Abstract
Despite the prevalence of shape-writing (gesture typing, swype input, or swiping for short) as a text entry method, there are currently no public datasets available. We report a large-scale dataset that can support efforts in both empirical study of swiping as well as the development of better intelligent text entry techniques. The dataset was collected via a web-based custom virtual keyboard, involving 1,338 users who submitted 11,318 unique English words. We report aggregate-level indices on typing performance, user-related factors, as well as trajectory-level data, such as the gesture path drawn on top of the keyboard or the time lapsed between consecutively swiped keys. We find some well-known effects reported in previous studies, for example that speed and error are affected by age and language skill. We also find surprising relationships such that, on large screens, swipe trajectories are longer but people swipe faster.
Original language | English |
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Title of host publication | Proceedings of MobileHCI 2021 - ACM International Conference on Mobile Human-Computer Interaction |
Subtitle of host publication | Mobile Apart, MobileTogether |
Publisher | Association for Computing Machinery, Inc |
ISBN (Electronic) | 9781450383288 |
DOIs | |
State | Published - 27 Sep 2021 |
Event | 23rd ACM International Conference on Mobile Human-Computer Interaction: Mobile Apart, MobileTogether, MobileHCI 2021 - Virtual, Online, France Duration: 27 Sep 2021 → 1 Oct 2021 |
Publication series
Name | Proceedings of MobileHCI 2021 - ACM International Conference on Mobile Human-Computer Interaction: Mobile Apart, MobileTogether |
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Conference
Conference | 23rd ACM International Conference on Mobile Human-Computer Interaction: Mobile Apart, MobileTogether, MobileHCI 2021 |
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Country/Territory | France |
City | Virtual, Online |
Period | 27/09/21 → 1/10/21 |
Bibliographical note
Publisher Copyright:© 2021 Owner/Author.
Keywords
- Dataset
- Gesture Typing
- Phrase set
- Shape-writing
- Swiping
- Text Entry