Name
Abstract | ||
|---|---|---|
Novel interaction techniques have been developed to address the difficulties of selecting moving targets. However, similar to their static-target counterparts, these techniques may suffer from clutter and overlap, which can be addressed by predicting intended targets. Unfortunately, current predictive techniques are tailored towards static-target selection. Thus, a novel approach for predicting user intention in moving-target selection tasks using decision-trees constructed with the initial physical states of both the user and the targets is proposed. This approach is verified in a virtual reality application in which users must choose, and select between different moving targets. With two targets, this model is able to predict user choice with approximately 71% accuracy, which is significantly better than both chance and a frequentist approach. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39360-0_2 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
novel approach,static-target selection,static-target counterpart,current predictive technique,moving-target selection task,user choice,novel interaction technique,user intention,frequentist approach,initial physical state,prediction,fitts law,virtual reality,decision trees | Decision tree,Frequentist inference,Virtual reality,Fitts's law,Clutter,Computer science,Artificial intelligence | Conference |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Sebastián Casallas | 1 | 1 | 0.69 |
| James H. Oliver | 2 | 130 | 10.40 |
| Jonathan W. Kelly | 3 | 115 | 18.87 |
| Frédéric Merienne | 4 | 35 | 13.08 |
| Samir Garbaya | 5 | 20 | 3.91 |