Typing on an Invisible Keyboard

Zhu, Suwen; Luo, Tianyao; Bi, Xiaojun; Zhai, Shumin

doi:10.1145/3173574.3174013

Cited by 55 publications

(27 citation statements)

References 35 publications

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“…More radically, completely invisible keyboards have been tried. Zhu et al [28] introduced an invisible keyboard on mobile devices by utilizing a decent touch point decoder. Invisiboard [21] facilitated a 9-key (T9) invisible gesture keyboard on a smart watch.…”

Section: Related Workmentioning

confidence: 99%

Design and Evaluation of Semi-Transparent Keyboards on a Touchscreen Tablet

Kim

Lee

2018

Proceedings of the 2018 ACM International Conference on Interactive Surfaces and Spaces

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As tablet computers are hosting more productivity applications, efficient text entry is becoming more important. A soft keyboard, which is the primary text entry interface for tablets, however, often competes with applications for the limited screen space. A promising solution to this problem may be a semi-transparent soft keyboard (STK), which can share the screen with an application. A few commercial STK products are already available, but research questions about the STK design have not been explored in depth yet. Therefore, we conducted three experiments to answer 1) the effect of the transparency level on usability, 2) exploration of diverse design options for an STK, and 3) the effect of an STK on the different text caret positions. The results imply that STKs with 50% opacity showed a balanced performance; well-designed STKs were acceptable in both content reading and typing situations; which could reach 90-100% of an opaque keyboard in terms of overall performance; and the text caret could intrude the STK down to the number row.

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Section: Related Workmentioning

confidence: 99%

Design and Evaluation of Semi-Transparent Keyboards on a Touchscreen Tablet

Kim

Lee

2018

Proceedings of the 2018 ACM International Conference on Interactive Surfaces and Spaces

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“…The probability P (t i |c i ) is approximated by a Gaussian distribution with Markov-Bayesian algorithm [17] or a bivariate Gaussian distribution [25] in conventional approaches. In addition, the probability is separately modelled for left and right hands.…”

Section: Decoding Algorithmsmentioning

confidence: 99%

“…The instruction screen shows the task sentence (phrase) at the center. During typing, three types of feedback could be offered: none, asterisk [17], [25] and highlight. For highlight feedback which is evaluated in this study for the first time, each character of the task sentence gets background-highlighted step by step as a legal touch is detected.…”

Section: User Study: Understanding User Behaviormentioning

confidence: 99%

I-Keyboard: Fully Imaginary Keyboard on Touch Devices Empowered by Deep Neural Decoder

Kim

Yoo

Kim

2021

IEEE Trans. Cybern.

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Text-entry aims to provide an effective and efficient pathway for humans to deliver their messages to computers. With the advent of mobile computing, the recent focus of text-entry research has moved from physical keyboards to soft keyboards. Current soft keyboards, however, increase the typo rate due to lack of tactile feedback and degrade the usability of mobile devices due to their large portion on screens. To tackle these limitations, we propose a fully imaginary keyboard (I-Keyboard) with a deep neural decoder (DND). The invisibility of I-Keyboard maximizes the usability of mobile devices and DND empowered by a deep neural architecture allows users to start typing from any position on the touch screens at any angle. To the best of our knowledge, the eyes-free ten-finger typing scenario of I-Keyboard which does not necessitate both a calibration step and a predefined region for typing is first explored in this work. For the purpose of training DND, we collected the largest user data in the process of developing I-Keyboard. We verified the performance of the proposed I-Keyboard and DND by conducting a series of comprehensive simulations and experiments under various conditions. I-Keyboard showed 18.95% and 4.06% increases in typing speed (45.57 WPM) and accuracy (95.84%), respectively over the baseline.

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“…This includes the time spent by the user correcting errors. As in prior wordgesture typing work [85], we use UER. U ER = (MW D(S, P) × 100)/Len(P), where MWD is the minimum word distance between the transcribed phrase S and target phrase P, and L is the number of words in P. Since participants performed word-level corrections by deleting a word and retyping: CER is defined as CER = (W D × 100)/Len(P), where WD is the no.…”

Section: Hand-up Preliminary Study: Ptg Vs Pts Experiments Designmentioning

confidence: 99%