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.
This paper introduces a new text input device called the chording glove. The keys of a chord keyboard are mounted on the fingers of a glove. A chord can be made by pressing the fingers against any surface. Shift buttons placed on the index finger enable the glove to enter the full ASCII character set. The chording glove is designed as a text input device for wearable computers and virtual environments. An experiment was conducted to assess the performance of the glove. After an average of 80 min of a tutorial, ten subjects reached a continuous text input speed of 8.9 spl plusmn 1.4 words min, and after 10 1-hr sessions, they achieved 16.8 spl plusmn 2.5 words min. Previously we demonstrated that after 400 minutes of practice, ten novices averaged over 26 words per minute (wpm) for text entry on the Twiddler one-handed chording keyboard, outperforming the multitap mobile text entry standard. We present a study that examines expert chording performance. Our five participants achieved an average rate of 47 wpm after approximately 25 hours of practice in varying conditions. One subject achieved a rate of 67 wpm, equivalent to the typing rate of the last author who has been a Twiddler user for ten years. We analyze the effects of learning on various aspects of chording, provide evidence that lack of visual feedback does not hinder expert typing speed and examine the potential use of multicharacter chords (MCCs) to increase text entry speed.  Touch typing on flat surfaces (e.g. interactive tabletop) is challenging due to lack of tactile feedback and hand drifting. In this paper, we present TOAST, an eyes-free keyboard technique for enabling efficient touch typing on touch-sensitive surfaces. We first formalized the problem of keyboard parameter (e.g. location and size) estimation based on users' typing data. Through a user study, we then examined users' eyes-free touch typing behavior on an interactive tabletop with only asterisk feedback. We fitted the keyboard model to the typing data, results suggested that the model parameters (keyboard location and size) changed not only between different users, but also within the same user along with time. Based on the results, we proposed a Markov-Bayesian algorithm for input prediction, which considers the relative location between successive touch points within each hand respectively. Simulation results showed that based on the pooled data from all users, this model improved the top-1 accuracy of the classical statistical decoding algorithm from 86.2 to 92.1 . In a second user study, we further improved TOAST with dynamical model parameter adaptation, and evaluated users' text entry performance with TOAST using realistic text entry tasks. Participants reached a pick-up speed of 41.4 WPM with a character-level error rate of 0.6 . And with less than 10 minutes of practice, they reached 44.6 WPM without sacrificing accuracy. Participants' subjective feedback also indicated that TOAST offered a natural and efficient typing experience. In a mobile environment, the amount of visual attention a person can devote to a computer is often limited. In addition to typing rapidly and accurately, it is important to be able to enter text with limited visual feedback. Previously we found that users can effectively type in such "blind" conditions with the Twiddler one-handed keyboard. In this paper we examine blind typing on mini-QWERTY keyboards and introduce a taxonomy for blind mobile text Input. We present a study in which eight expert mini-QWERTY typists participated in 5 typing sessions. Each session consists of three twenty minute typing conditions. In the first condition, the control or "normal" condition, the participant had full visual access to both the keyboard and the display. In the second condition, "single blind" we obstructed view of the keyboard. The final "double blind" condition also reduced visual feedback from the display. In contrast to our Twiddler work, we found that in the visually impaired conditions, typing rate and accuracy suffer, never reaching the non-blind rates. Across the blind mini-QWERTY conditions our participants averaged 45.8 wpm at 85.6 accuracy, while blind typing on the Twiddler averaged 47.3 wpm at 93.9 accuracy. We discuss these results in the context of our previous blind typing work and examine the trade-offs between the different keyboards for mobile and wearable computer use.