FFitts law

Bi, Xiaojun; Li, Yang; Zhai, Shumin

doi:10.1145/2470654.2466180

Cited by 180 publications

(51 citation statements)

References 21 publications

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“…Weir et al [37] demonstrated offset modelling using a GP regression model, and in particular showed the importance of user specificity in offset models. Bi and Zhai [1] extend Fitts' law to finger touch using a dual Gaussian distribution approach. Bi and Zhai [2] further extend this to handle target selection on a touchscreen using their Bayesian touch criterion, demonstrating a significant increase in accuracy.…”

Section: Offset Modellingmentioning

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: Offset Modellingmentioning

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

“…The smaller the mobile device, the more difficult it is to type. This is due to the size of the input device (i.e., a finger) relative to the size of the target (i.e., a single key on the onscreen keyboard) [16].…”

Section: Physical Considerationsmentioning

confidence: 99%

“…The smaller the mobile device, the more difficult it is to select items from a list. This is due to the size of the input device (i.e., a finger) relative to the size of the target (i.e., a single item on the UI) [16]. In addition to variability in physical surface size, UIs also vary from device to device and browser to browser.…”

Section: Physical Considerationsmentioning

confidence: 99%

Usability and Security Considerations for Public Safety Mobile Authentication

Choong¹,

Franklin²,

Greene³

2016

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“…If the expected error rate of 9 Â 9 mm targets is 0.5%, error rates for 11 Â 11 mm targets were expected to be lower. Even if the wellknown "fat fingers" problem occurs (Siek et al, 2005;Zhai et al, 2009;Wintergerst et al, 2010;Bi et al, 2013), the size of the numbered tiles would be large enough for error correction using one or two taps, including the 0.3 mm padding between the tiles.…”

Section: Visual Targetmentioning

confidence: 99%

“…The tapping task chosen was not a Fitts' reciprocal tapping task as used in other similar pointing performance studies Cockburn et al, 2012;Bi et al, 2013), but a gaming task in which the participants needed to visually search the screen for the next button to tap. Instead of adopting the "standard" Fitts' Law tapping task, most related studies adopted text entry tasks such as text creation and text copying (MacKenzie and Soukoreff, 2002;Lee and Zhai, 2009;Fukumoto, 2009;Zheng et al, 2012).…”

Section: Taskmentioning

confidence: 99%