Google Research

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

  • Suwen Zhu
  • Yoonsang Kim
  • Jingjie Zheng
  • Jennifer Yi Luo
  • Ryan Qin
  • Liuping Wang
  • Xiangmin Fan
  • Feng Tian
  • Xiaojun Bi
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, ACM, New York, NY, 642:1 - 642:15 (to appear)

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%).

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