Predictive text entry in immersive environments

One of the classic problems with immersive environments is data entry; with a head-mounted display (HMD) the user can no longer see the keyboard. Although for many applications data entry is not a requirement, for some it is essential: communicating in collaborative environments, entering a filename to which work can be saved, or accessing system controls. Combining data gloves and a graphically represented keyboard with a predictive spelling paradigm, we describe an effective text entry technique for immersive environments; we explore the key issues when using such a technique, and report the results of preliminary usability testing

Predictive text entry methods for mobile phones

This paper reports initial user tests comparing text entry methods, analysis of word clashes with dictionary based methods and keystroke level modelling of the different input methods

Pickup usability dominates: a brief history of mobile text entry research and adoption

Text entry on mobile devices (e.g. phones and PDAs) has been a research challenge since devices shrank below laptop size: mobile devices are simply too small to have a traditional full-size keyboard. There has been a profusion of research into text entry techniques for smaller keyboards and touch screens: some of which have become mainstream, while others have not lived up to early expectations. As the mobile phone industry moves to mainstream touch screen interaction we will review the range of input techniques for mobiles, together with evaluations that have taken place to assess their validity: from theoretical modelling through to formal usability experiments. We also report initial results on iPhone text entry speed

Analyzing the Impact of Cognitive Load in Evaluating Gaze-based Typing

Gaze-based virtual keyboards provide an effective interface for text entry by eye movements. The efficiency and usability of these keyboards have traditionally been evaluated with conventional text entry performance measures such as words per minute, keystrokes per character, backspace usage, etc. However, in comparison to the traditional text entry approaches, gaze-based typing involves natural eye movements that are highly correlated with human brain cognition. Employing eye gaze as an input could lead to excessive mental demand, and in this work we argue the need to include cognitive load as an eye typing evaluation measure. We evaluate three variations of gaze-based virtual keyboards, which implement variable designs in terms of word suggestion positioning. The conventional text entry metrics indicate no significant difference in the performance of the different keyboard designs. However, STFT (Short-time Fourier Transform) based analysis of EEG signals indicate variances in the mental workload of participants while interacting with these designs. Moreover, the EEG analysis provides insights into the user's cognition variation for different typing phases and intervals, which should be considered in order to improve eye typing usability.Comment: 6 pages, 4 figures, IEEE CBMS 201

OpenAdaptxt: an open source enabling technology for high quality text entry

Modern text entry systems, especially for touch screen phones and novel devices, rely on complex underlying technologies such as error correction and word suggestion. Furthermore, for global deployment a vast number of languages have to be supported. Together this has raised the entry bar for new text entry techniques, which makes developing and testing a longer process thus stifling innovation. For example, testing a new feedback mechanism in comparison to a stock keyboard now requires the researchers to support at least slip correction and probably word suggestion. This paper introduces OpenAdaptxt: an open source community driven text input platform to enable development of higher quality text input solutions. It is the first commercial-grade open source enabling technology for modern text entry that supports both multiple platforms and dictionary support for over 50 spoken languages