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To accurately probe the tactile information on soft skin, it is critical for the pressure sensing array to be free of noise and inter-taxel crosstalk, irrespective of the measurement condition. However, on dynamically moving and soft surfaces, which are common conditions for on-skin and robotic applications, obtaining precise measurement without compromising the sensing performance is a significant challenge due to mechanical coupling between the sensors and with the moving surface. In this work, multi-level architectural design of micro-pyramids and trapezoid-shaped mechanical barrier array was implemented to enable accurate spatiotemporal tactile sensing on soft surfaces under dynamic deformations. Trade-off relationship between limit of detection and bending insensitivity was discovered, which was overcome by employing micropores in barrier structures. Finally, in-situ pressure mapping on dynamically moving soft surfaces without signal distortion is demonstrated while human skin and/or soft robots are performing complicated tasks such as reading Braille and handling the artificial organs.

Patient-friendly medical diagnostics and treatments have been receiving a great deal of interest due to their rapid and cost-effective health care applications with minimized risk of infection, which has the potential to replace conventional hospital-based medical procedures. In particular, the integration of recently developed materials into health care devices allows the rapid development of point-of-care (POC) sensing platforms and implantable devices with special functionalities. In this review, the recent advances in biosensors for patient-friendly diagnosis and implantable devices for patient-friendly treatment are discussed. Comprehensive analysis of portable and wearable biosensing platforms for patient-friendly health monitoring and disease diagnosis is provided, including topics such as materials selection, device structure and integration, and biomarker detection strategies. Moreover, specific challenges related to each biological fluid for wearable biosensor-based POC applications are presented. Also, advances in implantable devices, including recent materials development and wireless communication strategies, are discussed. Furthermore, various patient-friendly surgical and treatment approaches are reviewed, such as minimally invasive insertion and mounting, in vivo electrical and optical modulations, and post-operation health monitoring. Finally, the challenges and future perspectives toward the development of the patient-friendly diagnosis and treatment are provided.

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.

An ultrahigh sensitive capacitive pressure sensor based on a porous pyramid dielectric layer (PPDL) is reported. Compared to that of the conventional pyramid dielectric layer, the sensitivity was drastically increased to 44.5 kPa-1 in the pressure range

Electronic skin are devices that mimic the functionalities of human skin, which require high sensitivity, large dynamic range, high spatial uniformity, low-cost and large-area processability, and the capacity to differentiate various external inputs. We herein introduce a versatile droplet-based microfluidic-assisted emulsion self-assembly process to generate three-dimensional microstructure-based high-performance capacitive and piezoresistive pressure sensors for electronic skin applications. Our technique can generate uniformly sized micropores that are self-assembled in an orderly close-packed manner over a large area, which results in high spatial uniformity. The size of the micropores can easily be tuned from 100 to 500 μm, through which sensitivity and dynamic range were controlled as high as 0.86 kPa-1 and up to 100 kPa. Our device can be printed on curvilinear surfaces and be molded into various shapes. We furthermore demonstrate that by simultaneously utilizing capacitive and piezoresistive pressure sensors, we can distinguish between pressure and temperature, or between pressure and proximity. These demonstrations make our process and sensors highly useful for a wide variety of electronic skin applications.

Tactile sensors that can mechanically decouple, and therefore differentiate, various tactile inputs are highly important to properly mimic the sensing capabilities of human skin. Herein, we present an all-solution processable pressure insensitive strain sensor that utilizes the difference in structural change upon the application of pressure and tensile strain. Under the application of strain, microcracks occur within the multiwalled carbon nanotube (MWCNT) network, inducing a large change in resistance with gauge factor of ∼56 at 70% strain. On the other hand, under the application of pressure to as high as 140 kPa, negligible change in resistance is observed, which can be attributed to the pressure working primarily to close the pores, and hence minimally changing the MWCNT network conformation. Our sensor can easily be coated onto irregularly shaped three-dimensional objects (e.g., robotic hand) via spray coating, or be attached to human joints, to detect bending motion. Furthermore, our sensor can differentiate between shear stress and normal pressure, and the local strain can be spatially mapped without the use of patterned electrode array using electrical impedance tomography. These demonstrations make our sensor highly useful and important for the future development of high performance tactile sensors.

Inspired by the human somatosensory system, pressure applied to multiple pressure sensors is received in parallel and combined into a representative signal pattern, which is subsequently processed using machine learning. The pressure signals are combined using a wireless system, where each sensor is assigned a specific resonant frequency on the reflection coefficient (S11 ) spectrum, and the applied pressure changes the magnitude of the S11 pole with minimal frequency shift. This allows the differentiation and identification of the pressure applied to each sensor. The pressure sensor consists of polypyrrole-coated microstructured poly(dimethylsiloxane) placed on top of electrodes, operating as a capacitive sensor. The high dielectric constant of polypyrrole enables relatively high pressure-sensing performance. The coils are vertically stacked to enable the reader to receive the signals from all of the sensors simultaneously at a single location, analogous to the junction between neighboring primary neurons to a secondary neuron. Here, the stacking order is important to minimize the interference between the coils. Furthermore, convolutional neural network (CNN)-based machine learning is utilized to predict the applied pressure of each sensor from unforeseen S11 spectra. With increasing training, the prediction accuracy improves (with mean squared error of 0.12), analogous to humans' cognitive learning ability.