Fitts ’ law models the inherent speed-accuracy trade-off constraint in stylus typing. Users attempting to go beyond the Fitts ’ law speed ceiling will tend to land the stylus outside the targeted key, resulting in erroneous words and increasing users ’ frustration. We propose a geometric pattern matching technique to overcome this problem. Our solution can be used either as an enhanced spell checker or as a way to enable users to escape the Fitts ’ law constraint in stylus typing, potentially resulting in higher text entry speeds than what is currently theoretically modeled. We view the hit points on a stylus keyboard as a high resolution geometric pattern. This pattern can be matched against patterns formed by the letter key center positions of legitimate words in a lexicon. We present the development and evaluation of an “elastic ” stylus keyboard capable of correcting words even if the user misses all the intended keys, as long as the user’s tapping pattern is close enough to the intended word.

The user expectations for usability and personalization along with decreasing size of handheld devices challenge traditional keypad layout design. We have developed a method for on-line adaptation of a touch pad keyboard layout. The method starts from an original layout and monitors the usage of the keyboard by recording and analyzing the keystrokes. An on-line learning algorithm subtly moves the keys according to the spatial distribution of keystrokes. In consequence, the keyboard matches better to the user’s physical extensions and grasp of the device, and makes the physical trajectories during typing more comfortable. We present two implementations that apply different vector quantization algorithms to produce an adaptive keyboard with visual on-line feedback. Both qualitative and quantitative results show that the changes in the keyboard are consistent, and related to the user's handedness and hand extensions. The testees found the on-line personalization positive. The method can either be applied for on-line personalization of keyboards or for ergonomics research.

We present Parakeet, a system for continuous speech recognition on mobile touch-screen devices. The design of Parakeet was guided by computational experiments and validated by a user study. Participants had an average text entry rate of 18 words-per-minute (WPM) while seated indoors and 13 WPM while walking outdoors. In an expert pilot study, we found that speech recognition has the potential to be a highly competitive mobile text entry method, particularly in an actual mobile setting where users are walking around while entering text. Author Keywords Continuous speech recognition, mobile text entry, text input, touch-screen interface, error correction, speech input, word confusion network, predictive keyboard ACM Classification Keywords H5.2. User Interfaces: Voice I/O

■ For text entry methods to be useful they have to deliver high entry rates and low error rates. At the same time they need to be easy to learn and provide effective means of correcting mistakes. Intelligent text entry methods combine AI techniques with human-computer interaction (HCI) theory to enable users to enter text as efficiently and effortlessly as possible. Here I sample a selection of such techniques from the research literature and set them into their historical context. I then highlight five challenges for text entry methods that aspire to make an impact in our society: localization, error correction, editor support, feedback, and context of use.

Abstract. Mobile devices often utilize touchscreen keyboards for text input. However, due to the lack of tactile feedback and generally small key sizes, users often produce typing errors. Key-target resizing, which dynamically adjusts the underlying target areas of the keys based on their probabilities, can significantly reduce errors, but requires training data in the form of touch points for intended keys. In this paper, we introduce Text Text Revolution (TTR), a game that helps users improve their typing experience on mobile touchscreen keyboards in three ways: first, by providing targeting practice, second, by highlighting areas for improvement, and third, by generating ideal training data for key-target resizing as a side effect of playing the game. In a user study, participants who played 20 rounds of TTR not only improved in accuracy over time, but also generated useful data for key-target resizing. To demonstrate usefulness, we trained key-target resizing on touch points collected from the first 10 rounds, and simulated how participants would have performed had personalized key-target resizing been used in the second 10 rounds. Key-target resizing reduced errors by 21.4%.

This paper describes a “relative keyboard, ” where keystrokes are treated as inputs in a continuous space relative to each other, instead of a discrete, unambiguous sequence. A user with the ability to touch-type may type anywhere on the sensing surface without the need for a visual keyboard. An implementation of such a system is explored and evaluated on simulated data and real user data. Author Keywords relative keyboard, soft keyboard, predictive keyboard

Although typing on touchscreens is slower than typing on physical keyboards, touchscreens offer a critical potential advantage: they are software-based, and, as such, the keyboard layout and classification models used to interpret key presses can dynamically adapt to suit each user’s typing pattern. To explore this potential, we introduce and evaluate two novel personalized keyboard interfaces, both of which adapt their underlying key-press classification models. The first keyboard also visually adapts the location of keys while the second one always maintains a visually stable rectangular layout. A three-session user evaluation showed that the keyboard with the stable rectangular layout significantly improved typing speed compared to a control condition with no personalization. Although no similar benefit was found for the keyboard that also offered visual adaptation, overall subjective response to both new touchscreen keyboards was positive. As personalized keyboards are still an emerging area of research, we also outline a design space that includes dimensions of adaptation and key-press classification features. Author Keywords Touchscreen text input, personalization, adaptive interfaces.

The lack of tactile feedback on touch screens makes typing difficult, a challenge exacerbated when situational impairments like walking vibration and divided attention arise in mobile settings. We introduce WalkType, an adaptive text entry system that leverages the mobile device’s built-in tri-axis accelerometer to compensate for extraneous movement while walking. WalkType’s classification model uses the displacement and acceleration of the device, and inference about the user’s footsteps. Additionally, WalkType models finger-touch location and finger distance traveled on the screen, features that increase overall accuracy regardless of movement. The final model was built on typing data collected from 16 participants. In a study comparing WalkType to a control condition, WalkType reduced uncorrected errors by 45.2 % and increased typing speed by 12.9 % for walking participants. Author Keywords: Touch screen; text entry; adaptive; situational impairments; mobile; virtual keyboard; walking.

We present a design methodology for small ambiguous keypads, where input often produces a list of candidate words for a given desired word. The methodology uses context through semantic relatedness and a part-of-speech language model to improve the order of candidate words and, thus, reduce the overall number of keystrokes per character entered. Simulations yield improvements in text entry speed of about 10 % and reductions in errors of about 20%, depending on the keypad size. We describe a user study with 32 participants entering text on a keypad with letters arranged on three keys. Entry speed was 9.6 % faster, and error rates 21.2 % lower, compared with standard disambiguation, as found on mobile phones.

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