Machine Learning for Touch Localization on Ultrasonic Wave Touchscreen

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 dat...

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Bibliographic Details
Published in:arXiv.org 2022-04
Main Authors: Bahrami, Sahar, Moriot, Jérémy, Masson, Patrice, Grondin, François
Format: Article
Language:English
Subjects:
Access control
Artificial neural networks
Classification
End effectors
Fingers
Localization
Machine learning
Signal processing
Touch screens
Online Access:Get full text
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Summary: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.
ISSN:2331-8422