  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.
