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Human Factors and Fitts’ Law Ken Goldberg, IEOR and EECS, UC Berkeley. What is Ergonomics? . Prof. Wojciech Jastrzebowski in Poland in 1857: From two Greek words Ergon meaning work and Nomos meaning principles or laws Ergonomics = The Science of Work. What is Ergonomics? .

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slide1

Human Factors and Fitts’ Law

Ken Goldberg, IEOR and EECS, UC Berkeley

what is ergonomics
What is Ergonomics?

Prof. Wojciech Jastrzebowski

in Poland in 1857:

From two Greek words

Ergon meaning work

and

Nomos meaning principles or laws

Ergonomics = The Science of Work

common definitions

What is Ergonomics?

Common Definitions

“Ergonomics is essentially fitting the workplace to the worker. The better the fit the higher the level of safety and worker efficiency.” Fitting the Task to the Human ~ Grandjean 1990

“Ergonomics removes barriers to quality, productivity and human performance by fitting products, tasks, and environments to people.” ErgoWeb.com

human factors
Human Factors

What Is Human Factors?

The following definition was adopted by the International Ergonomics Association in August 2000:

Ergonomics (or human factors) is the scientific discipline concerned with the understanding of interactions among humans and other elements of a system, and the profession that applies theory, principles, data, and other methods to design in order to optimize human well-being and overall system performance.

human factors and ergonomics
Human Factors and Ergonomics
  • Britain - The Ergonomic Society was formed in 1952 with people from psychology, biology, physiology, and design.
  • United States - The Human Factors Society was formed in 1957. In the US "human factors engineering" was emphasized by the US military with concentration on human engineering and engineering psychology.
slide7

from Mike Mandel, Making Good Time (CMP Bulletin vol. 8 no. 2, California Museum of Photography, UC California, Riverside, 1989)

slide9

Hawthorne Effect

Worker Study (1927 - 1932) of the Hawthorne Plant of the Western Electric Company in Cicero, Illinois. Led by Harvard Business School professor Elton Mayo: Effect of varying light levels on Productivity.

neutral posture for computer use
Neutral Posture for Computer Use

Position the monitor about an arm’s length away directly in front of you. The top of the screen no higher than eye level (Unless the user wears bi-focal glasses)

Adjust the seat height so upper arms hang vertically, elbows bent about 90 degrees, shoulders relaxed and wrists fairly straight

Use a document holder close to the monitor rather than laying papers flat

Adjust the back rest to provide firm support to the small of the back

Mouse should be next to keyboard both at a height equivalent to the user’s seated elbow height

Knees comfortably bent with feet resting on the floor. If the chair is raised so the keyboard height equals elbow height, use a footrest .

paul m fitts 1954
Paul M. Fitts, 1954

Fitts connected the speed-accuracy tradeoff of choice reaction times to reaching movement tasks

fitts law

A

W

ID

A

W

Fitts’ “Law”
  • T = a + b log2( )

Parameters a, b experimentally determined

alternative square root law
Alternative: Square-root Law
  • Fitts’ Logarithmic Law is not derived using biomechanics and kinematics
  • We derive a “Square-root” Law:

based on 2 simple assumptions

assumption 1
Assumption 1

Acceleration ( ) is piecewise constant

assumption 2
Assumption 2

Acceleration is proportional to target width

Wider targets are easier to reach

 larger accelerations possible

optimal control
Optimal Control
  • Given a bound on , Fastest way to reach a target is to use “bang-bang” control

T/2

T

optimal bang bang control velocity
Optimal Bang-Bang Control Velocity

s = T/2

T

Position at time T:

optimal binary acceleration model
Optimal Binary Acceleration Model
  • Use Assumption 2 to specify a single formula that relates A, W, and T
  • Assumption 2 Hypothesis:

Maximal acceleration set by the human is proportional to target width

(Wider targets permit larger accelerations)

optimal binary acceleration model25
Optimal Binary Acceleration Model
  • Assume:
  • Optimal bang-bang model:
  • Add reaction time a:
  • Parameters a,b set from experimental data
first mouse patent engelbart
First Mouse Patent (Engelbart)

(Shumin Zhai, IBM Almaden Research Center)

experimental tests
Experimental Tests

Homogeneous Cursor Motions

Heterogeneous Cursor Motions

Fixed Rectangle Test

Variable Rectangle Test

Circle Test

available data
Available Data
  • Original data set:
    • 2232 users for fixed rectangle tests
    • 2466 users for variable rectangle tests
    • 1897 users for circle test
    • User did not complete all trials  Removed
    • User has outlier points  Removed
  • Final data set:
    • 1640 users for fixed rectangle tests
    • 1996 users for variable rectangle tests
    • 1561 users for circle tests
model parameters
Model Parameters
  • Parameter set using least-squares linear regression for each user
  • Average parameters over all users:
effect size
Effect Size
  • Mean signed difference in RMS errors between the Square-root Law and Fitts’ Logarithmic Law, as a percent of the mean RMS error for Fitts’ Logarithmic Law, with 95% confidence intervals

