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.

Keywords
Pen Input
Tangible Interfaces
Learning
Command Selection
Authors
Lisa A. Elkin
University of Washington & University of Waterloo, Seattle, WA, USA
Jean-Baptiste Beau
University of Waterloo, Waterloo, ON, Canada
Géry Casiez
Univ. Lille, UMR 9189 - CRIStAL & Inria & Institut Universitaire de France (IUF) & University of Waterloo, Villeneuve d'Ascq, France
Daniel Vogel
University of Waterloo, Waterloo, ON, Canada
DOI

10.1145/3313831.3376772

Paper URL

https://doi.org/10.1145/3313831.3376772

Video

Conference: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

Session: Understanding & modeling users

Paper session
316C MAUI
5 items in this session
2020-04-30 11:00:00
2020-04-30 12:15:00
Japanese summary
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