TriboTouch: Micro-Patterned Surfaces for Low Latency Touchscreens

要旨

Touchscreen tracking latency, often 80ms or more, creates a rubber-banding effect in everyday direct manipulation tasks such as dragging, scrolling, and drawing. This has been shown to decrease system preference, user performance, and overall realism of these interfaces. In this research, we demonstrate how the addition of a thin, 2D micro-patterned surface with 5 micron spaced features can be used to reduce motor-visual touchscreen latency. When a finger, stylus, or tangible is translated across this textured surface frictional forces induce acoustic vibrations which naturally encode sliding velocity. This acoustic signal is sampled at 192kHz using a conventional audio interface pipeline with an average latency of 28ms. When fused with conventional low-speed, but high-spatial-accuracy 2D touch position data, our machine learning model can make accurate predictions of real time touch location.

受賞
Honorable Mention
著者
Craig Shultz
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Daehwa Kim
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Karan Ahuja
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Chris Harrison
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3502069

動画

会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)

セッション: Input Techniques

292
5 件の発表
2022-05-03 20:00:00
2022-05-03 21:15:00