Work

Using relative head and hand-target features to predict intention in 3D moving-target selection

Year: 2014

Type: conference-paper

Abstract: Selection of moving targets is a common, yet complex task in human-computer interaction (HCI) and virtual reality (VR). Predicting user intention may be beneficial to address the challenges inherent in interaction techniques for moving-target selection. This article extends previous models by integrating relative head-target and hand-target features to predict intended moving targets. The features are calculated in a time window ending at roughly two-thirds of the total target selection time and evaluated using decision trees. With two targets, this model is able to predict user choice with up to ~ 72% accuracy on general moving-target selection tasks and up to ~ 78% by also including task-related target properties.

Source: IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

DOI: 10.1109/vr.2014.6802050

Authors: Juan Sebastian Casallas, James H. Oliver, Jonathan W. Kelly, Frederic Merienne, Samir Garbaya

Institutions: Iowa State University, Université de Bourgogne, Arts et Métiers, Laboratoire d’Électronique, Informatique et Image

Language: English

Topic: Interactive and Immersive Displays

Subfield: Human-Computer Interaction

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2048556651

FWCI: 0.251

Cites: 45

Cited by: 11

Related to: 10