Adaptability of Ultrasonic Lamb Wave Touchscreen to the Variations in Touch Force and Touch Area

Abstract

Previous studies on Lamb wave touchscreen (LWT) were carried out based on the assumption that the unknown touch had the consistent parameters with acoustic fingerprints in the reference database. The adaptability of LWT to the variations in touch force and touch area was investigated in this study for the first time. The automatic collection of the databases of acoustic fingerprints was realized with an experimental prototype of LWT employing three pairs of transmitter–receivers. The self-adaptive updated weight coefficient of the used transmitter–receiver pairs was employed to successfully improve the accuracy of the localization model established based on a learning method. The performance of the improved method in locating single- and two-touch actions with the reference database of different parameters was carefully evaluated. The robustness of the LWT to the variation of the touch force varied with the touch area. Moreover, it was feasible to locate touch actions of large area with reference databases of small touch areas as long as the unknown touch and the reference databases met the condition of equivalent averaged stress.

Machine Learning for Touch Localization on Ultrasonic Wave Touchscreen

“…Amongst the existing technologies [1,2,3], tactile surfaces based on ultrasonic guided waves answer the need for converting non-planar surfaces (including plastic material and transparent glass) into tactile sensing surfaces with high resolution and durability. Moreover, this technology supports multitouch detection and can estimate the contact pressure [4,5]. Acoustic based tactile sensors exploit the Surface Acoustic Waves (SAW) [6,7], or guided or Lamb Waves (LW) [8,4,9,10,11,12,13].…”

“…The SAW tactile screens are vulnerable to surface contaminants such as liquid and scratches, and has limitation for multiple-finger commands. On the other hand, Lamb Wave Touchscreen (LWT) technology supports multi-touch, which makes it more suitable for interfacing with smart devices [4,5]. LWT technologies are either passive [11,8,15,16,17] or active [4,9,10,12,13,18,19,20,21,22].…”

“…In active LWT, the Lamb waves are both generated and received by piezoceramic elements. Static actions are not taken into account in passive mode since they do not create the wave field, while the active mode can overcome this deficiency [5,21].…”

Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches

“…Touchscreen technologies can be categorized into finger-touch and stylus-touch methods. While finger-touch methods include resistive, capacitive, acoustic wave, and optical approaches [ 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 ], stylus-touch ones cover up to electromagnetic resonance (EMR) schemes including finger-touch methodologies [ 102 , 103 , 104 , 105 , 106 , 107 , 108 ]. Recently, as wearable devices such as smartwatches and smartbands are becoming more popular, small-size displays are becoming further widespread with touch sensing functionality.…”