Dynamic Gaussian Force Field Controlled Kalman Filtering For Pointing Interaction

Abstract

As human computer interaction is extending from the desk to the whole room, modalities allowing for distant interaction become more important. Distant interaction however, is inherently inaccurate. Assisting technologies, like force fields, sticky targets, and target expansion have been shown to improve pointing tasks. We present a new variant of force fields that are modeled using Gaussian distributions, which makes placement and configuration as well as overlap handling straight forward. In addition, the force fields are dynamically activated by predicting targets, to allow for natural and fluent movements. Results from a user study show, that the dynamic Gaussian fields can speed up the time needed to click a button with a pointing gesture by up to 60%.