contact devices was developed to drive the samples to move horizontally or vertically on volar forearms. According to the magnitude estimation approach, 12 subjects were asked to assess the stickiness perceptions caused by 64 types of contact stimuli. The results showed that the water absorption capacity had a significant and negative correlation with stickiness perception under the two contact modes. However, the surface roughness was neither related to stickiness perception under the friction mode nor to stickiness perception under the adhesion-separation mode. There was an interaction between contact pressure and sliding velocity. The sliding velocity at medium or high pressure had a significant effect on stickiness perception, but the separating velocity had no significant effect. Stickiness perception under the friction mode is higher than that under the adhesion-separation mode in most cases. Consequently, the friction mode was considered the main contact mode that evokes stickiness perception.

Artificially Intelligent Tactile Ferroelectric Skin

Lightweight and flexible tactile learning machines can simultaneously detect, synaptically memorize, and subsequently learn from external stimuli acquired from the skin. This type of technology holds great interest due to its potential applications in emerging wearable and human-interactive artificially intelligent neuromorphic electronics. In this study, an integrated artificially intelligent tactile learning electronic skin (e-skin) based on arrays of ferroelectric-gate field-effect transistors with dome-shape tactile top-gates, which can simultaneously sense and learn from a variety of tactile information, is introduced. To test the e-skin, tactile pressure is applied to a dome-shaped top-gate that measures ferroelectric remnant polarization in a gate insulator. This results in analog conductance modulation that is dependent upon both the number and magnitude of input pressure-spikes, thus mimicking diverse tactile and essential synaptic functions. Specifically, the device exhibits excellent cycling stability between long-term potentiation and depression over the course of 10 000 continuous input pulses. Additionally, it has a low variability of only 3.18%, resulting in high-performance and robust tactile perception learning. The 4 × 4  device array is also able to recognize different handwritten patterns using 2-dimensional spatial learning and recognition, and this is successfully demonstrated with a high degree accuracy of 99.66%, even after considering 10% noise.

A Highly Sensitive, Reliable, and High-Temperature-Resistant Flexible Pressure Sensor Based on Ceramic Nanofibers

Flexible pressure sensors are essential components for soft electronics by providing physiological monitoring capability for wearables and tactile perceptions for soft robotics. Flexible pressure sensors with reliable performance are highly desired yet challenging to construct to meet the requirements of practical applications in daily activities and even harsh environments, such as high temperatures. This work describes a highly sensitive and reliable capacitive pressure sensor based on flexible ceramic nanofibrous networks with high structural elasticity, which minimizes performance degradation commonly seen in polymer-based sensors because of the viscoelastic behavior of polymers. Such ceramic pressure sensors exhibit high sensitivity (≈4.4 kPa-1), ultralow limit of detection (<0.8 Pa), fast response speed (<16 ms) as well as low fatigue over 50 000 loading/unloading cycles. The high stability is attributed to the excellent mechanical stability of the ceramic nanofibrous network. By employing textile-based electrodes, a fully breathable and wearable ceramic pressure sensor is demonstrated for real-time health monitoring and motion detection. Owing to the high-temperature resistance of ceramics, the ceramic nanofibrous network sensor can function properly at temperatures up to 370 °C, showing great promise for harsh environment applications.

Highly Robust and Self-Powered Electronic Skin Based on Tough Conductive Self-Healing Elastomer

Self-powered electronic skin (E-skin) can be endowed with high robustness by employing self-healing materials. However, most self-powered E-skin employs two heterogeneous materials with high modulus mismatch at the interface and poor fully self-healing ability, which reduces the robustness of the whole device. Here, a conductive polyurethane elastomer (PUE) with excellent mechanical toughness and self-healing ability is prepared. Based on the self-healing insulated/conductive PUE homogeneous structure and triboelectric-electrostatic induction effect, a highly robust and self-powered E-skin (HRSE-skin) is developed. The HRSE-skin possesses stable mechanosensation capability during the 50% stretching deformation due to a low modulus mismatch in the homogeneous structure. In addition, the stretchability and mechanosensation capability of the HRSE-skin can be restored after the fracture owing to the fully self-healing ability of the homogeneous structure. Therefore, the HRSE-skin has high robustness of the whole device including stable service behaviors and excellent restorability. The developed HRSE-skin demonstrates high robustness in the detection of the force and bending angle of the prosthetic joint. This work solves the low robustness of self-powered E-skin by the preparation of conductive self-healing PUE and the construction of the homogeneous structure, which is important for the practical applications of self-powered E-skin in prosthetic limbs and advanced robotics.

