Soft Nanocomposite Based Multi-point, Multi-directional Strain Mapping Sensor Using Anisotropic Electrical Impedance Tomography

Wide-Range, Washable Piezoresistive Pressure Sensor Based on MCNT-PDMS Dip-Coated PDMS Sponge

Flexible pressure sensors have great potential for wearable applications such as human health monitoring and human-computer interaction, which require different trade-offs between the sensitivity and operating range. However, preparing washable and wide-range piezoresistive pressure sensors remains a great challenge. Here, we developed a porous flexible elastomer sponge based on a carbon nanotube composite network coating for pressure sensors with extremely high stability and washability over a wide range. Specifically, a sugar template was used to fabricate a homogeneous macroporous PDMS sponge as a substrate, and a dip-coated MCNT-PDMS composite was used as a conductive layer. The high degree of adhesion formed between the substrate and the conductive layer resulted in a sponge with greatly enhanced mechanical properties and stability, while improving the operating range. The pressure sensors exhibited a broad operating range of 0-650 kPa, demonstrating excellent sensitivity (0.0049 kPa-1 in the range of 0-74 kPa, 0.0010 kPa-1 in the range of 74-310 kPa, and 0.0004 kPa-1 in the range of 310-650 kPa), as well as a fast response time of 143 ms and recovery time of 73 ms, long-term cycling stability of over 10,000 cycles, and excellent washable stability. Finally, we demonstrate that the sensors can be applied to gesture monitoring, human motion gait monitoring, and cycling pressure monitoring.

Advanced Flexible Physical Sensors with Independent Detection Mechanisms of Pressure and Strain Stimuli for Overcoming Signal Interference

Flexible and wearable physical sensors have gained significant interest owing to their potential in attachable devices, electronic skin, and multipurpose sensors. The physical stimuli of these sensors typically consist of vertically and horizontally applied pressures and strains, respectively. However, owing to their similar response characteristics, interference occurs between the two types of signals detected, complicating the distinction between pressure and strain stimuli, leading to inaccurate data interpretation and reduced sensor specificity. Therefore, we developed a dual-sensing-mode physical sensor with separate response mechanisms for the two types of physical stimuli based on a unique structural design that can independently induce changes in the piezocapacitance and piezoresistance for pressure and strain stimuli, respectively. The asterisk-shaped piezoresistive pathway (electrode), designed for multifunctionality, effectively detected the intensity and direction of tensile deformation, and an elastomeric sponge structure positioned between the two electrodes detected the pressure signals via changes in capacitance. This dual-sensing-mode sensor offers clearer signal differentiation and enhanced multifunctionality compared to those of traditional single-mode sensors. Additionally, extensive experimentation demonstrated that our sensor has a good sensitivity, high linearity, and stability in detecting signals, proving its applicability for sophisticated monitoring and control tasks that require the differential detection between pressure and deformation signals.

Topologically Engineered Strain Redistribution in Elastomeric Substrates for Dually Tunable Anisotropic Plasmomechanical Responses

The prompt visual response is considered to be a highly intuitive tenet among sensors. Therefore, plasmomechanical strain sensors, which exhibit dynamic structural color changes, have recently been developed by using mechanical stimulus-based elastomeric substrates for wearable sensors. However, the reported plasmomechanical strain sensors either lack directional sensitivity or require complex signal processing and device design strategies to ensure anisotropic optical responses. To the best of our knowledge, there have been no reports on utilizing anisotropic mechanical substrates to obtain directional optical responses. Herein, we propose an anisotropic plasmomechanical sensor to distinguish between the applied force direction and the force magnitude. We employ a simple strain-engineered topological elastomer to mechanically transform closely packed metallic nanoparticles (NPs) into anisotropic directional rearrangements depending on the applied force direction. The proposed structure consists of a heterogeneous-modulus elastomer that exhibits a highly direction-dependent Poisson effect owing to the periodically line-patterned local strain redistribution occurring due to the same magnitude of applied external force. Consequently, the reorientation of the self-assembled gold (Au)-NP array manifests dual anisotropy, i.e., force- and polarization-direction-dependent plasmonic coupling. The cost-effectiveness and simple design of our proposed heterogeneous-modulus platform pave the way for numerous optical applications based on dynamic transformation and topological inhomogeneities.

Single-Line Multi-Channel Flexible Stress Sensor Arrays

Flexible stress sensor arrays, comprising multiple flexible stress sensor units, enable accurate quantification and analysis of spatial stress distribution. Nevertheless, the current implementation of flexible stress sensor arrays faces the challenge of excessive signal wires, resulting in reduced deformability, stability, reliability, and increased costs. The primary obstacle lies in the electric amplitude modulation nature of the sensor unit's signal (e.g., resistance and capacitance), allowing only one signal per wire. To overcome this challenge, the single-line multi-channel signal (SLMC) measurement has been developed, enabling simultaneous detection of multiple sensor signals through one or two signal wires, which effectively reduces the number of signal wires, thereby enhancing stability, deformability, and reliability. This review offers a general knowledge of SLMC measurement beginning with flexible stress sensors and their piezoresistive, capacitive, piezoelectric, and triboelectric sensing mechanisms. A further discussion is given on different arraying methods and their corresponding advantages and disadvantages. Finally, this review categorizes existing SLMC measurement methods into RLC series resonant sensing, transmission line sensing, ionic conductor sensing, triboelectric sensing, piezoresistive sensing, and distributed fiber optic sensing based on their mechanisms, describes the mechanisms and characteristics of each method and summarizes the research status of SLMC measurement.

