Fingerprint‐Enhanced Capacitive‐Piezoelectric Flexible Sensing Skin to Discriminate Static and Dynamic Tactile Stimuli

Abstract

Inspired by the structure and functions of the human skin, a highly sensitive capacitive‐piezoelectric flexible sensing skin with fingerprint‐like patterns to detect and discriminate between spatiotemporal tactile stimuli including static and dynamic pressures and textures is presented. The capacitive‐piezoelectric tandem sensing structure is embedded in the phalange of a 3D‐printed robotic hand, and a tempotron classifier system is used for tactile exploration. The dynamic tactile sensor, interfaced with an extended gate configuration to a common source metal oxide semiconductor field effect transistor (MOSFET), exhibits a sensitivity of 2.28 kPa−1. The capacitive sensing structure has nonlinear characteristics with sensitivity varying from 0.25 kPa−1 in the low‐pressure range (<100 Pa) to 0.002 kPa−1 in high pressure (≈2.5 kPa). The output from the presented sensor under a closed‐loop tactile scan, carried out with an industrial robotic arm, is used as latency‐coded spike trains in a spiking neural network (SNN) tempotron classifier system. With the capability of performing a real‐time binary naturalistic texture classification with a maximum accuracy of 99.45%, the presented bioinspired skin finds applications in robotics, prosthesis, wearable sensors, and medical devices.

Soft eSkin: distributed touch sensing with harmonized energy and computing

“…For example, the viscoelastic medium between contact point on the skin surface and the receptor deep inside the soft skin could alter the response of receptors. In addition, the neuroscience studies have confirmed the presence of plasticity or synaptic modulation in biological neural systems referring to the ability of synapse to weaken or strengthen the synaptic weights with respect to the tactile activity over the time [2][3][4][5][6][7]. These observations have profound implications for the development of future large area tactile skin.…”

“…Skin is the largest organ in the human body (overall surface area of 1.5-2.0 m 2 ) [1], which houses a network of distributed and energy-aware receptors at various depths in the soft tissues [2][3][4]. These receptors (mechanoreceptors (pressure/force), nociceptors (pain) and thermoreceptors (temperature)) enable us to feel and perceive various contact parameters [2][3][4] and give us the most vital of the five essential senses (sight, hearing, touch, smell, taste). Based on the stimuli the mechanoreceptors respond to, their classification as SAs (slow adapting) and FAs (fast adapting) is well known [2][3][4].…”

“…These receptors (mechanoreceptors (pressure/force), nociceptors (pain) and thermoreceptors (temperature)) enable us to feel and perceive various contact parameters [2][3][4] and give us the most vital of the five essential senses (sight, hearing, touch, smell, taste). Based on the stimuli the mechanoreceptors respond to, their classification as SAs (slow adapting) and FAs (fast adapting) is well known [2][3][4]. In daily tasks such as lifting an object (figure 1a), the FAs are active only during the phase transitions (e.g.…”

“…even under static or quasi-static conditions (figure 1b) [2][3][4][5][6][7]. The human skin therefore responds to both dynamic and static or quasi-static stimuli through FAs and SAs [2][3][4][5][6][7]. Since SAs continue to generate spikes throughout the static or quasistatic events (figure 1b), the neuromorphic approach in eSkin is fundamentally different from the event-driven approaches that are widely explored today in context with vision and other sensory modalities [2][3][4][8][9][10][11][12][13][14].…”

“…The efficiency gap between artificial systems and their biological counterpart could be bridged through the neuromorphic mechanisms [12]. As briefly discussed above, the event-driven neuromorphic approaches demonstrated for vision and auditory may not be useful for large area eSkin [2][3][4][11][12][13][14]. Thus, eSkin over large area requires new integration paradigm with distributed sensors (equivalent of SAs and FAs) and distributed energy efficient non-volatile memory (NVM) devices [3,10,21,29] to store the information to allow local computation [3,10,17,21,29].…”