Self-Powered Pressure- and Vibration-Sensitive Tactile Sensors for Learning Technique-Based Neural Finger Skin.

Finger skin electronics are essential for realizing humanoid soft robots and/or medical applications that are very similar to human appendages. A selective sensitivity to pressure and vibration that are indispensable for tactile sensing is highly desirable for mimicking sensory mechanoreceptors in skin. Additionally, for a human-machine interaction, output signals of a skin sensor should be highly correlated to human neural spike signals. As a demonstration of fully mimicking the skin of a human finger, we propose a self-powered flexible neural tactile sensor (NTS) that mimics all the functions of human finger skin and that is selectively and sensitively activated by either pressure or vibration stimuli with laminated independent sensor elements. A sensor array of ultrahigh-density pressure (20 × 20 pixels on 4 cm2) of interlocked percolative graphene films is fabricated to detect pressure and its distribution by mimicking slow adaptive (SA) mechanoreceptors in human skin. A triboelectric nanogenerator (TENG) was laminated on the sensor array to detect high-frequency vibrations like fast adaptive (FA) mechanoreceptors, as well as produce electric power by itself. Importantly, each output signal for the SA- and FA-mimicking sensors was very similar to real neural spike signals produced by SA and FA mechanoreceptors in human skin, thus making it easy to convert the sensor signals into neural signals that can be perceived by humans. By introducing microline patterns on the top surface of the NTS to mimic structural and functional properties of a human fingerprint, the integrated NTS device was capable of classifying 12 fabrics possessing complex patterns with 99.1% classification accuracy.