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

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 What is Ergonomics?

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 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 In the US "human factors engineering" was emphasized by the US military with concentration on human engineering and engineering psychology.

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

Gilbreth Video

Hawthorne Effect Worker Study ( ) 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.

Measure of Man, Henry Dreyfuss, 1960

Occupational Safety and Health Administration, (OSHA, 1970,

Neutral Posture for Computer Use Adjust the seat height so upper arms hang vertically, elbows bent about 90 degrees, shoulders relaxed and wrists fairly straight 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) Use a document holder close to the monitor rather than laying papers flat 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. Adjust the back rest to provide firm support to the small of the back

Paul M. Fitts, 1954 Fitts connected the speed-accuracy tradeoff of choice reaction times to reaching movement tasks

Fitts’ “Law” T = a + b log 2 ( )AW ID AW Parameters a, b experimentally determined

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 Acceleration ( ) is piecewise constant

Assumption 2 Acceleration is proportional to target width Wider targets are easier to reach  larger accelerations possible

Optimal Control Given a bound on, Fastest way to reach a target is to use “bang-bang” control T/2T/2 T

Optimal Bang-Bang Control Velocity s = T/2T Position at time T:

Optimal Bang-Bang Control Position A2A2 A s = T/2 T 

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 Model Assume: Optimal bang-bang model: Add reaction time a : Parameters a, b set from experimental data

First Mouse (Douglas Engelbart and William English, 1964)

First Mouse Patent (Engelbart) (Shumin Zhai, IBM Almaden Research Center)

Modern Input Devices

Fitts’ Law Java Applet

Experimental Tests Fixed Rectangle Test Variable Rectangle Test Circle Test Homogeneous Cursor Motions Heterogeneous Cursor Motions

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 Parameter set using least-squares linear regression for each user Average parameters over all users:

Typical User

Models with Lowest RMS Error

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

Human Factors and Ergonomics Britain - The Ergonomic Society was formed in 1952 United States - The Human Factors Society was formed in 1957.

Human Factors and Fitts’ Law Ken Goldberg, IEOR and EECS, UC Berkeley

Cupstacking Video

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 J. Merkel, 1885: Stimuli 1,…,N equally likely. T R = a + b log 2 N Stimulus: 1,…,N Response: 1,…,N 

Information Theory Base 2 logarithm of the number of alternatives is a measure of information Number of bits = log 2 N Corresponds to the average number of yes/no questions required to identify correct stimulus In example: log 2 8 = 3 bits

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  # possibilities before event: 8 # possibilities after event: 2 Information transmitted: -log 2 (2/8) = 2 bits Uncertainty: 1 bit

Discrete vs. Continuous Choice Start Position Target Amplitude A Width W

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 Welford, 1968 Weber fraction: W/(A+0.5W) Start Position Target Amplitude A Width W

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

Outline Fitts’ Law Introduction Kinematics Models of Fitts’ Task –Symmetric Binary Acceleration Model –Asymmetric Binary Acceleration Model Fitts’ Task in HCI Web-based Experiments

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 1.Velocity profiles are asymmetric 2.Asymmetry increases as target width decreases 3.Amplitude has relatively little effect on asymmetry C.L. MacKenzie et al, 1987 [ ]

Asymmetric Binary Acceleration Model Assume: Percent time accelerating increases with W Asymmetric velocity profile: sT Acceleration is constant a Deceleration set so distance A reached at time T

Asymmetric Velocity Profile sT 

Asymmetric Model Position sT 

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 Model Velocity v Movement Time T a

Asymmetric Binary Acceleration Model Velocity v Movement Time T s

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 First mouse (1964): Douglas Engelbart and William English