Two Handed Selection Techniques for Volumetric Data

Abstract

We developed three distinct two-handed selection techniques for volumetric data visualizations that use splat-based rendering. Two techniques are bimanual asymmetric, where each hand has a different task. One technique is bimanual symmetric, where each hand has the same task. These techniques were then evaluated based on accuracy, completion times, TLX workload assessment, overall comfort and fatigue, ease of use, and ease of learning. Our results suggest that the bimanual asymmetric selection techniques are best used when performing gross selection for potentially long periods of time and for cognitively demanding tasks. However when optimum accuracy is needed, the bimanual symmetric technique was best for selection