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Fitts' law and throughput based on effective measures are two mathematical models frequently used to analyze human motor performance in a standardized pointing task, e.g., to compare the performance of input and output devices. Even though pointing has been deeply studied in 2D, it is not well understood how different task execution strategies affect throughput in pointing in 3D virtual environments. In this work, we examine the effective throughput measure, claimed to be invariant to task execution strategies, in Virtual Reality (VR) systems with three such strategies, "as fast, as precise, and as fast and as precise as possible" for ray casting and virtual hand interaction, by re-analyzing data from a 3D pointing ISO 9241-411 study. Results show that effective throughput is not invariant for different task execution strategies in VR, which also matches a more recent 2D result. Normalized speed vs. accuracy curves also did not fit the data. We thus suggest that practitioners, developers, and researchers who use MacKenzie's effective throughput formulation should consider our findings when analyzing 3D user pointing performance in VR systems. [ABSTRACT FROM AUTHOR]

The design space for user interfaces for Immersive Analytics applications is vast. Designers can combine navigation and manipulation to enable data exploration with ego- or exocentric views, have the user operate at different scales, or use different forms of navigation with varying levels of physical movement. This freedom results in a multitude of different viable approaches. Yet, there is no clear understanding of the advantages and disadvantages of each choice. Our goal is to investigate the affordances of several major design choices, to enable both application designers and users to make better decisions. In this article, we assess two main factors, exploration mode and frame of reference, consequently also varying visualization scale and physical movement demand. To isolate each factor, we implemented nine different conditions in a Space-Time Cube visualization use case and asked 36 participants to perform multiple tasks. We analyzed the results in terms of performance and qualitative measures and correlated them with participants’ spatial abilities. While egocentric room-scale exploration significantly reduced mental workload, exocentric exploration improved performance in some tasks. Combining navigation and manipulation made tasks easier by reducing workload, temporal demand, and physical effort. [ABSTRACT FROM AUTHOR]

In this work, we evaluate two standard interaction techniques for Immersive Analytics environments: virtual hands, with actions such as grabbing and stretching, and virtual ray pointers, with actions assigned to controller buttons. We also consider a third option: seamlessly integrating both modes and allowing the user to alternate between them without explicit mode switches. Easy-to-use interaction with data visualizations in Virtual Reality enables analysts to intuitively query or filter the data, in addition to the benefit of multiple perspectives and stereoscopic 3D display. While many VR-based Immersive Analytics systems employ one of the studied interaction modes, the effect of this choice is unknown. Considering that each has different advantages, we compared the three conditions through a controlled user study in the spatio-temporal data domain. We did not find significant differences between hands and ray-casting in task performance, workload, or interactivity patterns. Yet, 60% of the participants preferred the mixed mode and benefited from it by choosing the best alternative for each low-level task. This mode significantly reduced completion times by 23% for the most demanding task, at the cost of a 5% decrease in overall success rates. [ABSTRACT FROM AUTHOR]

Object manipulation techniques in immersive virtual environments are either inaccurate or slow. We present a novel technique, PinNPivot, where pins are used to constrain 1DOF/2DOF/3DOF rotations. It also supports 6DOF manipulation and 3DOF translation. A comparison with three existing techniques shows that PinNPivot is significantly more accurate and faster. [ABSTRACT FROM AUTHOR]

Walking has always been considered as the gold standard for navigation in Virtual Reality research. Though full rotation is no longer a technical challenge, physical translation is still restricted through limited tracked areas. While rotational information has been shown to be important, the benefit of the translational component is still unclear with mixed results in previous work. To address this gap, we conducted a mixed-method experiment to compare four levels of translational cues and control: none (using the trackpad of the HTC Vive controller to translate), upper-body leaning (sitting on a “NaviChair”, leaning the upper-body to locomote), whole-body leaning/stepping (standing on a platform called NaviBoard, leaning the whole body or stepping one foot off the center to navigate), and full translation (physically walking). Results showed that translational cues and control had significant effects on various measures including task performance, task load, and simulator sickness. While participants performed significantly worse when they used a controller with no embodied translational cues, there was no significant difference between the NaviChair, NaviBoard, and actual walking. These results suggest that translational body-based motion cues and control from a low-cost leaning/stepping interface might provide enough sensory information for supporting spatial updating, spatial awareness, and efficient locomotion in VR, although future work will need to investigate how these results might or might not generalize to other tasks and scenarios. [ABSTRACT FROM AUTHOR]

A Space-Time Cube enables analysts to clearly observe spatio-temporal features in movement trajectory datasets in geovisualization. However, its general usability is impacted by a lack of depth cues, a reported steep learning curve, and the requirement for efficient 3D navigation. In this work, we investigate a Space-Time Cube in the Immersive Analytics domain. Based on a review of previous work and selecting an appropriate exploration metaphor, we built a prototype environment where the cube is coupled to a virtual representation of the analyst's real desk, and zooming and panning in space and time are intuitively controlled using mid-air gestures. We compared our immersive environment to a desktop-based implementation in a user study with 20 participants across 7 tasks of varying difficulty, which targeted different user interface features. To investigate how performance is affected in the presence of clutter, we explored two scenarios with different numbers of trajectories. While the quantitative performance was similar for the majority of tasks, large differences appear when we analyze the patterns of interaction and consider subjective metrics. The immersive version of the Space-Time Cube received higher usability scores, much higher user preference, and was rated to have a lower mental workload, without causing participants discomfort in 25-minute-long VR sessions. [ABSTRACT FROM AUTHOR]