Skin plays an important role in mediating our interactions with the world. Recreating the properties of skin using electronic devices could have profound implications for prosthetics and medicine. The pursuit of artificial skin has inspired innovations in materials to imitate skin's unique characteristics, including mechanical durability and stretchability, biodegradability, and the ability to measure a diversity of complex sensations over large areas. New materials and fabrication strategies are being developed to make mechanically compliant and multifunctional skin-like electronics, and improve brain/machine interfaces that enable transmission of the skin's signals into the body. This Review will cover materials and devices designed for mimicking the skin's ability to sense and generate biomimetic signals.

Pursuing prosthetic electronic skin.

Humans can feel, weigh and grasp diverse objects, and simultaneously infer their material properties while applying the right amount of force—a challenging set of tasks for a modern robot1. Mechanoreceptor networks that provide sensory feedback and enable the dexterity of the human grasp2 remain difficult to replicate in robots. Whereas computer-vision-based robot grasping strategies3–5 have progressed substantially with the abundance of visual data and emerging machine-learning tools, there are as yet no equivalent sensing platforms and large-scale datasets with which to probe the use of the tactile information that humans rely on when grasping objects. Studying the mechanics of how humans grasp objects will complement vision-based robotic object handling. Importantly, the inability to record and analyse tactile signals currently limits our understanding of the role of tactile information in the human grasp itself—for example, how tactile maps are used to identify objects and infer their properties is unknown6. Here we use a scalable tactile glove and deep convolutional neural networks to show that sensors uniformly distributed over the hand can be used to identify individual objects, estimate their weight and explore the typical tactile patterns that emerge while grasping objects. The sensor array (548 sensors) is assembled on a knitted glove, and consists of a piezoresistive film connected by a network of conductive thread electrodes that are passively probed. Using a low-cost (about US$10) scalable tactile glove sensor array, we record a large-scale tactile dataset with 135,000 frames, each covering the full hand, while interacting with 26 different objects. This set of interactions with different objects reveals the key correspondences between different regions of a human hand while it is manipulating objects. Insights from the tactile signatures of the human grasp—through the lens of an artificial analogue of the natural mechanoreceptor network—can thus aid the future design of prosthetics7, robot grasping tools and human–robot interactions1,8–10. Tactile patterns obtained from a scalable sensor-embedded glove and deep convolutional neural networks help to explain how the human hand can identify and grasp individual objects and estimate their weights.

Learning the signatures of the human grasp using a scalable tactile glove.

https://www.princeton.edu/~yael/Publications/BotvinickNivBarto2008.pdf

Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective

Tactile sensing is required for the dexterous manipulation of objects in robotic applications. In particular, the ability to measure and distinguish in real time normal and shear forces is crucial for slip detection and interaction with fragile objects. Here, we report a biomimetic soft electronic skin (e-skin) that is composed of an array of capacitors and capable of measuring and discriminating in real time both normal and tangential forces. It is enabled by a three-dimensional structure that mimics the interlocked dermis-epidermis interface in human skin. Moreover, pyramid microstructures arranged along nature-inspired phyllotaxis spirals resulted in an e-skin with increased sensitivity, minimal hysteresis, excellent cycling stability, and response time in the millisecond range. The e-skin provided sensing feedback for controlling a robot arm in various tasks, illustrating its potential application in robotics with tactile feedback.

https://robotics.sciencemag.org/content/robotics/3/24/eaau6914.full-text.pdf

A hierarchically patterned, bioinspired e-skin able to detect the direction of applied pressure for robotics.

Flexible electronics, as an emerging and exciting research field, have brought great interest to the issue of how to make flexible electronic materials that offer both durability and high performance at strained states. With the advent of on-body wearable and implantable electronics, as well as increasing demands for human-friendly intelligent soft robots, enormous effort is being expended on highly flexible functional materials, especially stretchable electrodes, by both the academic and industrial communities. Among different deformation modes, stretchability is the most demanding and challenging. This review focuses on the latest advances in stretchable transparent electrodes based on a new design strategy known as kirigami (the art of paper cutting) and investigates the recent progress on novel applications, including skin-like electronics, implantable biodegradable devices, and bioinspired soft robotics. By comparing the optoelectrical and mechanical properties of different electrode materials, some of the most important outcomes with comments on their merits and demerits are raised. Key design considerations in terms of geometries, substrates, and adhesion are also discussed, offering insights into the universal strategies for engineering stretchable electrodes regardless of the material. It is suggested that highly stretchable and biocompatible electrodes will greatly boost the development of next-generation intelligent life-like electronics.

