CS376 Input Techniques - Stanford University

CS376 Input Techniques - Stanford University

stanford hci group / cs376 Input Techniques Jeffrey Heer 14 May 2009 http://cs376.stanford.edu Pointing Device Evaluation Real task: interacting with GUIs pointing is fundamental

Experimental task: target acquisition abstract, elementary W D 2

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

MT decreases as the size of the target increases 4 Experimental Data Considers Distance and Target Size MT = a + b log2 (D/W + 1)

Target 1 Same ID Same Difficulty Target 2 Considers Distance and Target

Size MT = a + b log2 (D/W + 1) Target 2 Targe t1 Smaller ID

Easier Considers Distance and Target Size MT = a + b log2 (D/W + 1) Target 2 Targe

t1 Larger ID Harder What does Fitts law really model? Target Width Velocit y

(c) (a) (b) Distance 9

Comparing device performance Device Study IP (bits/s) Hand Fitts (1954) 10.6 Mouse Card, English, & Burr (1978) Joystick Card, English, & Burr (1978)

Trackball Epps (1986) 2.9 Touchpad Epps (1986) 1.6 Eyetracker Ware & Mikaelian (1987) 10.4 5.0

13.7 Reference: MacKenzie, I. Fitts Law as a research and design tool in human computer interaction. Human Computer Interaction, 1992, vol. 7, pp. 91-139 10 Using laws to predict performance

Which will be faster on average? Pie menu (bigger targets & less distance)? Pop-up Linear Menu Today Pop-up Pie Menu Sunday

Monday Tuesday Wednesday Thursday Friday Saturday 11 12

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

MT decreases as the size of the target increases Beyond pointing: trajectories Steering Law Accot & Zhai 14

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

16 Crossing UIs [Apitz & Guimbretire 04] 17

18 20 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 21

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