Modeling Endpoint Distribution of Pointing Selection Tasks in Virtual Reality Environments

摘要

Understanding the endpoint distribution of pointing selection tasks can revealthe underlying patterns on how users tend to acquire a target, which isone of the most essential and pervasive tasks in interactive systems. It couldfurther aid designers to create new graphical user interfaces and interactiontechniques that are optimized for accuracy, efficiency, and ease of use. Previousresearch has explored the modeling of endpoint distribution outside ofvirtual reality (VR) systems that have shown to be useful in predicting selectionaccuracy and guide the design of new interactive techniques. This workaims at developing an endpoint distribution of selection tasks for VR systemswhich has resulted in EDModel, a novel model that can be used to predictendpoint distribution of pointing selection tasks in VR environments. Thedevelopment of EDModel is based on two users studies that have exploredhow factors such as target size, movement amplitude, and target depth affectthe endpoint distribution. The model is built from the collected data andits generalizability is subsequently tested in complex scenarios with morerelaxed conditions. Three applications of EDModel inspired by previousresearch are evaluated to show the broad applicability and usefulness of themodel: correcting the bias in Fitts’s law, predicting selection accuracy, andenhancing pointing selection techniques. Overall, EDModel can achieve highprediction accuracy and can be adapted to different types of applications inVR.