Flexible Electronics: Stretchable Electrodes and Their Future

When we run our fingers over the surface of an object, we acquire information about its microgeometry and material properties. Texture information is widely believed to be conveyed in spatial patterns of activation evoked across one of three populations of cutaneous mechanoreceptive afferents that innervate the fingertips. Here, we record the responses evoked in individual cutaneous afferents in Rhesus macaques as we scan a diverse set of natural textures across their fingertips using a custom-made rotating drum stimulator. We show that a spatial mechanism can only account for the processing of coarse textures. Information about most natural textures, however, is conveyed through precise temporal spiking patterns in afferent responses, driven by high-frequency skin vibrations elicited during scanning. Furthermore, these texture-specific spiking patterns predictably dilate or contract in time with changes in scanning speed; the systematic effect of speed on neuronal activity suggests that it can be reversed to achieve perceptual constancy across speeds. The proposed temporal coding mechanism involves converting the fine spatial structure of the surface into a temporal spiking pattern, shaped in part by the mechanical properties of the skin, and ascribes an additional function to vibration-sensitive mechanoreceptive afferents. This temporal mechanism complements the spatial one and greatly extends the range of tangible textures. We show that a combination of spatial and temporal mechanisms, mediated by all three populations of afferents, accounts for perceptual judgments of texture.

https://www.pnas.org/content/pnas/110/42/17107.full.pdf

Spatial and temporal codes mediate the tactile perception of natural textures

https://www.cell.com/trends/neurosciences/pdf/S0166-2236(14)00155-6.pdf

Touch is a team effort: interplay of submodalities in cutaneous sensibility.

Electrical stimulation of sensory nerves is a powerful tool for studying neural coding because it can activate neural populations in ways that natural stimulation cannot Electrical stimulation of the nerve has also been used to restore sensation to patients who have suffered the loss of a limb We have used long-term implanted electrical interfaces to elucidate the neural basis of perceived intensity in the sense of touch To this end, we assessed the sensory correlates of neural firing rate and neuronal population recruitment independently by varying two parameters of nerve stimulation: pulse frequency and pulse width Specifically, two amputees, chronically implanted with peripheral nerve electrodes, performed each of three psychophysical tasks-intensity discrimination, magnitude scaling, and intensity matching-in response to electrical stimulation of their somatosensory nerves We found that stimulation pulse width and pulse frequency had systematic, cooperative effects on perceived tactile intensity and that the artificial tactile sensations could be reliably matched to skin indentations on the intact limb We identified a quantity we termed the activation charge rate (ACR), derived from stimulation parameters, that predicted the magnitude of artificial tactile percepts across all testing conditions On the basis of principles of nerve fiber recruitment, the ACR represents the total population spike count in the activated neural population Our findings support the hypothesis that population spike count drives the magnitude of tactile percepts and indicate that sensory magnitude can be manipulated systematically by varying a single stimulation quantity

/pdf/the-neural-basis-of-perceived-intensity-in-natural-and-1zwnzsztt7.pdf

The neural basis of perceived intensity in natural and artificial touch

When we grasp and manipulate an object, populations of tactile nerve fibers become activated and convey information about the shape, size, and texture of the object and its motion across the skin. The response properties of tactile fibers have been extensively characterized in single-unit recordings, yielding important insights into how individual fibers encode tactile information. A recurring finding in this extensive body of work is that stimulus information is distributed over many fibers. However, our understanding of population-level representations remains primitive. To fill this gap, we have developed a model to simulate the responses of all tactile fibers innervating the glabrous skin of the hand to any spatiotemporal stimulus applied to the skin. The model first reconstructs the stresses experienced by mechanoreceptors when the skin is deformed and then simulates the spiking response that would be produced in the nerve fiber innervating that receptor. By simulating skin deformations across the palmar surface of the hand and tiling it with receptors at their known densities, we reconstruct the responses of entire populations of nerve fibers. We show that the simulated responses closely match their measured counterparts, down to the precise timing of the evoked spikes, across a wide variety of experimental conditions sampled from the literature. We then conduct three virtual experiments to illustrate how the simulation can provide powerful insights into population coding in touch. Finally, we discuss how the model provides a means to establish naturalistic artificial touch in bionic hands.

https://www.pnas.org/content/pnas/114/28/E5693.full.pdf

Simulating tactile signals from the whole hand with millisecond precision.

Sensory systems are designed to extract behaviorally relevant information from the environment. In seeking to understand a sensory system, it is important to understand the environment within which...

https://www.physiology.org/doi/pdf/10.1152/jn.00680.2013

Hannes P. Saal

Papers

Spatial and temporal codes mediate the tactile perception of natural textures

Touch is a team effort: interplay of submodalities in cutaneous sensibility.

The neural basis of perceived intensity in natural and artificial touch

Simulating tactile signals from the whole hand with millisecond precision.

Natural scenes in tactile texture