Uncertain text entry on mobile devices

Weir, Daryl; Pohl, Henning; Rogers, Simon; Vertanen, Keith; Kristensson, Per Ola

doi:10.1145/2556288.2557412

Cited by 65 publications

(38 citation statements)

References 33 publications

(29 reference statements)

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“…[10,16]) and allow users to choose among word predictions. In the research literature, STKs automatic typing correction algorithms have been further refined by considering users' posture [9], whether users are walking or standing still [8], finger pressure and Gaussian Process regression of touch locations [24], and by simultaneously supporting both word completions and automatic typing correction [2].…”

Section: Introductionmentioning

confidence: 99%

Performance and User Experience of Touchscreen and Gesture Keyboards in a Lab Setting and in the Wild

Reyal

Zhai

Kristensson

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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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-ofvocabulary 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.

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Section: Introductionmentioning

confidence: 99%

Performance and User Experience of Touchscreen and Gesture Keyboards in a Lab Setting and in the Wild

Reyal

Zhai

Kristensson

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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“…Recently, several touchscreen typing systems have leveraged additional information, such as activity (walking versus standing) [7], or hand posture [8]. Other recent improvements include using pressure information and Gaussian Process regression within a probabilistic decoder [21], and the development of an algorithm that simultaneously accommodates both completions and corrections [1].…”

Section: Introductionmentioning

confidence: 99%

VelociTap

Vertanen

Memmi

Emge

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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We present VelociTap: a state-of-the-art touchscreen keyboard decoder that supports a sentence-based text entry approach. VelociTap enables users to seamlessly choose from three word-delimiter actions: pushing a space key, swiping to the right, or simply omitting the space key and letting the decoder infer spaces automatically. We demonstrate that VelociTap has a significantly lower error rate than Google's keyboard while retaining the same entry rate. We show that intermediate visual feedback does not significantly affect entry or error rates and we find that using the space key results in the most accurate results. We also demonstrate that enabling flexible word-delimiter options does not incur an error rate penalty. Finally, we investigate how small we can make the keyboard when using VelociTap. We show that novice users can reach a mean entry rate of 41 wpm on a 40 mm wide smartwatch-sized keyboard at a 3% character error rate.

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“…al. 2003)), as demonstrated in (Rogers, Williamson, Stewart & Murray-Smith, 2010;Weir, Pohl, Rogers, Vertanen, Kristensson, 2014).…”

Section: Offset Modelsmentioning

confidence: 78%

Nomadic Input on Mobile Devices: The Influence of Touch Input Technique and Walking Speed on Performance and Offset Modeling

Musić

Murray-Smith

2015

Human–Computer Interaction

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Uncertain text entry on mobile devices

Cited by 65 publications

References 33 publications

Performance and User Experience of Touchscreen and Gesture Keyboards in a Lab Setting and in the Wild

Performance and User Experience of Touchscreen and Gesture Keyboards in a Lab Setting and in the Wild

VelociTap

Nomadic Input on Mobile Devices: The Influence of Touch Input Technique and Walking Speed on Performance and Offset Modeling

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