Ninja Cursors Using Multiple Cursors to Assist Target Acquisition on Large Screens Masatomo Kobayashi (The University of T...
Outline Background & Motivation Our Method Evaluation Discussion & Future Work Conclusion
Background Large display
Background Multi-display
Background (Virginia Tech) BlueSpace (IBM) Larger screens
Problem It is difficult to point to a distant object.
Introducing “ninja cursors” Demo
Basic idea of “ninja cursors”    The user can use the nearest cursor. Cover the screen with multiple, synchronously movin...
Reducing the distance ( n  : # of cursors) Average distance from the nearest cursor: n  = 1 n  = 4 D 
Studies on target pointing e.g., [Fitts 1954] Target Size Target Density e.g., [Guiard et al. 2004] + Cursor Size e.g., [K...
Outline Background & Motivation Our Method Evaluation Discussion & Future Work Conclusion
Ambiguity problem What happens if multiple cursors point to multiple targets simultaneously?
Resolving ambiguity Only one cursor can point to a target; others are blocked and in the waiting queue. Queued Pointing Le...
Resolving ambiguity Demo
Visual feedbacks Normal Pointing Blocked
Visual feedbacks Short waiting Long waiting Pointing
Outline Background & Motivation Our Method Evaluation Discussion & Future Work Conclusion
Goal Determine how the cursor number and the target density affect the performance.
Hypothesis # of Cursors Movement Time Effect of cursor blocking Effect of distance reduction
Design <ul><li>8 participants (within-participant) </li></ul><ul><li>4 cursor types ×3 target  numbers ×3 target sizes </l...
Setup
Cursor types 2 cursors 8 cursors 18 cursors 1 cursor (standard cursor)
Target numbers N  = 1 N  = 100 N  = 400
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  = 10...
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
Feedback & observation  <ul><li>The participants annoyed by frequent waiting ( N  = 18) </li></ul><ul><li>The participants...
Outline Background & Motivation Our Method Evaluation Discussion & Future Work Conclusion
Advanced features Drag & drop Lasso tool
Drag & drop Drag Drop Drag with a cursor, drop with another cursor
Drag & drop Demo
Lasso tool Resolving ambiguity by implicit rules
Lasso tool 1. No lasso stroke ever intersects with targets. 2. Any lasso must contain at least one target. lasso not a las...
Lasso tool Demo
Limitations Direct pointing devices   cannot be used. Dense targets   increase the  MT  too much.
Future work Combination with other techniques Measure the decision time
Decision time Cursors are visible even before each trial. Cursors are hidden until the start of each trial. vs. Compare 2 ...
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
Regularly distributed targets
Regularly distributed targets
Regularity of cursors or targets ≈
Extra cursors or extra targets ≈
Bubbling ninja cursors
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
Post-selection method Use a post-selection menu instead of a waiting queue.  +  Does not increase the  MT  so much. +  Doe...
Post-selection method Demo
Post-selection method Pie menu
Outline Background & Motivation Our Method Evaluation Discussion Conclusion
Related work Delphian Desktop [Asano et al. 2005]   Jump the cursor Bubble Cursor  [Grossman & Balakrishnan 2005]   Chan...
Conclusion  <ul><li>Ninja cursors </li></ul><ul><ul><li>Multiple cursors cover a large screen </li></ul></ul><ul><li>User ...
Thank you  <ul><li>http://www-ui.is.s.u-tokyo.ac.jp/~kobayash/ninja_cursors.html </li></ul>
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Ninja Cursors: Using Multiple Cursors to Assist Target Acquisition on Large Screens (presented at CHI 2008)

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  • Ninja Cursors

    1. 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. 2. Outline Background & Motivation Our Method Evaluation Discussion & Future Work Conclusion
    3. 3. Background Large display
    4. 4. Background Multi-display
    5. 5. Background (Virginia Tech) BlueSpace (IBM) Larger screens
    6. 6. Problem It is difficult to point to a distant object.
    7. 7. Introducing “ninja cursors” Demo
    8. 8. Basic idea of “ninja cursors”  The user can use the nearest cursor. Cover the screen with multiple, synchronously moving cursors.
    9. 9. Reducing the distance ( n : # of cursors) Average distance from the nearest cursor: n = 1 n = 4 D 
    10. 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. 11. Outline Background & Motivation Our Method Evaluation Discussion & Future Work Conclusion
    12. 12. Ambiguity problem What happens if multiple cursors point to multiple targets simultaneously?
    13. 13. Resolving ambiguity Only one cursor can point to a target; others are blocked and in the waiting queue. Queued Pointing Left Pointing
    14. 14. Resolving ambiguity Demo
    15. 15. Visual feedbacks Normal Pointing Blocked
    16. 16. Visual feedbacks Short waiting Long waiting Pointing
    17. 17. Outline Background & Motivation Our Method Evaluation Discussion & Future Work Conclusion
    18. 18. Goal Determine how the cursor number and the target density affect the performance.
    19. 19. Hypothesis # of Cursors Movement Time Effect of cursor blocking Effect of distance reduction
    20. 20. Design <ul><li>8 participants (within-participant) </li></ul><ul><li>4 cursor types ×3 target numbers ×3 target sizes </li></ul><ul><li>10 trials for each condition </li></ul>
    21. 21. Setup
    22. 22. Cursor types 2 cursors 8 cursors 18 cursors 1 cursor (standard cursor)
    23. 23. Target numbers N = 1 N = 100 N = 400
    24. 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. 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. 26. Feedback & observation <ul><li>The participants annoyed by frequent waiting ( N = 18) </li></ul><ul><li>The participants often used the second- or third-nearest cursor. </li></ul>
    27. 27. Outline Background & Motivation Our Method Evaluation Discussion & Future Work Conclusion
    28. 28. Advanced features Drag & drop Lasso tool
    29. 29. Drag & drop Drag Drop Drag with a cursor, drop with another cursor
    30. 30. Drag & drop Demo
    31. 31. Lasso tool Resolving ambiguity by implicit rules
    32. 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. 33. Lasso tool Demo
    34. 34. Limitations Direct pointing devices cannot be used. Dense targets increase the MT too much.
    35. 35. Future work Combination with other techniques Measure the decision time
    36. 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. 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. 38. Regularly distributed targets
    39. 39. Regularly distributed targets
    40. 40. Regularity of cursors or targets ≈
    41. 41. Extra cursors or extra targets ≈
    42. 42. Bubbling ninja cursors
    43. 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. 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. 45. Post-selection method Demo
    46. 46. Post-selection method Pie menu
    47. 47. Outline Background & Motivation Our Method Evaluation Discussion Conclusion
    48. 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. 49. Conclusion <ul><li>Ninja cursors </li></ul><ul><ul><li>Multiple cursors cover a large screen </li></ul></ul><ul><li>User study </li></ul><ul><ul><li>More cursors  efficient in sparse targets inefficient in dense targets </li></ul></ul><ul><li>Advanced features </li></ul><ul><ul><li>Drag & drop, lasso tool </li></ul></ul>
    50. 50. Thank you <ul><li>http://www-ui.is.s.u-tokyo.ac.jp/~kobayash/ninja_cursors.html </li></ul>

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