Servo-gaussian model to predict success rates in manual tracking: Path steering and pursuit of 1D moving target

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 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.