Improving Two-Thumb Touchpad Typing in Virtual Reality
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Two-Thumb Touchpad Typing (4T) using hand-held controllers is one of the common text entry techniques in Virtual Reality (VR). However, its performance is far below that of two-thumb typing on a smartphone. We explored the possibility of improving its performance focusing on the following two factors: the visual feedback of hovering thumbs and the grip stability of the controllers. We examined the effects of these two factors on the performance of 4T in VR in user experiments. Their results show that hover feedback had a significant main effect on the 4T performance, but grip stability did not. We then investigated the achievable performance of the final 4T design in a longitudinal study, and its results show that users could achieve a typing speed over 30 words per minute after two hours of practice.Keywords:
Touchpad
Text entry
Words per minute
Two-Thumb Touchpad Typing (4T) using hand-held controllers is one of the common text entry techniques in Virtual Reality (VR). However, its performance is far below that of two-thumb typing on a smartphone. We explored the possibility of improving its performance focusing on the following two factors: the visual feedback of hovering thumbs and the grip stability of the controllers. We examined the effects of these two factors on the performance of 4T in VR in user experiments. Their results show that hover feedback had a significant main effect on the 4T performance, but grip stability did not. We then investigated the achievable performance of the final 4T design in a longitudinal study, and its results show that users could achieve a typing speed over 30 words per minute after two hours of practice.
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Words per minute
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Split keyboards are widely used on hand-held touchscreen devices (e.g., tablets). However, typing on a split keyboard often requires eye movement and attention switching between two halves of the keyboard, which slows users down and increases fatigue. We explore peripheral typing, a superior typing mode in which a user focuses her visual attention on the output text and keeps the split keyboard in peripheral vision. Our investigation showed that peripheral typing reduced attention switching, enhanced user experience and increased overall performance (27 WPM, 28% faster) over the typical eyes-on typing mode. This typing mode can be well supported by accounting the typing behavior in statistical decoding. Based on our study results, we have designed GlanceType, a text entry system that supported both peripheral and eyes-on typing modes for real typing scenario. Our evaluation showed that peripheral typing not only well co-existed with the existing eyes-on typing, but also substantially improved the text entry performance. Overall, peripheral typing is a promising typing mode and supporting it would significantly improve the text entry performance on a split keyboard.
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Each year people spend a huge amount of time typing. The text people type typically contains a tremendous amount of redundancy due to predictable word usage patterns and the text's structure. This paper describes a neural network system call AutoTypist that monitors a person's typing and predicts what will be entered next. AutoTypist displays the most likely subsequent word to the typist, who can accept it with a single keystroke, instead of typing it in its entirety. The multi-layer perceptron at the heart of AutoTypist adapts its predictions of likely subsequent text to the user's word usage pattern, and to the characteristics of the text currently being typed. Increases in typing speed of 2-3% when typing English prose and 10-20% when typing C code have been demonstrated using the system, suggesting a potential time savings of more than 20 hours per user per year. In addition to increasing typing speed, AutoTypist reduces the number of keystrokes a user must type by a similar amount (2-3% for English, 10- 20% for computer programs). This keystroke savings has the potential to significantly reduce the frequency and severity of repeated stress injuries caused by typing, which are the most common injury suffered in today's office environment.
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A prototype keyboard system was developed, using off-the-shelf hardware and software, as an inexpensive keyboard-based system to facilitate data entry for single-finger and typing-stick typists. Evaluation established that the system can increase entry rate by 50 percent or more. The underlying concepts may provide a basis for developing other configurations that accelerate and simplify computer keyboard use for persons with a variety of hand impairments.
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Text entry for smart eyewear is generally limited to speech-based input due to constraints of the input channels. However, many smart eyewear devices are now including a side touchpad making gesture-based text entry feasible. The Swipeboard technique, recently proposed for ultra-small touch screens such as smart watches, may be particularly suitable for smart eyewear: unlike other recent text-entry techniques for small devices, it supports eyes-free input. We investigate the limitations and feasibility of implementing Swipeboard on smart eyewear, using the side touch pad for input. Our first study reveals usability and recognition problems of using the side touch pad to perform the required gestures. To address these problems, we propose SwipeZone, which replaces diagonal gestures with zone-specific swipes. In a text entry study, we show that our redesign achieved a WPM rate of 8.73, 15.2% higher than Swipeboard, with a statistically significant improvement in the last half of the study blocks.
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Individual characters and text are the main inputs in many computing devices. Currently there is a growing trend in developing small portable devices like mobile phones, personal digital assistants, GPS-navigators, and two-way pagers. Unfortunately these portable computing devices have different user interfaces and therefore the task of text input takes many forms. The user, who in the future is likely to have several of these devices, has to learn several text input methods. We argue that there is a need for a universal text input method. A method like this would work on a wide range of interface technologies and allow the user to transfer his or her writing skill without device-specific training. To show that device independent text input is possible, we present a candidate for a device independent text entry method that supports skill transfer between different devices. A limited longitudinal study was conducted to achieve a proof of concept evaluation of our Minimal Device Independent Text Input Method (MDITIM). We found MDITIM writing skill acquired with a touchpad to work almost equally well on mouse, trackball, joystick and keyboard without any additional training. Our test group reached on average 41% of their handwriting speed by the end of the tenth 30-minute training session.
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While text entry is an essential and frequent task in Augmented Reality (AR) applications, devising an efficient and easy-to-use text entry method for AR remains an open challenge. This research presents STAR, a smartphone-analogous AR text entry technique that leverages a user's familiarity with smartphone two-thumb typing. With STAR, a user performs thumb typing on a virtual QWERTY keyboard that is overlain on the skin of their hands. During an evaluation study of STAR, participants achieved a mean typing speed of 21.9 WPM (i.e., 56% of their smartphone typing speed), and a mean error rate of 0.3% after 30 minutes of practice. We further analyze the major factors implicated in the performance gap between STAR and smartphone typing, and discuss ways this gap could be narrowed.
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Text entry is expected to be a common task for smart glass users, which is generally performed using a touchpad on the temple or by a promising approach using eye tracking. However, each approach has its own limitations. For more efficient text entry, we present the concept of gaze-assisted typing (GAT), which uses both a touchpad and eye tracking. We initially examined GAT with a minimal eye input load, and demonstrated that the GAT technology was 51% faster than a two-step touch input typing method (i.e.,M-SwipeBoard: 5.85 words per minute (wpm) and GAT: 8.87 wpm). We also compared GAT methods with varying numbers of touch gestures. The results showed that a GAT requiring five different touch gestures was the most preferred, although all GAT techniques were equally efficient. Finally, we compared GAT with touch-only typing (SwipeZone) and eye-only typing (adjustable dwell) using an eye-trackable head-worn display. The results demonstrate that the most preferred technique, GAT, was 25.4% faster than the eye-only typing and 29.4% faster than the touch-only typing (GAT: 11.04 wpm, eye-only typing: 8.81 wpm, and touch-only typing: 8.53 wpm).
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Relatively little is known about eye and finger movement in typing with mobile devices. Most prior studies of mobile typing rely on log data, while data on finger and eye movements in typing come from studies with physical keyboards. This paper presents new findings from a transcription task with mobile touchscreen devices. Movement strategies were found to emerge in response to sharing of visual attention: attention is needed for guiding finger movements and detecting typing errors. In contrast to typing on physical keyboards, visual attention is kept mostly on the virtual keyboard, and glances at the text display are associated with performance. When typing with two fingers, although users make more errors, they manage to detect and correct them more quickly. This explains part of the known superiority of two-thumb typing over one-finger typing. We release the extensive dataset on everyday typing on smartphones.
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