Hands-free multi-type character text entry in virtual reality

被引:3
|
作者
Wan, Tingjie [1 ]
Shi, Rongkai [1 ]
Xu, Wenge [2 ]
Li, Yue [1 ]
Atkinson, Katie [3 ]
Yu, Lingyun [1 ]
Liang, Hai-Ning [1 ]
机构
[1] Xian Jiaotong Liverpool Univ, Dept Comp, Suzhou, Peoples R China
[2] Birmingham City Univ, DMT Lab, Birmingham, England
[3] Univ Liverpool, Dept Comp Sci, Liverpool, England
基金
中国国家自然科学基金;
关键词
Virtual reality; Text entry; Hands-free interaction; Multi-type character entry; Mode switching; Keyboard layout; User study; AUTHENTICATION; EMOTICONS;
D O I
10.1007/s10055-023-00902-z
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Multi-type characters, including uppercase and lowercase letters, symbols, and numbers, are essential in text entry activities. Although multi-type characters are used in passwords, instant messages, and document composition, there has been limited exploration of multi-character text entry for virtual reality head-mounted displays (VR HMDs). Typically, multi-type character entry requires four kinds of keyboards between which users need to switch. In this research, we explore hands-free approaches for rapid multi-type character entry. Our work explores two efficient and usable hands-free approaches for character selection: eye blinks and dwell. To enable quick switching between keyboards, we leverage the usability and efficiency of continuous head motions in the form of cross-based activation. In a pilot study, we explored the usability and efficiency of four locations of the switch keys, the two hands-free selection mechanisms, and crossing-based switching. In the main experiment, we evaluated four user-inspired layouts designed according to the findings from the pilot study. Results show that both blinking and dwell can work well with crossing-based switching and could lead to a relatively fast text entry rate (5.64 words-per-minute (WPM) with blinking and 5.42 WPM with dwell) with low errors (lower than 3% not corrected error rate (NCER)) for complex 8-digit passwords with upper/lowercase letters, symbols, and numbers. For sentences derived from the Brown Corpus, participants can reach 8.48 WPM with blinking and 7.78 WPM with dwell. Overall, as a first exploration, our results show that it is usable and efficient to perform hands-free text entry in VR using either eye blinks or dwell for character selection and crossing for mode switching.
引用
收藏
页数:19
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