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Machine Learning for Touch Localization on an Ultrasonic Lamb Wave Touchscreen.

Sahar Bahrami¹, Jérémy Moriot^1,2, Patrice Masson¹

¹Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada.

Sensors (Basel, Switzerland)

|May 20, 2022

Summary

This summary is machine-generated.

Deep Neural Networks (DNNs) accurately locate touches on surfaces using ultrasonic waves, achieving high precision for human-machine interactions and keypad access control with rapid computation.

Keywords:

human–machine interface machine earning signal processing touch technology ultrasonic wave

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Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects

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Published on: September 1, 2016

Area of Science:

Robotics and Machine Learning
Acoustic Sensing and Signal Processing

Background:

Traditional touch localization methods often rely on complex signal processing techniques.
Developing robust and efficient touch localization for human-machine interfaces remains a challenge.

Purpose of the Study:

To investigate the efficacy of Deep Neural Networks (DNNs) for touch localization using ultrasonic guided waves.
To compare DNN performance against signal processing approaches in terms of accuracy and speed.

Main Methods:

A robotic finger was used to collect touch data for training a DNN model.
The DNN model was validated using experimental data from human finger touches.
Both classification and regression approaches were employed for touch localization and identification.

Main Results:

The DNN model achieved a mean localization error of 0.47 cm with a standard deviation of 0.18 cm in 0.44 ms.
Touch identification on a keypad layout using classification yielded 97% accuracy in 0.28 ms.
The DNN approach demonstrated satisfactory localization for human-machine interactions.

Conclusions:

Deep Neural Networks offer a viable and efficient alternative to traditional signal processing for ultrasonic touch localization.
DNN-based methods provide accurate and robust performance for tactile sensing applications.
The study highlights the potential of DNNs in advancing human-machine interaction technologies.