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Bayesian touch: a statistical criterion of target selection with finger touch

Authors:

Shumin ZhaiAuthors Info & Claims

UIST '13: Proceedings of the 26th annual ACM symposium on User interface software and technology

October 2013

Pages 51 - 60

https://doi.org/10.1145/2501988.2502058

Published: 08 October 2013 Publication History

Abstract

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.

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Cited By

Yamanaka SUsuba HSato J(2024)Behavioral Differences between Tap and Swipe: Observations on Time, Error, Touch-point Distribution, and Trajectory for Tap-and-swipe Enabled TargetsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642272(1-12)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642272
Usuba HYamanaka SSato J(2023)Clarifying the Effect of Edge Targets in Touch Pointing through Crowdsourced ExperimentsProceedings of the ACM on Human-Computer Interaction10.1145/36264697:ISS(156-174)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1145/3626469
Chu JMa YZhai SGu XBi X(2023)TouchType-GAN: Modeling Touch Typing with Generative Adversarial NetworkProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology10.1145/3586183.3606760(1-13)Online publication date: 29-Oct-2023
https://dl.acm.org/doi/10.1145/3586183.3606760
Show More Cited By

Index Terms

Bayesian touch: a statistical criterion of target selection with finger touch
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

UIST '13: Proceedings of the 26th annual ACM symposium on User interface software and technology

October 2013

558 pages

ISBN:9781450322683

DOI:10.1145/2501988

General Chairs:
Shahram Izadi
Microsoft Research, UK
,
Aaron Quigley
University of St Andrews, UK
,
Program Chairs:
Ivan Poupyrev
Disney Research, USA
,
Takeo Igarashi
The University of Tokyo, Japan

Copyright © 2013 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 08 October 2013

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UIST'13

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UIST'13: The 26th Annual ACM Symposium on User Interface Software and Technology

October 8 - 11, 2013

St. Andrews, Scotland, United Kingdom

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UIST '13 Paper Acceptance Rate 62 of 317 submissions, 20%;

Overall Acceptance Rate 842 of 3,967 submissions, 21%

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UIST '24

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The 37th Annual ACM Symposium on User Interface Software and Technology

October 13 - 16, 2024

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Citations

Cited By

Yamanaka SUsuba HSato J(2024)Behavioral Differences between Tap and Swipe: Observations on Time, Error, Touch-point Distribution, and Trajectory for Tap-and-swipe Enabled TargetsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642272(1-12)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642272
Usuba HYamanaka SSato J(2023)Clarifying the Effect of Edge Targets in Touch Pointing through Crowdsourced ExperimentsProceedings of the ACM on Human-Computer Interaction10.1145/36264697:ISS(156-174)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1145/3626469
Chu JMa YZhai SGu XBi X(2023)TouchType-GAN: Modeling Touch Typing with Generative Adversarial NetworkProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology10.1145/3586183.3606760(1-13)Online publication date: 29-Oct-2023
https://dl.acm.org/doi/10.1145/3586183.3606760
Schneider AGraham T(2023)Supporting Aim Assistance Algorithms through a Rapidly Trainable, Personalized Model of Players’ Spatial and Temporal Aiming AbilityProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581293(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581293
Zhang HHuang JTu HTian F(2023)Shape-Adaptive Ternary-Gaussian Model: Modeling Pointing Uncertainty for Moving Targets of Arbitrary ShapesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581217(1-18)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581217
Wei YShi RYu DWang YLi YYu LLiang H(2023)Predicting Gaze-based Target Selection in Augmented Reality Headsets based on Eye and Head Endpoint DistributionsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581042(1-14)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581042
Yu DSyiem BIrlitti ADingler TVelloso EGoncalves J(2023)Modeling Temporal Target Selection: A Perspective from Its Spatial CorrespondenceProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581011(1-14)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581011
Yamanaka SUsuba H(2023)Tuning Endpoint-variability Parameters by Observed Error Rates to Obtain Better Prediction Accuracy of Pointing MissesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580746(1-18)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580746
Yamanaka SKinoshita TOba YTomihari RMiyashita H(2023)Varying Subjective Speed-accuracy Biases to Evaluate the Generalizability of Experimental Conclusions on Pointing-facilitation TechniquesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580740(1-13)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580740
Lei TChen JChen JLiu B(2023)Modeling the gaze point distribution to assist eye-based target selection in head-mounted displaysNeural Computing and Applications10.1007/s00521-023-08705-835:36(25069-25081)Online publication date: 8-Jun-2023
https://doi.org/10.1007/s00521-023-08705-8
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