Wearable Skin Resistance-based Tomographic Sensor for Imaging Contact Pressure Distribution on the Human Body

This study aims to develop a flexible and thin tactile sensor that can capture the contact pressure distribution on the human body. We, therefore, propose a contact resistance-based tomographic tactile sensor that uses the skin as part of the detector. We first evaluated force sensitivity to show that using the skin as a probing layer is possible. We then developed a flexible detector that is 40 mm × 80 mm in size, 200 μm thickness and uses 16 electrodes. As a result, we successfully demonstrated that the proposed method enabled the detection of the contact position within an error of 12.5 % by using frequencies higher than 1 kHz.

Recent Advances in Flexible Piezoresistive Arrays: Materials, Design, and Applications

Spatial distribution perception has become an important trend for flexible pressure sensors, which endows wearable health devices, bionic robots, and human-machine interactive interfaces (HMI) with more precise tactile perception capabilities. Flexible pressure sensor arrays can monitor and extract abundant health information to assist in medical detection and diagnosis. Bionic robots and HMI with higher tactile perception abilities will maximize the freedom of human hands. Flexible arrays based on piezoresistive mechanisms have been extensively researched due to the high performance of pressure-sensing properties and simple readout principles. This review summarizes multiple considerations in the design of flexible piezoresistive arrays and recent advances in their development. First, frequently used piezoresistive materials and microstructures are introduced in which various strategies to improve sensor performance are presented. Second, pressure sensor arrays with spatial distribution perception capability are discussed emphatically. Crosstalk is a particular concern for sensor arrays, where mechanical and electrical sources of crosstalk issues and the corresponding solutions are highlighted. Third, several processing methods are also introduced, classified as printing, field-assisted and laser-assisted fabrication. Next, the representative application works of flexible piezoresistive arrays are provided, including human-interactive systems, healthcare devices, and some other scenarios. Finally, outlooks on the development of piezoresistive arrays are given.

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.

A Hydrogel-Based Electronic Skin for Touch Detection Using Electrical Impedance Tomography

Recent advancement in wearable and robot-assisted healthcare technology gives rise to the demand for smart interfaces that allow more efficient human-machine interaction. In this paper, a hydrogel-based soft sensor for subtle touch detection is proposed. Adopting the working principle of a biomedical imaging technology known as electrical impedance tomography (EIT), the sensor produces images that display the electrical conductivity distribution of its sensitive region to enable touch detection. The sensor was made from a natural gelatin hydrogel whose electrical conductivity is considerably less than that of human skin. The low conductivity of the sensor enabled a touch-detection mechanism based on a novel short-circuiting approach, which resulted in the reconstructed images being predominantly affected by the electrical contact between the sensor and fingertips, rather than the conventionally used piezoresistive response of the sensing material. The experimental results indicated that the proposed sensor was promising for detecting subtle contacts without the necessity of exerting a noticeable force on the sensor.

Silicone Rubber Based-Conductive Composites for Stretchable "All-in-One" Microsystems

Wearable electronics with development trends such as miniaturization, multifunction, and smart integration have become an important part of the Internet of Things (IoT) and have penetrated various sectors of modern society. To meet the increasing demands of wearable electronics in terms of deformability and conformability, many efforts have been devoted to overcoming the nonstretchable and poor conformal properties of traditional functional materials and endowing devices with outstanding mechanical properties. One of the promising approaches is composite engineering in which traditional functional materials are incorporated into the various polymer matrices to develop different kinds of functional composites and construct different functions of stretchable electronics. Herein, we focus on the approach of composite engineering and the polymer matrix of silicone rubber (SR), and we summarize the state-of-the-art details of silicone rubber-based conductive composites (SRCCs), including a summary of their conductivity mechanisms and synthesis methods and SRCC applications for stretchable electronics. For conductivity mechanisms, two conductivity mechanisms of SRCC are emphasized: percolation theory and the quantum tunneling mechanism. For synthesis methods of SRCCs, four typical approaches to synthesize different kinds of SRCCs are investigated: mixing/blending, infiltration, ion implantation, and in situ formation. For SRCC applications, different functions of stretchable electronics based on SRCCs for interconnecting, sensing, powering, actuating, and transmitting are summarized, including stretchable interconnects, sensors, nanogenerators, antennas, and transistors. These functions reveal the feasibility of constructing a stretchable all-in-one self-powered microsystem based on SRCC-based stretchable electronics. As a prospect, this microsystem is expected to integrate the functional sensing modulus, the energy harvesting modulus, and the process and response modulus together to sense and respond to environmental stimulations and human physiological signals.

