Usability guided key-target resizing for soft keyboards

Gunawardana, Asela; Paek, Tim; Meek, Christopher

doi:10.1145/1719970.1719986

Cited by 84 publications

(52 citation statements)

References 11 publications

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“…Kristensson and Zhai [20] presented an alternative approach that used pattern matching to identify the words that most likely corresponded to users' tap sequences. Gunawardana et al [10] presented an approach with dynamic key-target resizing which defined a minimum target size, preventing key targets from shrinking too much. Rudchenko et al [29], with their Text Text Revolution game, showed that per-user touch models and personalized key-target resizing can further improve typing accuracy.…”

Section: Related Work Text Entry Correction Approachesmentioning

confidence: 99%

Uncertain text entry on mobile devices

Weir

Pohl²,

Rogers

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Users often struggle to enter text accurately on touchscreen keyboards. To address this, we present a flexible decoder for touchscreen text entry that combines probabilistic touch models with a language model. We investigate two different touch models. The first touch model is based on a Gaussian Process regression approach and implicitly models the inherent uncertainty of the touching process. The second touch model allows users to explicitly control the uncertainty via touch pressure. Using the first model we show that the character error rate can be reduced by up to 7% over a baseline method, and by up to 1.3% over a leading commercial keyboard. Using the second model we demonstrate that providing users with control over input certainty reduces the amount of text users have to correct manually and increases the text entry rate.

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Section: Related Work Text Entry Correction Approachesmentioning

confidence: 99%

Uncertain text entry on mobile devices

Weir

Pohl²,

Rogers

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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show abstract

“…Focusing on text entry, some studies proposed methods for transforming detection areas based on language models and users' touch distribution [3], [4]. For example, Rodrigues et al [4] analyzed the way to influence the typing behavior of older adults by varying the keyboard, including the color and the width of keys.…”

Section: Text Entry For Older Adultsmentioning

confidence: 99%

“…For example, Rodrigues et al [4] analyzed the way to influence the typing behavior of older adults by varying the keyboard, including the color and the width of keys. Gunawardana et al [3] proposed methods for varying the detection areas based on typing history in the language model. As a different approach, Bi et al [5] optimized an algorithm for presenting suitable suggestions to make correction and completion easier.…”

Section: Text Entry For Older Adultsmentioning

confidence: 99%

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Assistive Typing Application for Older Adults Based on Input Stumble Detection

Hagiya

Horiuchi

Yazaki

et al. 2017

Journal of Information Processing

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Smartphones offer new opportunities to improve the lives of older adults. Although many older adults are interested in smartphones, most of them face difficulties in self-instruction and need support. Text entry, which is essential for various applications, is one of the most difficult operations to master. Therefore, we propose an assistive typing application that detects input stumbles and provides instructions for typing presented sentences, instead of having human tutors help older adults resolve the input stumbles by themselves. First, we investigated the ways that novice older adults have problems with text entry on smartphones. Next, we confirmed the acceptability of being provided with instructions for text entry by Wizard-of-Oz (WoZ). Then, we constructed an assistive typing application based on the collected data from two user studies. An evaluation with novice older adults (60+) showed that the assistive typing application increased typing speed by 17.2% and reduced input stumble incidence by 59.1% compared with the users' initial performance. Improvement rates were almost the same as those achieved with human tutors.

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“…Numerous innovations in text entry have been studied, including gesture keyboards [39,4,23,29,1], key-target resizing [30,15], alternative layouts [9,38,6], and sensor-based adaptation [3,13]. Speed and accuracy gains have been reported in systems where the user presents a complete utterance to the system, which the system can then process as a whole: speech recognition with editing [35], or typing with feedback only at the end of a sentence [37].…”

Section: Related Workmentioning

confidence: 99%

On Suggesting Phrases vs. Predicting Words for Mobile Text Composition

Arnold

Gajos

Kalai

2016

Proceedings of the 29th Annual Symposium on User Interface Software and Technology

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A system capable of suggesting multi-word phrases while someone is writing could supply ideas about content and phrasing and allow those ideas to be inserted efficiently. Meanwhile, statistical language modeling has provided various approaches to predicting phrases that users type. We introduce a simple extension to the familiar mobile keyboard suggestion interface that presents phrase suggestions that can be accepted by a repeated-tap gesture. In an extended composition task, we found that phrases were interpreted as suggestions that affected the content of what participants wrote more than conventional single-word suggestions, which were interpreted as predictions. We highlight a design challenge: how can a phrase suggestion system make valuable suggestions rather than just accurate predictions?

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Usability guided key-target resizing for soft keyboards

Cited by 84 publications

References 11 publications

Uncertain text entry on mobile devices

Uncertain text entry on mobile devices

Assistive Typing Application for Older Adults Based on Input Stumble Detection

On Suggesting Phrases vs. Predicting Words for Mobile Text Composition

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