カンファレンス (国際) Rethinking the Dual Gaussian Distribution Model for Predicting Touch Accuracy in On-screen-start Pointing Tasks
Shota Yamanaka, Hiroki Usuba (Meiji University)
The 2020 ACM Interactive Surfaces and Spaces (ISS 2020)
The dual Gaussian distribution hypothesis has been used to predict the success rate of target pointing on touchscreens. Bi and Zhai evaluated their success-rate prediction model in off-screen-start pointing tasks. However, we found that their prediction model could also be used for on-screen-start pointing tasks. We discuss the reasons why and empirically validate our hypothesis in a series of four experiments with various target sizes and distances. The prediction accuracy of Bi and Zhai's model was high in all of the experiments, with a 10-point absolute (or 14.9% relative) prediction error at worst. Also, we show that there is no clear benefit to integrating the target distance when predicting the endpoint variability and success rate.