A biomimetic elastomeric robot skin using electrical impedance and acoustic tomography for tactile sensing

Human skin perceives physical stimuli applied to the body and mitigates the risk of physical interaction through its soft and resilient mechanical properties. Social robots would benefit from whole-body robotic skin (or tactile sensors) resembling human skin in realizing a safe, intuitive, and contact-rich human-robot interaction. However, existing soft tactile sensors show several drawbacks (complex structure, poor scalability, and fragility), which limit their application in whole-body robotic skin. Here, we introduce biomimetic robotic skin based on hydrogel-elastomer hybrids and tomographic imaging. The developed skin consists of a tough hydrogel and a silicone elastomer forming a skin-inspired multilayer structure, achieving sufficient softness and resilience for protection. The sensor structure can also be easily repaired with adhesives even after severe damage (incision). For multimodal tactile sensation, electrodes and microphones are deployed in the sensor structure to measure local resistance changes and vibration due to touch. The ionic hydrogel layer is deformed owing to an external force, and the resulting local conductivity changes are measured via electrodes. The microphones also detect the vibration generated from touch to determine the location and type of dynamic tactile stimuli. The measurement data are then converted into multimodal tactile information through tomographic imaging and deep neural networks. We further implement a sensorized cosmetic prosthesis, demonstrating that our design could be used to implement deformable or complex-shaped robotic skin.

Electrical Stability and Piezoresistive Sensing Performance of High Strain-Range Ultra-Stretchable CNT-Embedded Sensors

Highly flexible and stretchable sensors are becoming increasingly widespread due to their versatile applicability in human/robot monitoring sensors. Conductive polymeric composites have been regarded as potential candidates for such sensors, and carbon nanotubes (CNTs) are widely used to fabricate such composites. In the present study, CNT-embedded high flexible sensors were fabricated using a facile three-roll milling method, which mitigates the drawbacks of the conventional fabrication methods. CNTs content varied between 0.5 and 4.0 wt.%, and the percolation threshold range was obtained via conductivity/resistivity values of the fabricated sensors. Following this, the electrical stability of the sensors was examined against the various DC and AC signals. Furthermore, the fabricated sensors were stretched up to 500% strain, and their sensitivity against varying strain amplitudes was investigated in terms of the change in resistance and gauge factors. Lastly, the fabricated sensors were applied to human fingers for monitoring finger bending and releasing motions to validate their potential applications. The experimental results indicated that these sensors have a percolation threshold of around 2% CNTs content, and the sensors fabricated with 2 to 4% CNTs content showed measurable resistance changes against the applied strain amplitudes of 50–500%. Among these sensors, the sensor with 2% CNTs content showed the highest sensitivity in the studied strain range, exhibiting a resistance change and gauge factor of about 90% and 1.79 against 50% strain amplitude and about 18,500% and 37.07 against 500% strain amplitude, respectively. All these sensors also showed high sensitivity for finger motion detection, showing a resistance change of between 22 and 69%.

Guiding the design of superresolution tactile skins with taxel value isolines theory

Tactile feedback is essential to make robots more agile and effective in unstructured environments. However, high-resolution tactile skins are not widely available; this is due to the large size of robust sensing units and because many units typically lead to fragility in wiring and to high costs. One route toward high-resolution and robust tactile skins involves the embedding of a few sensor units (taxels) into a flexible surface material and the use of signal processing to achieve sensing with superresolution accuracy. Here, we propose a theory for geometric superresolution to guide the development of tactile sensors of this kind and link it to machine learning techniques for signal processing. This theory is based on sensor isolines and allows us to compute the possible force sensitivity and accuracy in contact position and force magnitude as a spatial quantity before building a sensor. We evaluate the influence of different factors, such as elastic properties of the material, structure design, and transduction methods, using finite element simulations and by implementing real sensors. We empirically determine sensor isolines and validate the theory in two custom-built sensors with 1D and 2D measurement surfaces that use barometric units. Using machine learning methods to infer contact information, our sensors obtain an average superresolution factor of over 100 and 1200, respectively. Our theory can guide future tactile sensor designs and inform various design choices.

Self-Restoring Capacitive Pressure Sensor Based on Three-Dimensional Porous Structure and Shape Memory Polymer

Highly flexible and compressible porous polyurethane (PU) structures have effectively been applied in capacitive pressure sensors because of the good elastic properties of the PU structures. However, PU porous structure-based pressure sensors have been limited in practical applications owing to their low durability during pressure cycling. Herein, we report a flexible pressure sensor based on a three-dimensional porous structure with notable durability at a compressive pressure of 500 kPa facilitated by the use of a shape memory polymer (SMP). The SMP porous structure was fabricated using a sugar templating process and capillary effect. The use of the SMP resulted in the maintenance of the sensing performance for 100 cycles at 500 kPa; the SMP can restore its original shape within 30 s of heating at 80 °C. The pressure sensor based on the SMP exhibited a higher sensitivity of 0.0223 kPa⁻¹ than a typical PU-based sensor and displayed excellent sensing performance in terms of stability, response time, and hysteresis. Additionally, the proposed sensor was used to detect shoe insole pressures in real time and exhibited remarkable durability and motion differentiation.

