1 / 21

Jeffrey Heer · 14 May 2009

Input Techniques. Jeffrey Heer · 14 May 2009. W. D. Pointing Device Evaluation. Experimental task: target acquisition abstract, elementary. Real task: interacting with GUIs pointing is fundamental. Index of Difficulty ( ID ). Fitts’ Law [Paul Fitts, 1954]. MT = a + b log 2 (D/W + 1).

sundberg
Download Presentation

Jeffrey Heer · 14 May 2009

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Input Techniques Jeffrey Heer · 14 May 2009

  2. W D Pointing Device Evaluation • Experimental task: target acquisition • abstract, elementary Real task: interacting with GUIs • pointing is fundamental

  3. Index of Difficulty (ID ) Fitts’ Law[Paul Fitts, 1954] MT = a + b log2 (D/W + 1) Models well-rehearsed selection task MT increases as the distance to the target increases MT decreases as the size of the target increases a, b = constants (empirically derived) D = distance W = size Index of Performance (IP ) = 1/b (bits/s)

  4. Experimental Data

  5. Considers Distance and Target Size MT = a + b log2 (D/W + 1) Target 1 Target 2 Same ID → Same Difficulty

  6. Target 1 Target 2 Considers Distance and Target Size MT = a + b log2 (D/W + 1) Smaller ID → Easier

  7. Target 1 Considers Distance and Target Size MT = a + b log2 (D/W + 1) Target 2 Larger ID → Harder

  8. What does Fitts’ law really model? Target Width Velocity (c) (a) (b) Distance

  9. Comparing device performance Reference: MacKenzie, I. Fitts’ Law as a research and design tool in human computer interaction. Human Computer Interaction, 1992, vol. 7, pp. 91-139 Device Study IP (bits/s) Hand Fitts (1954) 10.6 Mouse Card, English, & Burr (1978) 10.4 Joystick Card, English, & Burr (1978) 5.0 Trackball Epps (1986) 2.9 Touchpad Epps (1986) 1.6 Eyetracker Ware & Mikaelian (1987) 13.7

  10. Pop-up Linear Menu Pop-up Pie Menu Today Sunday Monday Tuesday Wednesday Thursday Friday Saturday Using laws to predict performance Which will be faster on average? • Pie menu (bigger targets & less distance)?

  11. Index of Difficulty (ID ) Fitts’ Law[Paul Fitts, 1954] MT = a + b log2 (D/W + 1) Models well-rehearsed selection task MT increases as the distance to the target increases MT decreases as the size of the target increases a, b = constants (empirically derived) D = distance W = size Index of Performance (IP ) = 1/b (bits/s)

  12. Beyond pointing: trajectories Steering Law Accot & Zhai

  13. EdgeWrite Corner-based text input Uses physical constraints Hard edges and corners Can help offset motor impairments

  14. Crossing UIs [Apitz & Guimbretière 04]

  15. Yves Guiard: Kinematic Chain Asymmetry in bimanual activities “Under standard conditions, the spontaneous writing speed of adults is reduced by some 20% when instructions prevent the non-preferred hand from manipulating the page” Non-dominant hand (NDH) provides a frame of reference for the dominant hand (DH) NDH operates at a coarse temporal and spatial scale; DH operates at a finer scales

More Related