Machine Learning for Touch Localization on Ultrasonic Wave Touchscreen

TL;DR

This paper demonstrates that deep neural networks can accurately localize touches on ultrasonic touchscreen surfaces, achieving low errors and high speed, and can also identify keypad touches with high accuracy.

Contribution

It introduces a DNN-based approach for ultrasonic touch localization and keypad identification, showing improved accuracy and speed over traditional signal processing methods.

Findings

Mean localization error of 0.47 cm

97% accuracy in keypad touch identification

Localization computation time of 0.44 ms

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 robotic finger first simulates the touch action and captures the data to train a model. The model is then validated with data from experiments conducted with human fingers. The localization root mean square errors (RMSE) in time and frequency domains are presented. 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 and a computing time of 0.44 ms. The classification approach is also adapted to identify touches on an access control keypad layout, which leads to an accuracy of 97% with a computing time of 0.28 ms. 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.