Review on Conductive Polymer/CNTs Nanocomposites Based Flexible and Stretchable Strain and Pressure Sensors

In the last decade, significant developments of flexible and stretchable force sensors have been witnessed in order to satisfy the demand of several applications in robotic, prosthetics, wearables and structural health monitoring bringing decisive advantages due to their manifold customizability, easy integration and outstanding performance in terms of sensor properties and low-cost realization. In this paper, we review current advances in this field with a special focus on polymer/carbon nanotubes (CNTs) based sensors. Based on the electrical properties of polymer/CNTs nanocomposite, we explain underlying principles for pressure and strain sensors. We highlight the influence of the manufacturing processes on the achieved sensing properties and the manifold possibilities to realize sensors using different shapes, dimensions and measurement procedures. After an intensive review of the realized sensor performances in terms of sensitivity, stretchability, stability and durability, we describe perspectives and provide novel trends for future developments in this intriguing field.

Ionic liquid based distributed touch sensor using electrical impedance tomography

Inspired by the human skin sensory mechanism, there are growing interests in creating a sense of touch in robotics. This work describes a new impedance based design to create an artificial tactile sensing skin. It has demonstrated that the electrical impedance tomography imaging technique allows for detecting the pressure distribution in a large area by a distributed touch sensor. The sensor is fabricated by filling a circular shaped phantom with liquid conductor and covering with an elastic shell on the top. The proposed sensor can detect the pressure applied to the elastic top using electrical impedance tomography imaging method. The sensor can therefore operate as a touch sensor mimicking a piezo-impedance operation in a simple fashion. The new sensor can differentiate between various force levels and their locations and thus produces a distribution of pressure. Such a simple sensor can function as a large area skin, enabling smarter human-machine interactions in emerging augmented reality and robotic applications.

Embodied tactile perception and learning

Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large‐scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.

Microdome-Induced Strain Localization for Biaxial Strain Decoupling toward Stretchable and Wearable Human Motion Detection

Soft strain sensors have attracted significant attention in wearable human motion monitoring applications. However, there is still a huge challenge for decoupled measurement of multidirectional strains. In this study, we have developed a biaxial and stretchable strain sensor based on a carbon nanotube (CNT) film and a microdome array (MA)-patterned elastomeric substrate. The MA structures lead to generating localized and directional microcracks of CNT films within the intended regions under tensile strain. This mechanism allows a single sensing layer to act as a strain sensor capable of decoupling the biaxial strains into axial and transverse terms. The ratio of resistance change between two perpendicular axes is about 960% under an x-directional strain of 30%, demonstrating the biaxial decoupling capability. Also, the proposed strain sensor shows high stretchability and excellent long-term reliability under a cyclic loading test. Finally, wearable devices integrated with the strain sensor have been successfully utilized to monitor various human motions of the wrist, elbow, knee, and fingers by measuring joint bending and skin elongation.

3D printed deformable sensors

The ability to directly print compliant biomedical devices on live human organs could benefit patient monitoring and wound treatment, which requires the 3D printer to adapt to the various deformations of the biological surface. We developed an in situ 3D printing system that estimates the motion and deformation of the target surface to adapt the toolpath in real time. With this printing system, a hydrogel-based sensor was printed on a porcine lung under respiration-induced deformation. The sensor was compliant to the tissue surface and provided continuous spatial mapping of deformation via electrical impedance tomography. This adaptive 3D printing approach may enhance robot-assisted medical treatments with additive manufacturing capabilities, enabling autonomous and direct printing of wearable electronics and biological materials on and inside the human body.

Artificial Sensory Memory

Sensory memory, formed at the beginning while perceiving and interacting with the environment, is considered a primary source of intelligence. Transferring such biological concepts into electronic implementation aims at achieving perceptual intelligence, which would profoundly advance a broad spectrum of applications, such as prosthetics, robotics, and cyborg systems. Here, the recent developments in the design and fabrication of artificial sensory memory devices are summarized and their applications in recognition, manipulation, and learning are highlighted. The emergence of such devices benefits from recent progress in both bioinspired sensing and neuromorphic engineering technologies and derives from abundant inspiration and benchmarks from an improved understanding of biological sensory processing. Increasing attention to this area would offer unprecedented opportunities toward new hardware architecture of artificial intelligence, which could extend the capabilities of digital systems with emotional/psychological attributes. Pending challenges are also addressed to aspects such as integration level, energy efficiency, and functionality, which would undoubtedly shed light on the future development of translational implementations.

