Machine Learning for Touch Localization on Ultrasonic Wave Touchscreen

Abstract

Classification and regression employing a simple Deep Neural Network (DNN) are investigated to perform touch localization on a tactile surface using ultrasonic guided waves. A model is trained using data captured using a robotic finger, and validated with experiments conducted with human fingers. The proposed method provides satisfactory localization results for most human-machine interactions, with a mean error of 0.47 cm and standard deviation of 0.18 cm. The classification approach is also adapted to identify touches on an access control keypad layout, which leads to an accuracy of 97%. These results demonstrate that DNN-based methods are a viable alternative to signal processing-based approaches for accurate and robust touch localization using ultrasonic guided waves.

Fabrication and Characterization of a Highly Sensitive and Flexible Tactile Sensor Based on Indium Zinc Oxide (IZO) with Imprecise Data Analysis

“…The tactile sensor senses the externally applied mechanical force's position and time and strength in the resolution of micron levels through the processing technology and produces useful data. 4 There are various sensing approaches used for developing tactile sensors such as capacitive type, 5 resistive type, 6 surface acoustic wave (SAW) type, 7 and optical using infrared (IR). 8 Numerous materials have been used in the fabrication of tactile sensors.…”