Reference : How We Swipe: A Large-scale Shape-writing Dataset and Empirical Findings
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/47546
How We Swipe: A Large-scale Shape-writing Dataset and Empirical Findings
English
Leiva, Luis A. mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Kim, Sunjun []
Cui, Wenzhe []
Bi, Xiaojun []
Oulasvirta, Antti []
2021
Proceedings of the ACM International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI)
Yes
No
International
International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI)
September 27 - October 1, 2021
[en] 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.
http://hdl.handle.net/10993/47546
10.1145/3447526.3472059

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