Improving two-thumb text entry on touchscreen devices

Oulasvirta, Antti; Reichel, Anna; Li, Wenbin; Zhang, Yan; Bachynskyi, Myroslav; Vertanen, Keith; Kristensson, Per Ola

doi:10.1145/2470654.2481383

Cited by 108 publications

(76 citation statements)

References 25 publications

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“…We adapted a language model that has previously been used for a thumb-typing touchscreen keyboard [24]. The Twitterbased language model was trained based on 778M tweets sent between 12/2010 and 6/2012.…”

Section: Language Modelmentioning

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: Language Modelmentioning

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

Self Cite

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“…One approach is to partially or completely redesign the standard QWERTY touchscreen typing experience. Examples include gesture keyboards [13], optimized keyboards such as ATOMIK [23], interlaced QWERTY [22], the quasi-Qwerty optimized keyboard [2], multidimensional Pareto keyboard optimization [5], multilingual keyboard optimization [3], KALQ [17], and systems that adapt to user input or sensor data (e.g. [4,6]).…”

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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“…Recently, there has been interest in countermeasures against perturbed user input on touchscreens due to situational impairments (such as walking vibrations) and/or divided attention for mobile computing platforms [33]- [36]. Solutions typically rely on built-in inertial measurement units or camera(s) to dynamically adapt the GUI layout and/or compensate for the present noise.…”

Section: Related Workmentioning

confidence: 99%

Intent Inference for Hand Pointing Gesture-Based Interactions in Vehicles

Ahmad

Murphy

Langdon

et al. 2016

IEEE Trans. Cybern.

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Using interactive displays, such as a touchscreen, in vehicles typically requires dedicating a considerable amount of visual as well as cognitive capacity and undertaking a hand pointing gesture to select the intended item on the interface. This can act as a distractor from the primary task of driving and consequently can have serious safety implications. Due to road and driving conditions, the user input can also be highly perturbed resulting in erroneous selections compromising the system usability. In this paper, we propose intent-aware displays that utilize a pointing gesture tracker in conjunction with suitable Bayesian destination inference algorithms to determine the item the user intends to select, which can be achieved with high confidence remarkably early in the pointing gesture. This can drastically reduce the time and effort required to successfully complete an in-vehicle selection task. In the proposed probabilistic inference framework, the likelihood of all the nominal destinations is sequentially calculated by modeling the hand pointing gesture movements as a destination-reverting process. This leads to a Kalman filter-type implementation of the prediction routine that requires minimal parameter training and has low computational burden; it is also amenable to parallelization. The substantial gains obtained using an intent-aware display are demonstrated using data collected in an instrumented vehicle driven under various road conditions.

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Improving two-thumb text entry on touchscreen devices

Cited by 108 publications

References 25 publications

Uncertain text entry on mobile devices

Uncertain text entry on mobile devices

VelociTap

Intent Inference for Hand Pointing Gesture-Based Interactions in Vehicles

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