Publications
カンファレンス (国際) 0.2-mm-Step Verification of the Dual Gaussian Distribution Model with Large Sample Size for Predicting Tap Success Rates
Shota Yamanaka, Hiroki Usuba
The 2024 ACM Interactive Surfaces and Spaces Conference (ACM ISS 2024)
2024.10.24
The Dual Gaussian Distribution Model can be utilized for predicting the success rates of tapping targets. However, previous studies have shown that the prediction error increases to as much as 10 points, where ``points'' represent the percentage difference between the observed and predicted values of the tap success rate, particularly for a small target width W such as 2 mm. We hypothesize that this could be due to the experimental designs with sparse W levels performed by few participants, rather than the model itself. Our experiment involving horizontal and vertical bar targets with W = 2-8 mm (step: 0.2 mm) performed by more than 180 participants showed that the maximum prediction errors were relatively small: 2.769 and 3.185 points, respectively. Furthermore, the correlation between W and the prediction error was statistically small (Pearson's |r| < 0.2), and W was not a significant contributor to changing prediction errors (p>0.05). As these results do not support the concerns that the Dual Gaussian Distribution Model has an issue when used with small targets, the development of applications and refined models is encouraged to continue.
Paper :
0.2-mm-Step Verification of the Dual Gaussian Distribution Model with Large Sample Size for Predicting Tap Success Rates
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