Seedless Hydrothermal Growth of ZnO Nanorods as a Promising Route for Flexible Tactile Sensors

Hydrothermal growth of ZnO nanorods has been widely used for the development of tactile sensors, with the aid of ZnO seed layers, favoring the growth of dense and vertically aligned nanorods. However, seed layers represent an additional fabrication step in the sensor design. In this study, a seedless hydrothermal growth of ZnO nanorods was carried out on Au-coated Si and polyimide substrates. The effects of both the Au morphology and the growth temperature on the characteristics of the nanorods were investigated, finding that smaller Au grains produced tilted rods, while larger grains provided vertical rods. Highly dense and high-aspect-ratio nanorods with hexagonal prismatic shape were obtained at 75 °C and 85 °C, while pyramid-like rods were grown when the temperature was set to 95 °C. Finite-element simulations demonstrated that prismatic rods produce higher voltage responses than the pyramid-shaped ones. A tactile sensor, with an active area of 1 cm², was fabricated on flexible polyimide substrate and embedding the nanorods forest in a polydimethylsiloxane matrix as a separation layer between the bottom and the top Au electrodes. The prototype showed clear responses upon applied loads of 2-4 N and vibrations over frequencies in the range of 20-800 Hz.

Laser-Printed, Flexible Graphene Pressure Sensors

While the outstanding properties of graphene have attracted a lot of attention, one of the major bottlenecks of its widespread usage is its availability in large volumes. Laser printing graphene on polyimide films is an efficient single-step fabrication process that can remedy this issue. A laser-printed, flexible pressure sensor is developed utilizing the piezoresistive effect of 3D porous graphene. The pressure sensors performance can be easily adjusted via the geometrical parameters. They have a sensitivity in the range of 1.23 × 10-3 kPa and feature a high resolution with a detection limit of 10 Pa in combination with an extremely wide dynamic range of at least 20 MPa. They also provide excellent long-term stability of at least 15 000 cycles. The biocompatibility of laser-induced graphene is also evaluated by cytotoxicity assays and fluorescent staining, which show an insignificant drop in viability. Polymethyl methacrylate coating is particularly useful for underwater applications, protecting the sensors from biofouling and shunt currents, and enable operation at a depth of 2 km in highly saline Red Sea water. Due to its features, the sensors are a prime choice for multiple healthcare applications; for example, they are used for heart rate monitoring, plantar pressure measurements, and tactile sensing.

A Face-Shear Mode Piezoelectric Array Sensor for Elasticity and Force Measurement

We present the development of a 6 × 6 piezoelectric array sensor for measuring elasticity and force. The proposed sensor employs an impedance measurement technique, sensing the acoustic load impedance of a target by measuring the electrical impedance shift of face-shear mode PMN-PT (lead magnesium niobate-lead titanate) single crystal elements. Among various modes of PMN-PT single crystals, the face-shear mode was selected due to its especially high sensitivity to acoustic loads. To verify the elasticity sensing performance, gelatin samples with different elastic moduli were prepared and tested. For the force measurement test, different magnitudes of force were loaded to the sensing layer whose acoustic impedance was varied with applied forces. From the experimental results, the fabricated sensor showed an elastic stiffness sensitivity of 23.52 Ohm/MPa with a resolution of 4.25 kPa and contact force sensitivity of 19.27 Ohm/N with a resolution of 5.19 mN. In addition, the mapping experiment of elasticity and force using the sensor array was successfully demonstrated.

Conductive Self-Healing Nanocomposite Hydrogel Skin Sensors with Antifreezing and Thermoresponsive Properties

With growing interest in flexible and wearable devices, the demand for nature-inspired soft smart materials, especially intelligent hydrogels with multiple perceptions toward external strain and temperatures to mimic the human skin, is on the rise. However, simultaneous achievement of intelligent hydrogels with skin-compatible performances, including good transparency, appropriate mechanical properties, autonomous self-healing ability, multiple mechanical/thermoresponsiveness, and retaining flexibility at subzero temperatures, is still challenging and thus limits their application as skinlike devices. Here, conductive nanocomposite hydrogels (NC gels) were delicately designed and prepared via gelation of oligo(ethylene glycol) methacrylate (OEGMA)-based monomers in a glycerol-water cosolvent, where inorganic clay served as the physical cross-linker and provided conductive ions. The resultant NC gels exhibited good conductivity (∼3.32 × 10^-4 S cm^-1, akin to biological muscle tissue) and an autonomously self-healing capacity (healing efficiency reached 84.8%). Additionally, such NC gels displayed excellent flexibility and responded well to multiple strain/temperature external stimuli and subtle human motions in a wide temperature range (from -20 to 45 °C). These distinguished properties would endow such NC gels significant applications in fields of biosensors, human-machine interfaces, and soft robotics.

