Abstract
A tactile position sensing system based on the sensing of acoustic waves and analyzing with artificial intelligence is proposed. The system comprises a thin steel plate with multiple piezoelectric transducers attached to the underside, to excite and detect Lamb waves (or plate waves). A data acquisition and control system synchronizes the wave excitation and detection and records the transducer signals. When the steel plate is touched by a finger, the waveform signals are perturbed by wave absorption and diffraction effects, and the corresponding changes in the output signal waveforms are sent to a convolutional neural network (CNN) model to predict the x- and y-coordinates of the finger contact position on the sensing surface. The CNN model is trained by using the experimental waveform data collected using an artificial finger carried by a three-axis motorized stage. The trained model is then used in a series of tactile sensing experiments performed using a human finger. The experimental results show that the proposed touch sensing system has an accuracy of more than 95%, a spatial resolution of 1 × 1 cm2, and a response time of 60 ms.
Highlights
Touchscreens are widely used throughout daily life for such applications as mobile phones, computers, and interactive machines
To deal with the challenging issue of localization algorithm in tactile position sensing, the waveform signals produced by the detection piezoelectric transducers of the touchscreen proposed in the present study are processed by convolutional neural network (CNN)
To facilitate tactile position sensing, acoustic waves are continuously waves are continuously excited in the plate by voltage signals, and the received wave signals are constantly excited in the plate by voltage signals, and the received wave signals are constantly monitored by a monitored by a personal computer (PC)
Summary
Touchscreens are widely used throughout daily life for such applications as mobile phones, computers, and interactive machines. Depending on how the wave energy is generated, Lamb-wave-based touchscreens can be classified as either passive or active In touchscreens of the former type, the Lamb waves are generated via the application of pressure to the sensing surface by the human finger and are detected by acoustic sensors strategically positioned on the plate [4,5,6]. While such devices have the Sensors 2020, 20, 2619; doi:10.3390/s20092619 www.mdpi.com/journal/sensors. To deal with the challenging issue of localization algorithm in tactile position sensing, the waveform signals produced by the detection piezoelectric transducers of the touchscreen proposed in the present study are processed by CNN. To the best of our knowledge, this is the first time the use of the deep machine learning algorithm of CNN to solve the ultrasonic tactile position sensing problems has been introduced
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