Alphanumeric text entry is a challenge for Virtual Reality (VR) applications. VR enables new capabilities, impossible in the real world, such as an unobstructed view of the keyboard, without occlusion by the user's physical hands. Several hand representations have been proposed for typing in VR on standard physical keyboards. However, to date, these hand representations have not been compared regarding their performance and effects on presence for VR text entry. Our work addresses this gap by comparing existing hand representations with minimalistic fingertip visualization. We study the effects of four hand representations (no hand representation, inverse kinematic model, fingertip visualization using spheres and video inlay) on typing in VR using a standard physical keyboard with 24 participants. We found that the fingertip visualization and video inlay both resulted in statistically significant lower text entry error rates compared to no hand or inverse kinematic model representations. We found no statistical differences in text entry speed.
The mainstream mobile phone touchscreen keyboard might be a good text entry option @cite_14 . It is portable, and can generate a relatively high text entry rate @cite_17 . While we do not investigate this small type of touchscreen keyboard in this paper, we do believe it has potential. Currently, a limitation with using a phone is its small size, which combined with the display resolution limitation does not generate a good experience.
@cite_14 @cite_17
2010376411 2128267659 2618581749 2169710492
We study the performance and user experience of two popular mainstream mobile text entry methods: the Smart Touch Keyboard (STK) and the Smart Gesture Keyboard (SGK). Our first study is a lab-based ten-session text entry experiment. In our second study we use a new text entry evaluation methodology based on the experience sampling method (ESM). In the ESM study, participants installed an Android app on their own mobile phones that periodically sampled their text entry performance and user experience amid their everyday activities for four weeks. The studies show that text can be entered at an average speed of 28 to 39 WPM, depending on the method and the user's experience, with 1.0 to 3.6 character error rates remaining. Error rates of touchscreen input, particularly with SGK, are a major challenge; and reducing out-of-vocabulary errors is particularly important. Both SGK and STK have strengths, weaknesses, and different individual awareness and preferences. Two-thumb touch typing in a focused setting is particularly effective on STK, whereas one-handed SGK typing with the thumb is particularly effective in more mobile situations. When exposed to both, users tend to migrate from STK to SGK. We also conclude that studies in the lab and in the wild can both be informative to reveal different aspects of keyboard experience, but used in conjunction is more reliable in comprehensively assessing input technologies of current and future generations. Mobile devices gain increasing computational power and storage capabilities, and there are already mobile phones that can show movies, act as digital music players and offer full-scale web browsing. The bottleneck for information flow is however limited by the inefficient communication channel between the user and the small device. The small mobile phone form factor has proven to be surprisingly difficult to overcome and limited text entry capabilities are in effect crippling mobile devices’ use experience. The desktop keyboard is too large for mobile phones, and the keypad too limited. In recent years, advanced mobile phones have come equipped with touch-screens that enable new text entry solutions. This dissertation explores how software keyboards on touch-screens can be improved to provide an efficient and practical text and command entry experience on mobile devices. The central hypothesis is that it is possible to combine three elements: software keyboard, language redundancy and pattern recognition, and create new effective interfaces for text entry and control. These are collectively called “shape writing” interfaces. Words form shapes on the software keyboard layout. Users write words by articulating the shapes for words on the software keyboard. Two classes of shape writing interfaces are developed and analyzed: discrete and continuous shape writing. The former recognizes users’ pen or finger tapping motion as discrete patterns on the touch-screen. The latter recognizes users’ continuous motion patterns. Experimental results show that novice users can write text with an average entry rate of 25 wpm and an error rate of 1 after 35 minutes of practice. An accelerated novice learning experiment shows that users can exactly copy a single well-practiced phrase with an average entry rate of 46.5 wpm, with individual phrase entry rate measurements up to 99 wpm. When used as a control interface, users can send commands to applications 1.6 times faster than using de-facto standard linear pull-down menus. Visual command preview leads to significantly less errors and shorter gestures for unpracticed commands. Taken together, the quantitative results show that shape writing is among the fastest mobile interfaces for text entry and control, both initially and after practice, that are currently known. Abstract Typing on tiny QWERTY keyboards on smartwatches is considered challenging or even impractical due to the limited screen space. In this paper, we describe three user studies undertaken to investigate users’ typing abilities and preferences on tiny QWERTY keyboards. The first two studies, using a smartphone as a substitute for a smartwatch, tested five different keyboard sizes (2, 2.5, 3, 3.5 and 4 cm). Study 1 collected typing data from participants using keyboards and given asterisk feedback. We analyzed both the distribution of touch points (e.g., the systematic offset and shape of the distribution) and the effect of keyboard size. Study 2 adopted a Bayesian algorithm based on a touch model derived from Study 1 and a unigram word language model to perform input prediction. We found that on the smart keyboard, participants could type between 26.8 and 33.6 words per minute (WPM) across the five keyboard sizes with an uncorrected character error rate ranging from 0.4 to 1.9 . Participants’ subjective feedback indicated that they felt most comfortable with keyboards larger than 2.5 cm. Study 3 replicated the 3.0 and 3.5 cm keyboard tests on a real smartwatch and verified that in terms of text entry speed, error rate and user preference, there was no significant difference between the results measured on a smartphone and that on a smartwatch with same sized keys. This study result indicated that the results of Study 1 and 2 are applicable to smartwatch devices. Finally, we conducted a simulation to investigate the performance of different touch language models based on our collected data. The results showed that using either a bigram language model or a detailed touch model can effectively correct imprecision in users’ input. Our results suggest that achieving satisfactory levels of text input on tiny QWERTY keyboards is possible. Text entry rates are explored for several variations of soft keyboards. We present a model to predict novice and expert entry rates and present an empirical test with 24 subjects. Six keyboards were examined: the Qwerty, ABC, Dvorak, Fitaly, JustType, and telephone. At 8-10 wpm, novice predictions are low for all layouts because the dominant factor is the visual scan time, rather than the movement time. Expert predictions are in the range of 22-56 wpm, although these were not tested empirically. In a quick, novice test with a representative phrase of text, subjects achieved rates of 20.2 wpm (Qwerty), 10.7 wpm (ABC), 8.5 wpm (Dvorak), 8.0 wpm (Fitaly), 7.0 wpm (JustType), and 8.0 wpm (telephone). The Qwerty rate of 20.2 wpm is consistent with observations in other studies. The relatively high rate for Qwerty suggests that there is skill transfer from users' familiarity with desktop computers to the stylus tapping task.