Beginners Performance with MessagEase and QWERTY

With the increased use of mobile phones, interest in text entry with them has also amplified. Many new mobile phones are equipped with a QWERTY keypad; new methods to surpass the QWERTY performance are also being developed. This thesis compares user performance of virtual QWERTY keypad to MessagEase. MessagEase uses 9 keys and can therefore be used even on very small touch displays. 9 characters are entered with tapping and the rest with a tap-and-slide gesture. An experiment was conducted with 10 participants transcribing text with both text entry techniques. The experiment consisted of three sessions. In each session, the participants transcribed 30 phrases in total - 15 phrases using each text entry technique. Responses to the System Usability Scale (SUS) for each text entry technique and informal interview data were also collected. From a Repeated-measures analysis of variance a significant effect of the text entry method on text entry rate was seen (F1,19= 47.140, p < 0.0001). The effect of the session (i.e. learning) was also statistically significant (F2,18= 3.631, p = 0.047).The interaction of the session and method was also statistically significant (F2,18= 10.286, p = 0.001) indicating different learning rates. Average text entry speed with MessagEase was 7.43 words per minute (wpm) in the first session and 10.96 wpm in the third session. Likewise, text entry speed with the QWERTY soft keyboard was 17.75 wpm in the first session and 17.16 wpm in the third session. No significant difference was found in the error rates. Keywords: text entry method, MessagEase, QWERT

Investigating five key predictive text entry with combined distance and keystroke modelling

This paper investigates text entry on mobile devices using only five-keys. Primarily to support text entry on smaller devices than mobile phones, this method can also be used to maximise screen space on mobile phones. Reported combined Fitt's law and keystroke modelling predicts similar performance with bigram prediction using a five-key keypad as is currently achieved on standard mobile phones using unigram prediction. User studies reported here show similar user performance on five-key pads as found elsewhere for novice nine-key pad users

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

Multidimensional Pareto optimization of touchscreen keyboards for speed, familiarity and improved spell checking

The paper presents a new optimization technique for keyboard layouts based on Pareto front optimization. We used this multifactorial technique to create two new touchscreen phone keyboard layouts based on three design metrics: minimizing finger travel distance in order to maximize text entry speed, a new metric to maximize the quality of spell correction quality by minimizing neighbouring key ambiguity, and maximizing familiarity through a similarity function with the standard Qwerty layout. The paper describes the optimization process and resulting layouts for a standard trapezoid shaped keyboard and a more rectangular layout. Fitts' law modelling shows a predicted 11% improvement in entry speed without taking into account the significantly improved error correction potential and the subsequent effect on speed. In initial user tests typing speed dropped from approx. 21wpm with Qwerty to 13wpm (64%) on first use of our layout but recovered to 18wpm (85%) within four short trial sessions, and was still improving. NASA TLX forms showed no significant difference on load between Qwerty and our new layout use in the fourth session. Together we believe this shows the new layouts are faster and can be quickly adopted by users

Ambiguous keyboards for AAC

Purpose – “Ambiguous keyboards” and “disambiguation processes” are becoming universally recognised through the popularisation of “predictive text messaging” on mobile phones. As this paper shows, although originating in the AT and AAC fields, these terms and techniques no longer appear to be widely understood or adopted by practitioners or users. The purpose of this paper is to introduce these techniques, discussing the research and theory around them, and to suggest them as AT and AAC strategies to be considered by practitioners and users. Design/methodology/approach – This is a conceptual paper that describes the use of ambiguous keyboards and disambiguation. The hypothesis of the paper is that ambiguous keyboards and disambiguation processes offer potential to increase the efficiency and effectiveness of AAC and should thus be considered further in research and practice. Findings – The two broad methods for removing the ambiguity from the output of an ambiguous keyboard are presented. A summary of the literature around the use of disambiguation processes provided and the use of disambiguation processes for AAC discussed. Originality/value – This paper suggests that ambiguity should be adopted as a characteristic of an AAC keyboard as should the method of removing ambiguity – namely either coding or a disambiguation process

A framework for accessible m-government implementation

The great popularity and rapid diffusion of mobile technologies at worldwide level has also been recognised by the public sector, leading to the creation of m-government. A major challenge for m-government is accessibility – the provision of an equal service to all citizens irrespective of their psychical, mental or technical capabilities. This paper sketches the profiles of six citizen groups: Visually Impaired, Hearing Impaired, Motor Impaired, Speech Impaired, Cognitive Impaired and Elderly. M-government examples that target the aforementioned groups are discussed and a framework for accessible m-government implementation with reference to the W3C Mobile Web Best Practices is proposed

Modelling and correcting for the impact of the gait cycle on touch screen typing accuracy

Walking and typing on a smartphone is an extremely common interaction. Previous research has shown that error rates are higher when walking than when stationary. In this paper we analyse the acceleration data logged in an experiment in which users typed whilst walking, and extract the gait phase angle. We find statistically significant relationships between tapping time, error rate and gait phase angle. We then use the gait phase as an additional input to an offset model, and show that this allows more accurate touch interaction for walking users than a model which considers only the recorded tap position