Direct 3D Printing of Highly Anisotropic, Flexible, Constriction-Resistive Sensors for Multidirectional Proprioception in Soft Robots

A key missing technology for the emerging field of soft robotics is the provision of highly selective multidirectional tactile sensing that can be easily integrated into a robot using simple fabrication techniques. Conventional strain sensors, such as strain gauges, are typically designed to respond to strain in a single direction and are mounted on the external surface of a structure. Herein, we present a technique for three-dimensional (3D) printing of multidirectional, anisotropic, and constriction-resistive strain sensors, which can be directly integrated into the interior of soft robots. Using a carbon-nanotube-reinforced polylactic acid (PLA-CNT), both the sensing element and the conductive interconnect of the sensor system are 3D-printed. The sensor's sensitivity and anisotropy can be adjusted by controlling the air gap between printed adjacent tracks, infill density, and build orientation relative to the main loading direction. In particular, sensors printed with a near-zero air gap, i.e., adjacent tracks forming a kissing bond, can achieve a gauge factor of ∼1342 perpendicular to the raster orientation and a gauge factor of ∼1 parallel to the raster orientation. The maximum directional selectivity of this ultrasensitive sensor is 31.4, which is approximately 9 times greater than the highest value reported for multidirectional sensors so far. The high sensitivity stems from the progressive opening and closing of the kissing bond between adjacent tracks. The potential of this type of sensors and the simple manufacturing process are demonstrated by integrating the sensor with a soft robotic actuator. The sensors are able to identify and quantify the bending deformation and angle in different directions. The ability to fabricate sensors with tailored footprints and directional selectivity during 3D printing of soft robotic systems paves the way toward highly customizable, highly integrated multifunctional soft robots that are better able to sense both themselves and their environments.

Wearable Strain Sensors Using Light Transmittance Change of Carbon Nanotube-Embedded Elastomers with Microcracks

A number of flexible and stretchable strain sensors based on piezoresistive and capacitive principles have been recently developed. However, piezoresistive sensors suffer from poor long-term stability and considerable hysteresis of signals. On the other hand, capacitive sensors exhibit limited sensitivity and strong electromagnetic interference from the neighboring environment. In order to resolve these problems, a novel stretchable strain sensor based on the modulation of optical transmittance of carbon nanotube (CNT)-embedded Ecoflex is introduced in this paper. Within the film of multiwalled CNTs embedded in the Ecoflex substrate, the microcracks are propagated under tensile strain, changing the optical transmittance of the film. The proposed sensor exhibits good stretchability (ε ≈ 400%), high linearity (R² > 0.98) in the strain range of ε = 0-100%, excellent stability, high sensitivity (gauge factor ≈ 30), and small hysteresis (∼1.8%). The sensor was utilized to detect the bending of the finger and wrist for the control of a robot arm. Furthermore, the applications of this sensor to the real-time posture monitoring of the neck and to the detection of subtle human motions were demonstrated.

Ultrathin, Biocompatible, and Flexible Pressure Sensor with a Wide Pressure Range and Its Biomedical Application

In this research, an ultrathin, biocompatible, and flexible pressure sensor with a wide pressure range has been developed and applied in biomedical applications. The pressure sensing mechanism is based on the variation of contact resistance between an electrode and a three-dimensional microstructured polyimide/carbon nanotube composite film. The sensor has a thickness of about 31.3 μm, a maximum sensitivity of 41.0 MPa^-1, and a sensing range of 10-500 kPa. Moreover, in situ temperature measurement by an integrated resistive temperature detector enables data correction for varying temperature conditions. In order to show the advantages of the fabricated sensor, it is attached to the human body and integrated with the surface of a radiofrequency ablation (RFA) needle with small radius of curvature. In the experiments, the proposed pressure sensor measured subtle pressure levels (pulse pressure) and high pressure levels (fingertip pressure) without losing conformal contact with the skin. In addition, when the pressure-sensor-integrated RFA needle was inserted into a bovine liver, successful detection of steam popping phenomenon was observed.

Inconsistency Calibrating Algorithms for Large Scale Piezoresistive Electronic Skin

In the field of safety and communication of human-robot interaction (HRI), using large-scale electronic skin will be the tendency in the future. The force-sensitive piezoresistive material is the key for piezoresistive electronic skin. In this paper, a non-array large scale piezoresistive tactile sensor and its corresponding calibration methods were presented. Because of the creep inconsistency of large scale piezoresistive material, a creep tracking compensation method based on K-means clustering and fuzzy pattern recognition was proposed to improve the detection accuracy. With the compensated data, the inconsistency and nonlinearity of the sensor was calibrated. The calibration process was divided into two parts. The hierarchical clustering algorithm was utilized firstly to classify and fuse piezoresistive property of different regions over the whole sensor. Then, combining the position information, the force detection model was constructed by Back-Propagation (BP) neural network. At last, a novel flexible tactile sensor for detecting contact position and force was designed as an example and tested after being calibrated. The experimental results showed that the calibration methods proposed were effective in detecting force, and the detection accuracy was improved.

