We propose a Servo-Gaussian model to predict success rates in continuous manual tracking tasks. Two tasks were conducted to validate this model: path steering and pursuit of a 1D moving target. We hypothesized that (1) hand movements follow the servo-mechanism model, (2) submovement endpoints form a bivariate Gaussian distribution, thus enabling us to predict the success rate at which a submovement endpoint falls inside the tolerance, and (3) the success rate for a whole trial can be predicted if the number of submovements is known. The cross-validation showed R 2 > 0.92 and MAE < 4.9% for steering and R 2 > 0.95 and MAE < 6.5% for pursuit tasks. These results demonstrate that our proposed model delivers high prediction accuracy even for unknown datasets.

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.

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In touch interfaces, a target, such as an icon, has two widths: the visual width and the touchable width. The visual width is the target's appearance, and the touchable width is the area in which users can touch a target and execute an action. In this study, we conduct two experiments to investigate the effects of the visual and touchable widths on touch pointing performance (movement time and success rate). Based on the results, we build candidate models for predicting the movement time and compare them by the values of adjusted R^2 and AIC. In addition, we build a success rate model and test it through cross-validation. Existing models can be applied only to situations where the visual and touchable widths are equal, and we show that our refined model achieves better model fitness, even when such widths are different. We also discuss the design implications of the touch interfaces based on our models.