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.

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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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