stanford hci groupFeb 9, 2009 Björn Hartmann Understanding & Modeling Input Devices

Questions for today 1.How do common input devices work? 2.How can we think about the larger space of all possible input devices? 3.Can we predict human input performance? Next class: What about uncommon input devices (music controllers, multitouch, …)? 2

Today’s lecture in graph form 3 time Level of abstraction concrete details abstract models Functional Dissection of Mouse & Keyboard Design Space of Input Devices Modeling Human Performance

I spilled coffee on my keyboard. Now 25% of the keys don’t work anymore. But some of the defective keys are nowhere near the spill. What’s going on?

5

Key cap Top conductive layer Bottom conductive layer Separating layer (with hole)

Key cap Top conductive layer Bottom conductive layer Separating layer (with hole)

9

Row/Column Scanning 10 Q Q W W E E R R T T A A S S D D F F G G Z Z X X C C V V B B R1 R2 R3 R4 C1 C2 C3C4C5 9 lines 20 keys

Mouse. Engelbart and English ~1964 Source: Card, Stu. Lecture on Human Information Interaction. Stanford, 2007.

A Layered Framework 12 From: Hartmann, Follmer, Klemmer: Input Devices are like Onions

13

14

15 Right button Left button Encoder wheel for scrolling

16 IR emitter IR detector slotted wheel (between emitter & detector)

Sensing: Rotary Encoder 17 High

Sensing: Fwd Rotation 18 Low

Sensing: Backwd Rotation 19 Low Oops!

Solution: Use two out-of-phase detectors 20 High

Sensing: Rotary Encoder 21 Low High

Sensing: Rotary Encoder 22 High Low Coding: HH-> LH: dx = 1 HH-> HL: dx = - 1

Transformation 23 cx t = max(0, min( sw, cx t-1 +dx*cd )) cy t = … cx t : cursor x position in screen coordinates at time t dx: mouse x movement delta in mouse coordinates sw: screen width cd: control-display ratio

Device Abstraction  Click, DoubleClick, MouseUp, MouseDown, MouseMove … 24

What about optical mice? 25 Source:

bbbbbbbbbb 26 Source:

27 Trackball, Trackpad

28 Trackpoint  Indirect, force sensing, velocity control  Nonlinear transfer function Force Velocity (cc) Image by flickr user tsaiid

29 Joysticks

A design space of input devices… Card, S. K., Mackinlay, J. D., and Robertson, G. G A morphological analysis of the design space of input devices. ACM TOIS 9, 2 (Apr. 1991),

Implicit Assumptions: Desktop-centric computing

Which device is fastest?  For what task? Pointing.  Combination of two factors:  Bandwidth of human muscle group (upper limit)  Bandwidth of device itself 33

Bandwidth of Human Muscle Groups Source: Card, Stu. Lecture on Human Information Interaction. Stanford, 2007.

Fitts’ Law  Time T pos to move the hand to target size S which is distance D away is given by:  T pos = a + b log 2 (2D/S)  Time to move the hand depends only on the relative precision required Source: Landay, James. “Human Abilities”. CS160 UC Berkeley.

Mouse vs. Headmouse Source: Card, Stu. Lecture on Human Information Interaction. Stanford, 2007.

Headmouse: No chance to win Source: Card, Stu. Lecture on Human Information Interaction. Stanford, 2007.

Fitts’ Law in Windows & Mac OS Windows 95: Missed by a pixel Windows XP: Good to the last drop The Apple menu in Mac OS X v10.4 Tiger.Mac OS X v10.4 Tiger Source: Jensen Harris, An Office User Interface Blog : Giving You Fitts. Microsoft, 2007; Apple

Fitts’ Law in Microsoft Office 2007 Larger, labeled controls can be clicked more quickly Mini Toolbar: Close to the cursor Magic Corner: Office Button in the upper-left corner Source: Jensen Harris, An Office User Interface Blog : Giving You Fitts. Microsoft, 2007.

stanford hci groupFeb 9,