Using Bayes' Theorem for Command Input: Principle, Models, and Applications

要旨

Entering commands on touchscreens can be noisy, but existing interfaces commonly adopt deterministic principles for deciding targets and often result in errors. Building on prior research of using Bayes' theorem to handle uncertainty in input, this paper formalized Bayes' theorem as a generic guiding principle for deciding targets in command input (referred to as "BayesianCommand"), developed three models for estimating prior and likelihood probabilities, and carried out experiments to demonstrate the effectiveness of this formalization. More specifically, we applied BayesianCommand to improve the input accuracy of (1) point-and-click and (2) word-gesture command input. Our evaluation showed that applying BayesianCommand reduced errors compared to using deterministic principles (by over 26.9% for point-and-click and by 39.9% for word-gesture command input) or applying the principle partially (by over 28.0% and 24.5%).

キーワード
Bayes' theorem
command input
point-and-click
word-gesture shortcuts
touchscreen
著者
Suwen Zhu
Stony Brook University, Stony Brook, NY, USA
Yoonsang Kim
Stony Brook University, Stony Brook, NY, USA
Jingjie Zheng
Google, Kitchener, ON, Canada
Jennifer Yi Luo
Stony Brook University, Stony Brook, NY, USA
Ryan Qin
Stony Brook University, Stony Brook, NY, USA
Liuping Wang
Institute of Software, Chinese Academy of Sciences, Beijing, China
Xiangmin Fan
Institute of Software, Chinese Academy of Sciences, Beijing, China
Feng Tian
Institute of Software, Chinese Academy of Sciences, Beijing, China
Xiaojun Bi
Stony Brook University, Stony Brook, NY, USA
DOI

10.1145/3313831.3376771

論文URL

https://doi.org/10.1145/3313831.3376771

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: Engineering design & modelling

Paper session
312 NI'IHAU
5 件の発表
2020-04-28 20:00:00
2020-04-28 21:15:00
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