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Ninja Cursors: Using Multiple Cursors to Assist Target Acquisition on Large Screens (presented at CHI 2008)

Ninja Cursors: Using Multiple Cursors to Assist Target Acquisition on Large Screens (presented at CHI 2008)

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  • 1. Ninja Cursors Using Multiple Cursors to Assist Target Acquisition on Large Screens Masatomo Kobayashi (The University of Tokyo) Takeo Igarashi (The University of Tokyo)
  • 2. Outline Background & Motivation Our Method Evaluation Discussion & Future Work Conclusion
  • 3. Background Large display
  • 4. Background Multi-display
  • 5. Background (Virginia Tech) BlueSpace (IBM) Larger screens
  • 6. Problem It is difficult to point to a distant object.
  • 7. Introducing “ninja cursors” Demo
  • 8. Basic idea of “ninja cursors”  The user can use the nearest cursor. Cover the screen with multiple, synchronously moving cursors.
  • 9. Reducing the distance ( n : # of cursors) Average distance from the nearest cursor: n = 1 n = 4 D 
  • 10. Studies on target pointing e.g., [Fitts 1954] Target Size Target Density e.g., [Guiard et al. 2004] + Cursor Size e.g., [Kabbash & Buxton 1995] + Cursor Density
  • 11. Outline Background & Motivation Our Method Evaluation Discussion & Future Work Conclusion
  • 12. Ambiguity problem What happens if multiple cursors point to multiple targets simultaneously?
  • 13. Resolving ambiguity Only one cursor can point to a target; others are blocked and in the waiting queue. Queued Pointing Left Pointing
  • 14. Resolving ambiguity Demo
  • 15. Visual feedbacks Normal Pointing Blocked
  • 16. Visual feedbacks Short waiting Long waiting Pointing
  • 17. Outline Background & Motivation Our Method Evaluation Discussion & Future Work Conclusion
  • 18. Goal Determine how the cursor number and the target density affect the performance.
  • 19. Hypothesis # of Cursors Movement Time Effect of cursor blocking Effect of distance reduction
  • 20. Design
    • 8 participants (within-participant)
    • 4 cursor types ×3 target numbers ×3 target sizes
    • 10 trials for each condition
  • 21. Setup
  • 22. Cursor types 2 cursors 8 cursors 18 cursors 1 cursor (standard cursor)
  • 23. Target numbers N = 1 N = 100 N = 400
  • 24. Movement Time ( MT ) N = 2, 8 worked well. 0 0.5 1 1.5 2 2.5 (sec) 1 cursor 2 cursors 8 cursors 18 cursors N = 1 N = 100 N = 400
  • 25. Error rate No significant trend. 0 1 2 3 4 5 6 7 N = 1 N = 100 N = 400 (%) 1 cursor 2 cursors 8 cursors 18 cursors
  • 26. Feedback & observation
    • The participants annoyed by frequent waiting ( N = 18)
    • The participants often used the second- or third-nearest cursor.
  • 27. Outline Background & Motivation Our Method Evaluation Discussion & Future Work Conclusion
  • 28. Advanced features Drag & drop Lasso tool
  • 29. Drag & drop Drag Drop Drag with a cursor, drop with another cursor
  • 30. Drag & drop Demo
  • 31. Lasso tool Resolving ambiguity by implicit rules
  • 32. Lasso tool 1. No lasso stroke ever intersects with targets. 2. Any lasso must contain at least one target. lasso not a lasso lasso not a lasso
  • 33. Lasso tool Demo
  • 34. Limitations Direct pointing devices cannot be used. Dense targets increase the MT too much.
  • 35. Future work Combination with other techniques Measure the decision time
  • 36. Decision time Cursors are visible even before each trial. Cursors are hidden until the start of each trial. vs. Compare 2 configuration:  Total Time = MT  Total Time = DT + MT
  • 37. Decision time 0 0.2 0.4 0.6 0.8 1 1.2 1.4 MT DT+MT Total Time (s) 1 cursor 4 cursors
  • 38. Regularly distributed targets
  • 39. Regularly distributed targets
  • 40. Regularity of cursors or targets ≈
  • 41. Extra cursors or extra targets ≈
  • 42. Bubbling ninja cursors
  • 43. Bubbling ninja cursors 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Point Bubble Movement Time (s) 1 cursor 4 cursors
  • 44. Post-selection method Use a post-selection menu instead of a waiting queue. + Does not increase the MT so much. + Does not modify the C-D gain.
  • 45. Post-selection method Demo
  • 46. Post-selection method Pie menu
  • 47. Outline Background & Motivation Our Method Evaluation Discussion Conclusion
  • 48. Related work Delphian Desktop [Asano et al. 2005]  Jump the cursor Bubble Cursor [Grossman & Balakrishnan 2005]  Change the cursor size Shadow Reaching [Shoemaker et al. 2007]  Use the shadow
  • 49. Conclusion
    • Ninja cursors
      • Multiple cursors cover a large screen
    • User study
      • More cursors  efficient in sparse targets inefficient in dense targets
    • Advanced features
      • Drag & drop, lasso tool
  • 50. Thank you
    • http://www-ui.is.s.u-tokyo.ac.jp/~kobayash/ninja_cursors.html