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Leveraging Head Movement for Navigating Off-Screen Content on Large Curved Displays

A K M Amanat Ullah, David Ahlström, Khalad Hasan

Abstract

Large curved displays are ideal for viewing 360 degree content, such as 3D maps, but typically restrict users to a 180 degree viewport, leaving information off-screen. Since users naturally direct their heads toward regions on-screen before interacting, head movements offer a promising alternative for workspace manipulation to bring off-screen content into view. We explore rate control functions (linear, sigmoid, polynomial) and zone control functions (continuous, friction, interrupted, additive) to translate head rotations into workspace control, enabling users to access off-screen content. Polynomial rate control emerges as the best choice, achieving the fastest trial times and highest subjective ratings. Using a map navigation task, our second study demonstrates that users perform better with the polynomial head-based technique than with the industry-standard controller-based methods, click-and-drag and joystick-push, for 360\degree workspace navigation. Based on these findings, we provide guidelines to inform the design of future 360\degree workspace navigation techniques for large curved displays.

Leveraging Head Movement for Navigating Off-Screen Content on Large Curved Displays

Abstract

Large curved displays are ideal for viewing 360 degree content, such as 3D maps, but typically restrict users to a 180 degree viewport, leaving information off-screen. Since users naturally direct their heads toward regions on-screen before interacting, head movements offer a promising alternative for workspace manipulation to bring off-screen content into view. We explore rate control functions (linear, sigmoid, polynomial) and zone control functions (continuous, friction, interrupted, additive) to translate head rotations into workspace control, enabling users to access off-screen content. Polynomial rate control emerges as the best choice, achieving the fastest trial times and highest subjective ratings. Using a map navigation task, our second study demonstrates that users perform better with the polynomial head-based technique than with the industry-standard controller-based methods, click-and-drag and joystick-push, for 360\degree workspace navigation. Based on these findings, we provide guidelines to inform the design of future 360\degree workspace navigation techniques for large curved displays.
Paper Structure (56 sections, 9 equations, 10 figures, 1 table)

This paper contains 56 sections, 9 equations, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Mapping Functions for Workspace Navigation
  4. Interaction with 360$^{\circ}$ Workspaces
  5. Workspace Navigation using Head and Gaze Input
  6. Central Factors for accessing off-screen information
  7. Mapping Functions
  8. Rate control
  9. Zone control
  10. Display Window
  11. Target Distance
  12. Study 1 -- Mapping functions
  13. Apparatus
  14. Participants
  15. Tasks and Procedure
  16. ...and 41 more sections

Figures (10)

  • Figure 1: (a) A user can control the velocity of the workspace movement by rotating the head in the horizontal direction. The workspace velocity is set to zero between $\pm10$°, allowing the user to stop moving the workspace. Rate control mapping function (b) Linear, (c) Sigmoid, (d) Polynomial where the vertical axis represents the velocity and the horizontal axis the head rotation.
  • Figure 2: (a) Four zones: Stop, Constant, Dynamic, and Flick, for the zone control techniques; (b) three different display window sizes (400cm, 600cm, and 800cm); and (c) three different target distances (500cm, 750cm, and 1000cm) used in the user study.
  • Figure 3: Zone control techniques mapping Function (a) Continuous, (b) Friction, (c) Additive, (d) Interrupted where the vertical axis is the velocity of the workspace and the horizontal axis shows head movement performed by the user.
  • Figure 4: Study 1. (a) 3D model showing the curved display hardware setup. (b-f) Study tasks for polynomial mapping function.
  • Figure 5: (a) Trial time by Mapping Function for each Target Distance, (b) Total head movement time by Mapping Function for each Target Distance and (c) Crossing by Mapping Function for each Display Window. Error bars represent 95% confidence intervals. Capped horizontal bars denote significance.
  • ...and 5 more figures