Hidekuni Takao
Full Professor & Director, Kagawa University, JAPAN
Title: Reproducing our Fingertip Sensation by Super High Resolution Tactile Sensing
Biography
Biography: Hidekuni Takao
Abstract
We, human has very sophisticated sense of touch on our fingertip skin. We can recognize and distinguish various and delicate difference of touch feelings obtained by sweep motion of fingertip on various kinds of materials and objects. Fingertip skin has the highest density of force and vibration receptors (Meissner’s Corpuscles and Merkel Cells) under the surface skin where fine pitch patterns of fingerprint are formed on. Human’s fingertip has a very high spatial resolution below 100µm and can recognize existence of 13nm-pitch patterns as reported recently.
In order to reproduce artificial sense of touch like human's fingertip, very high performances on spatial resolution and sensitivity are required to tactile sensors. In this talk, silicon based MEMS tactile sensors with a ultra-high force and spatial resolutions are introduced and demonstrated. All the mechanical structures in the tactile sensor deice are made from “pure” single crystal silicon layer of SOI wafers. No elastomer/polymer structures are used in the sensing structure. The contactor parts of the tactile sensor have curved shape which is very similar to the cross-section of a fingerprint, and its suspension springs are designed similarly to a spring constant of human’s fingertip skin surface. In the latest version of our tactile sensors, six contactors with fingerprint-like shape are integrated at a pitch of 500µm to get high spatial resolution tactile images. Each fingerprint-like contactor reproduces vertical motion (by micro roughness) and horizontal motion (by frictional force) of a fingerprint closely under sweeping motion of fingertip in measurement. Spatial resolution of our tactile sensor reaches to sub-micron region, and its force resolution of friction is below 50µN. These performances are enough high to analyze surface touch feelings of “Hair surface condition”, “Skin condition”, and “Touch feeling of various papers and clothes” like human fingertip. Machine learning based on deep neural network has been applied to the signal from the high resolution tactile sensors. As a result, 10 kinds of “cloth” samples have been discriminated at a correct percentage of 99% successfully. Combination of high resolution tactile sensor and deep neural network is a strong approach to reproduce human fingertip sensation by state-of-the-art device electron device technology.