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How We Swipe: A Large-scale Shape-writing Dataset and Empirical Findings

Published: 27 September 2021 Publication History

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.

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cover image ACM Conferences
MobileHCI '21: Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction
September 2021
637 pages
ISBN:9781450383288
DOI:10.1145/3447526
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 27 September 2021

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Author Tags

  1. Dataset
  2. Gesture Typing
  3. Phrase set
  4. Shape-writing
  5. Swiping
  6. Text Entry

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MobileHCI '21
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MobileHCI '21: 23rd International Conference on Mobile Human-Computer Interaction
September 27 - October 1, 2021
Toulouse & Virtual, France

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