Reducing the Latency of Touch Tracking on Ad-Hoc Surfaces (ISS ’22)
Touch sensing on ad-hoc surfaces has the potential to transform everyday surfaces in the environment – desks, tables and walls – into tactile, touch-interactive surfaces, creating large, comfortable interactive spaces without the cost of large touch sensors. Depth sensors are a promising way to provide touch sensing on arbitrary surfaces, but past systems have suffered from high latency and poor touch detection accuracy. We apply a novel state machine-based approach to analyzing touch events, combined with a machine-learning approach to predictively classify touch events from depth data with lower latency and higher touch accuracy than previous approaches. Our system can reduce end-to-end touch latency to under 70ms, comparable to conventional capacitive touchscreens. Additionally, we open-source our dataset of over 30,000 touch events recorded in depth, infrared and RGB for the benefit of future researchers.
Xu, N. X. and Xiao, R. (2022). Reducing the Latency of Touch Tracking on Ad-hoc Surfaces. In Proceedings of the ACM on Human-Computer Interaction, Interactive Surfaces and Spaces (ISS ’22). ACM, New York, NY, USA. Article 577 (December 2022), 16 pages. DOI: 10.1145/3567730
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