Skip to Main content Skip to Navigation
Conference papers

Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on Touchscreens

Abstract : Suggesting multiple target candidates based on touch input is a possible option for high-accuracy target selection on small touchscreen devices. But it can become overwhelming if suggestions are triggered too often. To address this, we propose SATS, a Suggestionbased Accurate Target Selection method, where target selection is formulated as a sequential decision problem. The objective is to maximize the utility: the negative time cost for the entire target selection procedure. The SATS decision process is dictated by a policy generated using reinforcement learning. It automatically decides when to provide suggestions and when to directly select the target. Our user studies show that SATS reduced error rate and selection time over Shift [51], a magnification-based method, and MUCS, a suggestion-based alternative that optimizes the utility for the current selection. SATS also significantly reduced error rate over BayesianCommand [58], which directly selects targets based on posteriors, with only a minor increase in selection time.
Document type :
Conference papers
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03664185
Contributor : Michel Beaudouin-Lafon Connect in order to contact the contributor
Submitted on : Tuesday, May 10, 2022 - 6:43:14 PM
Last modification on : Friday, August 5, 2022 - 9:27:34 AM

File

RLforTouchTargetSelection-CHI2...
Files produced by the author(s)

Identifiers

Citation

Zhi Li, Maozheng Zhao, Dibyendu Das, Hang Zhao, Yan Ma, et al.. Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on Touchscreens. CHI 2022 - ACM Conference on Human Factors in Computing Systems, Apr 2022, New Orleans, LA, United States. pp.1-15, ⟨10.1145/3491102.3517472⟩. ⟨hal-03664185⟩

Share

Metrics

Record views

16

Files downloads

13