Fast and precise touch-based text entry for head-mounted augmented reality with variable occlusion
Ola Kristensson, P
ACM Transactions on Computer-Human Interaction
Association for Computing Machinery (ACM)
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Dudley, J., Vertanen, K., & Ola Kristensson, P. (2018). Fast and precise touch-based text entry for head-mounted augmented reality with variable occlusion. ACM Transactions on Computer-Human Interaction, 25 (6)https://doi.org/10.1145/3232163
We present the VISAR keyboard: an augmented reality (AR) head-mounted display (HMD) system that supports text entry via a virtualised input surface. Users select keys on the virtual keyboard by imitating the process of single-hand typing on a physical touchscreen display. Our system uses a statistical decoder to infer users' intended text and to provide error-tolerant predictions. There is also a high-precision fall-back mechanism to support users in indicating which keys should be unmodified by the auto-correction process. A unique advantage of leveraging the well-established touch input paradigm is that our system enables text entry with minimal visual clutter on the see-through display, thus preserving the user’s field-of-view. We iteratively designed and evaluated our system and show that the final iteration of the system supports a mean entry rate of 17.75 wpm with a mean character error rate less than 1%. This performance represents a 19.6% improvement relative to the state-of-the-art baseline investigated: a gaze-then-gesture text entry technique derived from the system keyboard on the Microsoft HoloLens. Finally, we validate that the system is effective in supporting text entry in a fully mobile usage scenario likely to be encountered in industrial applications of AR HMDs.
Per Ola Kristensson was supported in part by a Google Faculty research award and EPSRC grants EP/N010558/1 and EP/N014278/1. Keith Vertanen was supported in part by a Google Faculty research award. John Dudley was supported by the Trimble Fund.
External DOI: https://doi.org/10.1145/3232163
This record's URL: https://www.repository.cam.ac.uk/handle/1810/283643