予稿集Proceeding

Servo-Gaussian Model to Predict Success Rates in Manual Tracking: Path Steering and Pursuit of 1D Moving Target


Journal: Proceedings of the 33rd annual ACM symposium on User interface software and technology

Pages:844 - 857

Source URL:https://dl.acm.org/doi/10.1145/3379337.3415896


Published:

Publisher:Association for Computing Machinery


Keywords:Servo-Gaussian Model


Abstract

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 mov-ing target. We hypothesized that (1) hand movements follow the servo-mechanism model, (2) submovement endpoints forma bivariate Gaussian distribution, thus enabling us to predict the success rate at which a submovement endpoint falls in-side 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.