How We Swipe: A Large-scale Shape-writing Dataset and Empirical Findings
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A4 Artikkeli konferenssijulkaisussa
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Date
2021-09-27
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en
Pages
13
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Proceedings of MobileHCI 2021 - ACM International Conference on Mobile Human-Computer Interaction: Mobile Apart, MobileTogether
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.Description
Publisher Copyright: © 2021 Owner/Author.
Keywords
Dataset, Gesture Typing, Phrase set, Shape-writing, Swiping, Text Entry
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Citation
Leiva, L A, Kim, S, Cui, W, Bi, X & Oulasvirta, A 2021, How We Swipe : A Large-scale Shape-writing Dataset and Empirical Findings . in Proceedings of MobileHCI 2021 - ACM International Conference on Mobile Human-Computer Interaction : Mobile Apart, MobileTogether ., 11, ACM, International Conference on Human-Computer Interaction with Mobile Devices and Services, Virtual, Online, France, 27/09/2021 . https://doi.org/10.1145/3447526.3472059