Synergetic Effect of Porous Elastomer and Percolation of Carbon Nanotube Filler toward High Performance Capacitive Pressure Sensors

Wearable pressure sensors have been attracting great attention for a variety of practical applications, including electronic skin, smart textiles, and healthcare devices. However, it is still challenging to realize wearable pressure sensors with sufficient sensitivity and low hysteresis under small mechanical stimuli. Herein, we introduce simple, cost-effective, and sensitive capacitive pressure sensor based on porous Ecoflex-multiwalled carbon nanotube composite (PEMC) structures, which leads to enhancing the sensitivity (6.42 and 1.72 kPa^-1 in a range of 0-2 and 2-10 kPa, respectively) due to a synergetic effect of the porous elastomer and percolation of carbon nanotube fillers. The PEMC structure shows excellent mechanical deformability and compliance for an effective integration with practical wearable devices. Also, the PEMC-based pressure sensor shows not only the long-term stability, low-hysteresis, and fast response under dynamic loading but also the high robustness against temperature and humidity changes. Finally, we demonstrate a prosthetic robot finger integrated with a PEMC-based pressure sensor and an actuator as well as a healthcare wristband capable of continuously monitoring blood pressure and heart rate.

Large-Area Carbon Nanotube-Based Flexible Composites for Ultra-Wide Range Pressure Sensing and Spatial Pressure Mapping

Flexible pressure sensors are of broad interest for applications including human-machine interfaces, wearable electronics, and object/motion detection. However, complexities associated with constituent materials, fabrication processes, sensing mechanisms, and hardwiring often hinder the large-scale applications of using high performance pressure sensors reported in the literature. Here we demonstrate a large-area, highly flexible, conformable, and mechanically robust pressure sensor using a silicone elastomer with an embedded nonwoven textile carrier coated with carbon nanotubes. The selected silicone polymer allows through-thickness deformability of the sensor while the high modulus textile carrier ensures in-plane stiffness and stability. The sensor has an initial electrical conductivity of 4.4 ± 0.38 S/m and is fabricated using a straightforward dip coating and polymer infusion process and can be easily scaled-up for large-scale applications. On the basis of its hierarchical composite structure, this piezoresistive pressure sensor possesses extremely high resilience under compression, a repeatable monotonic positive pressure correlation, and an ultrawide elastic working range (5.5 ± 0.5 MPa) that can be segmentally linearized. A true two-dimensional modality for spatial pressure mapping is realized by utilizing electrical impedance tomography (EIT) and demonstrated to yield conductivity maps that can estimate the location, shape, and amplitude of both localized and distributed pressure with simple contact areas.

Electronic Skin: Recent Progress and Future Prospects for Skin-Attachable Devices for Health Monitoring, Robotics, and Prosthetics

Recent progress in electronic skin or e-skin research is broadly reviewed, focusing on technologies needed in three main applications: skin-attachable electronics, robotics, and prosthetics. First, since e-skin will be exposed to prolonged stresses of various kinds and needs to be conformally adhered to irregularly shaped surfaces, materials with intrinsic stretchability and self-healing properties are of great importance. Second, tactile sensing capability such as the detection of pressure, strain, slip, force vector, and temperature are important for health monitoring in skin attachable devices, and to enable object manipulation and detection of surrounding environment for robotics and prosthetics. For skin attachable devices, chemical and electrophysiological sensing and wireless signal communication are of high significance to fully gauge the state of health of users and to ensure user comfort. For robotics and prosthetics, large-area integration on 3D surfaces in a facile and scalable manner is critical. Furthermore, new signal processing strategies using neuromorphic devices are needed to efficiently process tactile information in a parallel and low power manner. For prosthetics, neural interfacing electrodes are of high importance. These topics are discussed, focusing on progress, current challenges, and future prospects.

Multipoint-Detection Strain Sensor with a Single Electrode Using Optical Ultrasound Generated by Carbon Nanotubes

With the development of wearable devices, strain sensors have attracted large interest for the detection of human motion, movement, and breathing. Various strain sensors consisting of stretchable conductive materials have been investigated based on resistance and capacitance differences according to the strain. However, this method requires multiple electrodes for multipoint detection. We propose a strain sensor capable of multipoint detection with a single electrode, based on the ultrasound pulse-echo method. It consists of several transmitters of carbon nanotubes (CNTs) and a single polyvinylidene fluoride receiver. The strain sensor was fabricated using CNTs embedded in stretchable polydimethylsiloxane. The received data are characterized by the different times of transmission from the CNTs of each point depending on the strain, i.e., the sensor can detect the positions of the CNTs. This study demonstrates the application of the multipoint strain sensor with a single electrode for measurements up to a strain of 30% (interval of 1%). We considered the optical and acoustic energy losses in the sensor design. In addition, to evaluate the utility of the sensor, finger bending with three-point CNTs and flexible phantom bending with six-point CNTs for the identification of an S-curve having mixed expansion and compression components were carried out.

