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Conference Papers Year : 2020

Manipulation, Learning, and Recall with Tangible Pen-Like Input

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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Dates and versions

hal-02919664 , version 1 (23-08-2020)

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Lisa A Elkin, Jean-Baptiste Beau, Géry Casiez, Daniel Vogel. Manipulation, Learning, and Recall with Tangible Pen-Like Input. CHI 2020 - ACM Conference on Human Factors in Computing Systems, Apr 2020, Honolulu, United States. pp.1-12, ⟨10.1145/3313831.3376772⟩. ⟨hal-02919664⟩
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