Square-root Law better

Logarithmic Law better

web based fitts law demo
Web-Based Fitts’ Law Demo

www.tele-actor.net/fitts/

human factors and ergonomics37
Human Factors and Ergonomics
  • Britain - The Ergonomic Society was formed in 1952
  • United States - The Human Factors Society was formed in 1957.
slide38

Human Factors and Fitts’ Law

Ken Goldberg, IEOR and EECS, UC Berkeley

outline
Outline
  • Fitts’ Law Introduction
  • Kinematics Models of Fitts’ Task
    • Symmetric Binary Acceleration Model
    • Asymmetric Binary Acceleration Model
  • Fitts’ Task in HCI
  • Web-based Experiments
choice reaction time task

1

2

3

4

5

6

7

8

Choice Reaction Time Task

Stimulus: 1,…,N Response: 1,…,N

J. Merkel, 1885: Stimuli 1,…,N equally likely.

TR = a + b log2N

4

information theory
Information Theory
  • Base 2 logarithm of the number of alternatives is a measure of information

Number of bits = log2N

Corresponds to the average number of yes/no questions required to identify correct stimulus

  • In example:

log2 8 = 3 bits

fitts information theory approach
Fitts’ Information Theory Approach
  • Define “information” encoded in a reaching moving task
  • Information transmitted I in a response is a measure of the reduction in uncertainty
information transmitted

1

2

3

4

5

6

7

8

Information Transmitted

7-8

  • # possibilities before event: 8
  • # possibilities after event: 2
  • Information transmitted: -log2(2/8) = 2 bits
  • Uncertainty: 1 bit

000

001

010

011

100

101

110

111

discrete vs continuous choice

1

2

3

4

5

6

7

8

Discrete vs. Continuous Choice

000

001

010

011

100

101

110

111

Target

StartPosition

Width W

Amplitude A

fitts formulation
Fitts’ Formulation

Number of possibilities after response: W

Number of possibilities before response: 2A

Information transmitted = Index of Difficulty

weber fraction formulation of fitts task
Weber Fraction Formulation of Fitts’ Task
  • Welford, 1968
  • Weber fraction: W/(A+0.5W)

Target

StartPosition

Width W

Amplitude A

shannon formulation of fitts task
Shannon Formulation of Fitts’ Task
  • Formulation based on Shannon’s Theorem[I. Scott MacKenzie 1992]
  • Shannon Formulation for Fitts’ Task:
  • C = Information capacity of communication channel
  • B = channel bandwidth
  • S = signal strength
  • N = noise power
outline51
Outline
  • Fitts’ Law Introduction
  • Kinematics Models of Fitts’ Task
    • Symmetric Binary Acceleration Model
    • Asymmetric Binary Acceleration Model
  • Fitts’ Task in HCI
  • Web-based Experiments
outline52
Outline
  • Fitts’ Law Introduction
  • Kinematics Models of Fitts’ Task
    • Symmetric Binary Acceleration Model
    • Asymmetric Binary Acceleration Model
  • Fitts’ Task in HCI
  • Web-based Experiments
velocity profiles of fitts task
Velocity Profiles of Fitts’ Task
  • Velocity profiles are asymmetric
  • Asymmetry increases as target width decreases
  • Amplitude has relatively little effect on asymmetry

[ ]

C.L. MacKenzie et al,1987

asymmetric binary acceleration model
Asymmetric Binary Acceleration Model

Assume: Percent time accelerating increases with W

Asymmetric velocity profile:

Acceleration is constant a

Deceleration set so distanceA reached at time T

s

T

asymmetric binary acceleration model57
Asymmetric Binary Acceleration Model
  • Add reaction time a:
  • Parameters a,b set from experimental data
  • Same formula as Optimal Binary Acceleration Model;Different assumptions and derivations
optimal binary acceleration model58
Optimal Binary Acceleration Model

Velocity v

a

Movement Time T

asymmetric binary acceleration model59
Asymmetric Binary Acceleration Model

s

Velocity v

Movement Time T

outline60
Outline
  • Fitts’ Law Introduction
  • Kinematics Models of Fitts’ Task
    • Symmetric Binary Acceleration Model
    • Asymmetric Binary Acceleration Model
  • Fitts’ Task in HCI
  • Web-based Experiments
mouse
Mouse
  • First mouse (1964): Douglas Engelbart and William English
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