Machine Learning for Haptics: Inferring Multi-Contact Stimulation From Sparse Sensor Configuration

Robust haptic sensation systems are essential for obtaining dexterous robots. Currently, we have solutions for small surface areas, such as fingers, but affordable and robust techniques for covering large areas of an arbitrary 3D surface are still missing. Here, we introduce a general machine learning framework to infer multi-contact haptic forces on a 3D robot's limb surface from internal deformation measured by only a few physical sensors. The general idea of this framework is to predict first the whole surface deformation pattern from the sparsely placed sensors and then to infer number, locations, and force magnitudes of unknown contact points. We show how this can be done even if training data can only be obtained for single-contact points using transfer learning at the example of a modified limb of the Poppy robot. With only 10 strain-gauge sensors we obtain a high accuracy also for multiple-contact points. The method can be applied to arbitrarily shaped surfaces and physical sensor types, as long as training data can be obtained.

Wearable, Ultrawide-Range, and Bending-Insensitive Pressure Sensor Based on Carbon Nanotube Network-Coated Porous Elastomer Sponges for Human Interface and Healthcare Devices

Flexible and wearable pressure sensors have attracted a tremendous amount of attention due to their wider applications in human interfaces and healthcare monitoring. However, achieving accurate pressure detection and stability against external stimuli (in particular, bending deformation) over a wide range of pressures from tactile to body weight levels is a great challenge. Here, we introduce an ultrawide-range, bending-insensitive, and flexible pressure sensor based on a carbon nanotube (CNT) network-coated thin porous elastomer sponge for use in human interface devices. The integration of the CNT networks into three-dimensional microporous elastomers provides high deformability and a large change in contact between the conductive CNT networks due to the presence of micropores, thereby improving the sensitivity compared with that obtained using CNT-embedded solid elastomers. As electrical pathways are continuously generated up to high compressive strain (∼80%), the pressure sensor shows an ultrawide pressure sensing range (10 Pa to 1.2 MPa) while maintaining favorable sensitivity (0.01-0.02 kPa^-1) and linearity ( R² ∼ 0.98). Also, the pressure sensor exhibits excellent electromechanical stability and insensitivity to bending-induced deformations. Finally, we demonstrate that the pressure sensor can be applied in a flexible piano pad as an entertainment human interface device and a flexible foot insole as a wearable healthcare and gait monitoring device.

Transparent wearable three-dimensional touch by self-generated multiscale structure

Pressure-sensitive touch panels can measure pressure and location (3D) information simultaneously and provide an intuitive and natural method for expressing one’s intention with a higher level of controllability and interactivity. However, they have been generally realized by a simple combination of pressure and location sensor or a stylus-based interface, which limit their implementation in a wide spectrum of applications. Here, we report a first demonstration (to our knowledge) of a transparent and flexible 3D touch which can sense the 3D information in a single device with the assistance of functionally designed self-generated multiscale structures. The single 3D touch system is demonstrated to draw a complex three-dimensional structure by utilizing the pressure as a third coordinate. Furthermore, rigorous theoretical analysis is carried out to achieve the target pressure performances with successful 3D data acquisition in wireless and wearable conditions, which in turn, paves the way for future wearable devices.

Touch technology holds potential for the development of smartphones and touchscreens, yet the conventional devices are usually built on separate pressure and location sensing units. Kim et al. show a flexible and transparent touch sensor capable of mapping the position and pressure at the same time.

Characterization of Elastic Polymer-Based Smart Insole and a Simple Foot Plantar Pressure Visualization Method Using 16 Electrodes

In this paper, we propose a smart insole for inexpensive plantar pressure sensing and a simple visualizing scheme. The insole is composed of two elastomeric layers and two electrode layers where the common top electrode is submerged in the insole. The upper elastomeric layer is non-conductive poly-dimethyl-siloxane (PDMS) and supports plantar pressure buffering and the lower layer is carbon nano-tube (CNT)-dispersed PDMS for pressure sensing through piezo-resistivity. Under the lower sensing layer are 16 bottom electrodes for pressure distribution sensing without cell-to-cell interference. Since no soldering or sewing is needed the smart insole manufacturing processes is simple and cost-effective. The pressure sensitivity and time response of the material was measured and based on the 16 sensing data of the smart insole, we virtually extended the frame size for continuous and smoothed pressure distribution image with the help of a simple pseudo interpolation scheme.

Toward Perceptive Soft Robots: Progress and Challenges

In the past few years, soft robotics has rapidly become an emerging research topic, opening new possibilities for addressing real-world tasks. Perception can enable robots to effectively explore the unknown world, and interact safely with humans and the environment. Among all extero- and proprioception modalities, the detection of mechanical cues is vital, as with living beings. A variety of soft sensing technologies are available today, but there is still a gap to effectively utilize them in soft robots for practical applications. Here, the developments in soft robots with mechanical sensing are summarized to provide a comprehensive understanding of the state of the art in this field. Promising sensing technologies for mechanically perceptive soft robots are described, categorized, and their pros and cons are discussed. Strategies for designing soft sensors and criteria to evaluate their performance are outlined from the perspective of soft robotic applications. Challenges and trends in developing multimodal sensors, stretchable conductive materials and electronic interfaces, modeling techniques, and data interpretation for soft robotic sensing are highlighted. The knowledge gap and promising solutions toward perceptive soft robots are discussed and analyzed to provide a perspective in this field.

