The bubble cursor: enhancing target acquisition by dynamic resizing of the cursor's activation area
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Precise selection techniques for multi-touch screens
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Empirical evaluation for finger input properties in multi-touch interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Back-of-device interaction allows creating very small touch devices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A framework for robust and flexible handling of inputs with uncertainty
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
100,000,000 taps: analysis and improvement of touch performance in the large
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The word-gesture keyboard: reimagining keyboard interaction
Communications of the ACM
Touch behavior with different postures on soft smartphone keyboards
MobileHCI '12 Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services
A user-specific machine learning approach for improving touch accuracy on mobile devices
Proceedings of the 25th annual ACM symposium on User interface software and technology
FFitts law: modeling finger touch with fitts' law
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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To improve the accuracy of target selection for finger touch, we conceptualize finger touch input as an uncertain process, and derive a statistical target selection criterion, Bayesian Touch Criterion, by combining the basic Bayes' rule of probability with the generalized dual Gaussian distribution hypothesis of finger touch. The Bayesian Touch Criterion selects the intended target as the candidate with the shortest Bayesian Touch Distance to the touch point, which is computed from the touch point to the target center distance and the target size. We give the derivation of the Bayesian Touch Criterion and its empirical evaluation with two experiments. The results showed that for 2-dimensional circular target selection, the Bayesian Touch Criterion is significantly more accurate than the commonly used Visual Boundary Criterion (i.e., a target is selected if and only if the touch point falls within its boundary) and its two variants.