Accurate Measurements of Pointing Performance from In Situ Observations

Abstract

We present a method for obtaining lab-quality measurements of pointing performance from unobtrusive observations of natural in situ interactions. Specifically, we have developed a set of user-independent classifiers for discriminating between deliberate, targeted mouse pointer movements and those movements that were affected by any extraneous factors. To develop and validate these classifiers, we developed logging software to unobtrusively record pointer trajectories as participants naturally interacted with their computers over the course of several weeks. Each participant also performed a set of pointing tasks in a formal study set-up. For each movement, we computed a set of measures capturing nuances of the trajectory and the speed, acceleration, and jerk profiles. Treating the observations from the formal study as positive examples of deliberate, targeted movements and the in situ observations as unlabeled data with an unknown mix of deliberate and distracted interactions, we used a recent advance in machine learning to develop the classifiers. Our results show that, on four distinct metrics, the data collected in-situ and filtered with our classifiers closely matches the results obtained from the formal experiment.