Highly Sensitive and Stretchable Resistive Strain Sensors Based on Microstructured Metal Nanowire/Elastomer Composite Films

High sensitivity and high stretchability are two conflicting characteristics that are difficult to achieve simultaneously in elastic strain sensors. A highly sensitive and stretchable strain sensor comprising a microstructured metal nanowire (mNW)/elastomer composite film is presented. The surface structure is easily prepared by combining an mNW coating and soft-lithographic replication processes in a simple and reproducible manner. The densely packed microprism-array architecture of the composite film leads to a large morphological change in the mNW percolation network by efficiently concentrating the strain in the valley regions upon stretching. Meanwhile, the percolation network comprising mNWs with a high aspect ratio is stable enough to prevent electrical failure, even under high strains. This enables the sensor to simultaneously satisfy high sensitivity (gauge factor ≈81 at >130% strain) and high stretchability (150%) while ensuring long-term reliability (10 000 cycles at 150% strain). The sensor can also detect strain induced by bending and pressure, thus demonstrating its potential as a versatile sensing tool. The sensor is successfully utilized to monitor a wide range of human motions in real time. Furthermore, the unique sensing mechanism is easily extended to detect more complex multiaxial strains by optimizing the surface morphology of the device.

Stretchable, Flexible, Scalable Smart Skin Sensors for Robotic Position and Force Estimation

The design and validation of a continuously stretchable and flexible skin sensor for collaborative robotic applications is outlined. The skin consists of a PDMS skin doped with Carbon Nanotubes and the addition of conductive fabric, connected by only five wires to a simple microcontroller. The accuracy is characterized in position as well as force, and the skin is also tested under uniaxial stretch. There are also two examples of practical implementations in collaborative robotic applications. The stationary position estimate has an RMSE of 7.02 mm, and the sensor error stays within 2.5 ± 1.5 mm even under stretch. The skin consistently provides an emergency stop command at only 0.5 N of force and is shown to maintain a collaboration force of 10 N in a collaborative control experiment.

Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces

Flexible hybrid electronics (FHE), designed in wearable and implantable configurations, have enormous applications in advanced healthcare, rapid disease diagnostics, and persistent human-machine interfaces. Soft, contoured geometries and time-dynamic deformation of the targeted tissues require high flexibility and stretchability of the integrated bioelectronics. Recent progress in developing and engineering soft materials has provided a unique opportunity to design various types of mechanically compliant and deformable systems. Here, we summarize the required properties of soft materials and their characteristics for configuring sensing and substrate components in wearable and implantable devices and systems. Details of functionality and sensitivity of the recently developed FHE are discussed with the application areas in medicine, healthcare, and machine interactions. This review concludes with a discussion on limitations of current materials, key requirements for next generation materials, and new application areas.

Nanomaterial-Enabled Wearable Sensors for Healthcare

Highly sensitive wearable sensors that can be conformably attached to human skin or integrated with textiles to monitor the physiological parameters of human body or the surrounding environment have garnered tremendous interest. Owing to the large surface area and outstanding material properties, nanomaterials are promising building blocks for wearable sensors. Recent advances in the nanomaterial-enabled wearable sensors including temperature, electrophysiological, strain, tactile, electrochemical, and environmental sensors are presented in this review. Integration of multiple sensors for multimodal sensing and integration with other components into wearable systems are summarized. Representative applications of nanomaterial-enabled wearable sensors for healthcare, including continuous health monitoring, daily and sports activity tracking, and multifunctional electronic skin are highlighted. Finally, challenges, opportunities, and future perspectives in the field of nanomaterial-enabled wearable sensors are discussed.

An extremely simple macroscale electronic skin realized by deep machine learning

Complicated structures consisting of multi-layers with a multi-modal array of device components, i.e., so-called patterned multi-layers, and their corresponding circuit designs for signal readout and addressing are used to achieve a macroscale electronic skin (e-skin). In contrast to this common approach, we realized an extremely simple macroscale e-skin only by employing a single-layered piezoresistive MWCNT-PDMS composite film with neither nano-, micro-, nor macro-patterns. It is the deep machine learning that made it possible to let such a simple bulky material play the role of a smart sensory device. A deep neural network (DNN) enabled us to process electrical resistance change induced by applied pressure and thereby to instantaneously evaluate the pressure level and the exact position under pressure. The great potential of this revolutionary concept for the attainment of pressure-distribution sensing on a macroscale area could expand its use to not only e-skin applications but to other high-end applications such as touch panels, portable flexible keyboard, sign language interpreting globes, safety diagnosis of social infrastructures, and the diagnosis of motility and peristalsis disorders in the gastrointestinal tract.