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Spatial Bar: Exploring Window Switching Techniques for Large Virtual Displays

Leonardo Pavanatto, Jens Grubert, Doug Bowman

TL;DR

This paper tackles window-switching overhead on large virtual displays provided by head-worn devices. It introduces Spatial Bar, a categorized thumbnail bar placed in underutilized space and supporting gaze+click selection with automatic cursor teleportation to the target window. A within-subject VR study compares four conditions (Gaze/Cursor × Teleport/Stay) across distance blocks, revealing that Gaze Teleport minimizes task time for far targets while Cursor Stay performs best at close distances, with eye-tracking posing accuracy and fatigue considerations. The findings offer practical guidance for designing efficient window-management interactions in AR/VR contexts and highlight the tradeoffs between gaze and cursor modalities when navigating expansive virtual workspaces.

Abstract

Virtual displays provided through head-worn displays (HWDs) offer users large screen space for productivity, but managing this space effectively presents challenges. This paper explores how to enhance window-switching strategies for virtual displays by leveraging eye tracking provided by HWDs and underutilized spaces around the main display area. We investigate the efficiency and usability of different cursor behaviors and selection modes in a Spatial Bar interface for window-switching tasks in augmented reality environments. Results show gaze coupled with teleport led to the quickest window-switching times, particularly in tasks where the original cursor position or the target window was far from the Spatial Bar.

Spatial Bar: Exploring Window Switching Techniques for Large Virtual Displays

TL;DR

This paper tackles window-switching overhead on large virtual displays provided by head-worn devices. It introduces Spatial Bar, a categorized thumbnail bar placed in underutilized space and supporting gaze+click selection with automatic cursor teleportation to the target window. A within-subject VR study compares four conditions (Gaze/Cursor × Teleport/Stay) across distance blocks, revealing that Gaze Teleport minimizes task time for far targets while Cursor Stay performs best at close distances, with eye-tracking posing accuracy and fatigue considerations. The findings offer practical guidance for designing efficient window-management interactions in AR/VR contexts and highlight the tradeoffs between gaze and cursor modalities when navigating expansive virtual workspaces.

Abstract

Virtual displays provided through head-worn displays (HWDs) offer users large screen space for productivity, but managing this space effectively presents challenges. This paper explores how to enhance window-switching strategies for virtual displays by leveraging eye tracking provided by HWDs and underutilized spaces around the main display area. We investigate the efficiency and usability of different cursor behaviors and selection modes in a Spatial Bar interface for window-switching tasks in augmented reality environments. Results show gaze coupled with teleport led to the quickest window-switching times, particularly in tasks where the original cursor position or the target window was far from the Spatial Bar.
Paper Structure (31 sections, 5 figures, 3 tables)

This paper contains 31 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Spatial Bar Technique
  4. User Study
  5. Conditions
  6. Implementation and Apparatus
  7. Experimental Design
  8. Hypotheses
  9. Experimental Task
  10. Procedure
  11. Participants
  12. Objective Results
  13. Selection Errors
  14. Thumbnail Time
  15. Distance Blocks.
  16. ...and 16 more sections

Figures (5)

  • Figure 1: Spatial Bar is shown below the virtual display. Categories can be seen in the interface; in this case, they were Browser, Office, Coding, and Explorer.
  • Figure 2: The environment used in the study simulated window management. Windows had colors and numbers, and categories in the spatial bars represented the colors.
  • Figure 3: Thumbnail Time: large distance (left); short distance (right). Error bars represent 95% confidence intervals.
  • Figure 4: Button Time: large distance (left); short distance (right). Error bars represent 95% confidence intervals.
  • Figure 5: Total Time in distance blocks: Large-Large (left); Large-Short; Short-Large; Short-Short (right). Error bars represent 95% confidence intervals.