Manipulation, Learning, and Recall with Tangible Pen-Like Input
Lisa A. Elkin, Jean-Baptiste Beau, Géry Casiez, Daniel Vogel
Abstract
We examine two key human performance characteristics of a pen-like tangible input device that executes a different command depending on which corner, edge, or side contacts a surface. The manipulation time when transitioning between contacts is examined using physical mock-ups of three representative device sizes and a baseline pen mock-up. Results show the largest device is fastest overall and minimal differences with a pen for equivalent transitions. Using a hardware prototype able to sense all 26 different contacts, a second experiment evaluates learning and recall. Results show almost all 26 contacts can be learned in a two-hour session with an average of 94% recall after 24 hours. The results provide empirical evidence for the practicality, design, and utility for this type of tangible pen-like input.
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Install the CLIlune papers fulltext ed524b07-29e9-440a-95ec-db81f435cc84Cited by top-tier papers3
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- Deep Learning Super-Resolution Network Facilitating Fiducial Tangibles on Capacitive TouchscreensMarius Mihai Rusu, Sven MayerCHI 2023 · 8 citations
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