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Investigating performance and usage of input methods for soft keyboard hotkeys

Fennedy, Katherine orcid logoORCID: 0000-0002-9643-2072, Malacria, Sylvain, Lee, Hyowon orcid logoORCID: 0000-0003-4395-7702 and Perrault, Simon orcid logoORCID: 0000-0002-3105-9350 (2020) Investigating performance and usage of input methods for soft keyboard hotkeys. In: 22nd International Conference on Human-Computer Interaction with Mobile Devices & Services (MobileHCI '20), 5-8 Oct 2020, Oldenburg, Germany (Online).

Abstract
Touch-based devices, despite their mainstream availability, do not support a unified and efficient command selection mechanism, available on every platform and application. We advocate that hotkeys, conventionally used as a shortcut mechanism on desktop computers, could be generalized as a command selection mechanism for touch-based devices, even for keyboard-less applications. In this paper, we investigate the performance and usage of soft keyboard shortcuts or hotkeys (abbreviated SoftCuts) through two studies comparing different input methods across sitting, standing and walking conditions. Our results suggest that SoftCuts not only are appreciated by participants but also support rapid command selection with different devices and hand configurations. We also did not find evidence that walking deters their performance when using the Once input method.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Additional Information:Article Number: 29
Uncontrolled Keywords:Hotkey; shortcut; soft keyboard; modifier-based shortcuts; command selection
Subjects:Computer Science > Interactive computer systems
Computer Science > Surfaces
Computer Science > Visualization
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of the 22nd International Conference on Human-Computer Interaction with Mobile Devices & Services (MobileHCI '20). . Association for Computing Machinery (ACM).
Publisher:Association for Computing Machinery (ACM)
Official URL:https://dx.doi.org/10.1145/3379503.3403552
Copyright Information:© 2020 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Agence Nationale de la Recherche (Discovery, ANR-19-CE33-0006) and by the Singapore Ministry of Education and Singapore University of Technology and Design (SUTD) Start-up Research Grant (T1SRIS18141).
ID Code:25838
Deposited On:11 May 2021 16:43 by Hyowon Lee . Last Modified 11 May 2021 16:43
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