Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [31 references]  Display  Hide  Download
Contributor : Géry Casiez Connect in order to contact the contributor
Submitted on : Sunday, August 23, 2020 - 7:45:05 PM
Last modification on : Sunday, June 26, 2022 - 1:12:57 AM
Long-term archiving on: : Tuesday, December 1, 2020 - 6:48:22 PM


Files produced by the author(s)




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⟩



Record views


Files downloads