@inproceedings{52daed04d94044cbbdd8775f07b78793,
title = "Using Bayes' Theorem for Command Input: Principle, Models, and Applications",
abstract = "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\%).",
keywords = "bayes' theorem, command input, point-and-click, touchscreen, word-gesture shortcuts",
author = "Suwen Zhu and Yoonsang Kim and Jingjie Zheng and Luo, \{Jennifer Yi\} and Ryan Qin and Liuping Wang and Xiangmin Fan and Feng Tian and Xiaojun Bi",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020 ; Conference date: 25-04-2020 Through 30-04-2020",
year = "2020",
month = apr,
day = "21",
doi = "10.1145/3313831.3376771",
language = "English",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
booktitle = "CHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems",
}