An Optimal Control Model of Mouse Pointing Using the LQR

TL;DR

This paper models mouse pointing movements using an optimal control approach with LQR, demonstrating it explains user data better than traditional models by assuming users minimize effort and jerk.

Contribution

Introduces an LQR-based optimal control model for mouse pointing that outperforms classical models in fitting user movement data.

Findings

LQR model fits user data significantly better than minimum-jerk models.

Model parameters are identified from real user movement data.

The approach provides a new framework for understanding mouse movement control.

Abstract

In this paper we explore the Linear-Quadratic Regulator (LQR) to model movement of the mouse pointer. We propose a model in which users are assumed to behave optimally with respect to a certain cost function. Users try to minimize the distance of the mouse pointer to the target smoothly and with minimal effort, by simultaneously minimizing the jerk of the movement. We identify parameters of our model from a dataset of reciprocal pointing with the mouse. We compare our model to the classical minimum-jerk and second-order lag models on data from 12 users with a total of 7702 movements. Our results show that our approach explains the data significantly better than either of these previous models.