A Thumb Stroke-Based Virtual Keyboard for Sight-Free Text Entry on Touch-Screen Mobile Phones

The use of QWERTY on most of the current mobile devices for text entry usually requires users’ full visual attention and both hands, which is not always possible due to situational or physical impairments of users. Prior research has shown that users prefer to hold and interact with a mobile device with a single hand when possible, which is challenging and poorly supported by current mobile devices. We propose a novel thumb-stroke based keyboard called ThumbStroke, which can support both sight-free and one-handed text entry on touch-screen mobile devices. Selecting a character for text entry via ThumbStroke completely relies on the directions of thumb movements at anywhere on a device screen. We evaluated ThumbStroke through a longitudinal lab experiment including 20 sessions with 13 participants. ThumbStroke shows advantages in typing accuracy and user perceptions in comparison to Escape and QWERTY and results in faster typing speed than QWERTY for sight-free text entry

Velocitap: Investigating fast mobile text entry using sentence-based decoding of touchscreen keyboard input

We present VelociTap: a state-of-the-art touchscreen keyboard decoder that supports a sentence-based text entry approach. VelociTap enables users to seamlessly choose from three word-delimiter actions: pushing a space key, swiping to the right, or simply omitting the space key and letting the decoder infer spaces automatically. We demonstrate that VelociTap has a significantly lower error rate than Google’s keyboard while retaining the same entry rate. We show that intermediate visual feedback does not significantly affect entry or error rates and we find that using the space key results in the most accurate results. We also demonstrate that enabling flexible word-delimiter options does not incur an error rate penalty. Finally, we investigate how small we can make the keyboard when using VelociTap. We show that novice users can reach a mean entry rate of 41 wpm on a 40mm wide smartwatch-sized keyboard at a 3% character error rate.This is the accepted manuscript. The final version is available from ACM at http://dl.acm.org/citation.cfm?id=2702135

VelociWatch: Designing and evaluating a virtual keyboard for the input of challenging text

© 2019 Association for Computing Machinery. Virtual keyboard typing is typically aided by an auto-correct method that decodes a user’s noisy taps into their intended text. This decoding process can reduce error rates and possibly increase entry rates by allowing users to type faster but less precisely. However, virtual keyboard decoders sometimes make mistakes that change a user’s desired word into another. This is particularly problematic for challenging text such as proper names. We investigate whether users can guess words that are likely to cause auto-correct problems and whether users can adjust their behavior to assist the decoder. We conduct computational experiments to decide what predictions to ofer in a virtual keyboard and design a smartwatch keyboard named VelociWatch. Novice users were able to use the features of VelociWatch to enter challenging text at 17 words-per-minute with a corrected error rate of 3%. Interestingly, they wrote slightly faster and just as accurately on a simpler keyboard with limited correction options. Our fnding suggest users may be able to type dif-fcult words on a smartwatch simply by tapping precisely without the use of auto-correct

The Impact of Word, Multiple Word, and Sentence Input on Virtual Keyboard Decoding Performance

Entering text on non-desktop computing devices is often done via an onscreen virtual keyboard. Input on such keyboards normally consists of a sequence of noisy tap events that specify some amount of text, most commonly a single word. But is single word-at-a-time entry the best choice? This paper compares user performance and recognition accuracy of wordat- a-time, phrase-at-a-time, and sentence-at-a-time text entry on a smartwatch keyboard. We evaluate the impact of differing amounts of input in both text copy and free composition tasks. We found providing input of an entire sentence significantly improved entry rates from 26wpm to 32wpm while keeping character error rates below 4%. In offline experiments with more processing power and memory, sentence input was recognized with a much lower 2.0% error rate. Our findings suggest virtual keyboards can enhance performance by encouraging users to provide more input per recognition event.This work was supported by Google Faculty awards (K.V. and P.O.K.

Next-Generation text entry

Users are reluctant to learn new text entry methods, as they demand a substantial training investment. Allowing users to flexibly combine multiple existing probabilistic text entry modalities is a way to still provide performance benefits to users.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/MC.2015.18

Improving the Accuracy of Mobile Touchscreen QWERTY Keyboards

In this thesis we explore alternative keyboard layouts in hopes of finding one that increases the accuracy of text input on mobile touchscreen devices. In particular, we investigate if a single swap of 2 keys can significantly improve accuracy on mobile touchscreen QWERTY keyboards. We do so by carefully considering the placement of keys, exploiting a specific vulnerability that occurs within a keyboard layout, namely, that the placement of particular keys next to others may be increasing errors when typing. We simulate the act of typing on a mobile touchscreen QWERTY keyboard, beginning with modeling the typographical errors that can occur when doing so. We then construct a simple autocorrector using Bayesian methods, describing how we can autocorrect user input and evaluate the ability of the keyboard to output the correct text. Then, using our models, we provide methods of testing and define a metric, the WAR rating, which provides us a way of comparing the accuracy of a keyboard layout. After running our tests on all 325 2-key swap layouts against the original QWERTY layout, we show that there exists more than one 2-key swap that increases the accuracy of the current QWERTY layout, and that the best 2-key swap is i ↔ t, increasing accuracy by nearly 0.18 percent

Shortlinks and tiny keyboards: a systematic exploration of design trade-offs in link shortening services

Link-shortening services save space and make the manual entry of URLs less onerous. Short links are often included on printed materials so that people using mobile devices can quickly enter URLs. Although mobile transcription is a common use-case, link-shortening services generate output that is poorly suited to entry on mobile devices: links often contain numbers and capital letters that require time consuming mode switches on touch screen keyboards. With the aid of computational modeling, we identified problems with the output of a link-shortening service, bit.ly. Based on the results of this modeling, we hypothesized that longer links that are optimized for input on mobile keyboards would improve link entry speeds compared to shorter links that required keyboard mode switches. We conducted a human performance study that confirmed this hypothesis. Finally, we applied our method to a selection of different non-word mobile data-entry tasks. This work illustrates the need for service design to fit the constraints of the devices people use to consume services