Recent progress in tactile sensors and their applications in intelligent systems

With the rapid development of intelligent technology, tactile sensors as sensing devices constitute the core foundation of intelligent systems. Biological organs that can sense various stimuli play vital roles in the interaction between human beings and the external environment. Inspired by this fact, research on skin-like tactile sensors with multifunctionality and high performance has attracted extensive attention. An overview of the development of high-performance tactile sensors applied in intelligent systems is systematically presented. First, the development of tactile sensors endowed with stretchability, self-healing, biodegradability, high resolution and self-powered capability is discussed. Then, for intelligent systems, tactile sensors with excellent application prospects in many fields, such as wearable devices, medical treatment, artificial limbs and robotics, are presented. Finally, the future prospects of tactile sensors for intelligent systems are discussed.

Capturing the dynamics of peripersonal space by integrating expectancy effects and sound propagation properties

BACKGROUND

Humans perceive near space and far space differently. Peripersonal space (PPS), i.e. the space directly surrounding the body, is often studied using paradigms based on audiotactile integration. In these paradigms, reaction time (RT) to a tactile stimulus is measured in the presence of a concurrent auditory looming stimulus.

NEW METHOD

We propose here to refine the experimental procedure by disentangling behavioral contributions of the targeted audiotactile integration mechanisms from expectancy effects. To this aim, we added to the protocol a baseline with a fixed sound distance. Furthermore, in order to improve the relevance of the audiotactile integration measures, we took into account sound propagation properties and assessed RTs for logarithmically spaced auditory distances.

RESULTS

Expectation contributed significantly to overall behavioral responses. Subtracting it isolated the audiotactile effect due to the stimulus proximity. This revealed that audiotactile integration effects have to be tested on a logarithmic scale of distances, and that they follow a linear variation on this scale.

COMPARISON WITH EXISTING METHOD(S)

The current method allows cleaner and more pertinent sampling measures for evaluating audiotactile integration phenomena linked to PPS. Furthermore, most of the existing methods propose a sigmoid fitting, which rests on the intuitive framework that PPS is an in-or-out zone. Our results suggest that behavioral effects follow a logarithmic decrease, thus a response graduated in space.

CONCLUSIONS

The proposed protocol design and method of analysis contribute to sharpen the experimental investigation of the factors influencing and modifying multisensory integration phenomena in the space surrounding the body.

Development of Phantom Material that Resembles Compression Properties of Human Brain Tissue for Training Models

There is a need to quantify and reproduce the mechanical behavior of brain tissue for a variety of applications from designing proper training models for surgeons to enabling research on the effectiveness of personal protective gear, such as football helmets. The mechanical response of several candidate phantom materials, including hydrogels and emulsions, was characterized and compared to porcine brain tissue under similar strains and strain rates. Some candidate materials were selected since their compositions were similar to brain tissue, such as emulsions that mimic the high content of lipids. Others, like silicone, were included since these are currently used as phantom materials. The mechanical response of the emulsion was closer to that of the native porcine brain tissue than the other candidates. The emulsions, created by addition of oil to a hydrogel, were able to withstand compressive strain greater than 40%. The addition of lipids in the emulsions also prevented the syneresis typically seen with hydrogel materials. This allowed the emulsion material to undergo freeze-thaw cycles with no significant change in their mechanical properties.

From finger friction to brain activation: Tactile perception of the roughness of gratings

The formation of tactile perception is related to skin receptors and the cerebral cortex. In order to systematically study the tactile perception from finger friction to the brain response, a 32-channel Brain Products system and two tri-axial force sensors were used to obtain electroencephalograph (EEG) and friction signals during fingers exploring grating surfaces. A finite element finger model was established to analyze the stress changes of the skin receptors during tactile perception. Samples with different grating widths and spaces were chosen. The results indicated that different gratings induced different stress concentrations within skin that stimulated Meissner and Merkel receptors. Skin friction was affected by gratings during the tactile perception. It was also found that P300 evoked by gratings was related with the skin deformation, contact area, friction force, and stress around cutaneous mechanoreceptors. The wider grating width generated larger skin deformation, friction force, and stress, which induced stronger tactile stimulation. The smaller grating spacing generated higher vibration frequency, inducing stronger tactile stimulation. The latency of the P300 peak was related to the difference between the textured target stimulus and the smooth non-target stimulus. This study proofed that there was a relationship between the activation in brain regions, surface friction, and contact conditions of skin during the tactile perception. It contributes to understanding the formation process and cognitive mechanism of tactile perception of different surface textures.