ERGONOMICS

Milind A. Pelagade

• “Ergonomics is an applied science concerned with the design of workplaces, tools, and tasks that match the physiological, anatomical, and psychological characteristics and capabilities of the worker.” Vern Putz-Anderson

• “The Goal of ergonomics is to ‘fit the job to the person,’ rather than making the person fit the job.” Ergotech

• “If it hurts when you are doing something, don’t do it.” Bill Black

What is Ergonomics?

• Dates back to Ramazzini 1700’s

• Gained significance during WWII for airplane cockpit layout

• Progressed slowly until the 80’s and 90’s with the advent of the computer and more efficient workplace design

• Now, guidelines are in place and greater use of technology

Evolution of Ergonomics

Why do we care about Ergonomics?

MSDs

Contact with Objects

Transportation Related

Falls

All Other

Workplace Violence

Source: Bureau of Labor Statistics Annual Survey, 1996

MUSCULOSKELATAL DISORDERS (MSDS)

REPRESENT 1 IN 3 LOST TIME INJURIES

TYPES OF INJURIES• Muscle pain

• Joint pain

• Swelling

• Numbness

• Restricted motion

• Repetitive stress injury

• Repetitive motion injury

• Cumulative trauma disorder

• Musculoskeletal disorder

TARGET REGIONS

• Back

• Upper extremities

• Lower extremities

0 2000 4000 6000 8000 10000

MSDs of the Back

Upper ExtremityMSDs

Average Claim

Average Costs per Claim ($)

MSDS COST TWICE THE AVERAGE WORKERS’ COMPENSATION CLAIM

Source: Workers’ Compensation data from Insurance Companies 1993

METHODS AND TOOLS IN ERGONOMICS

The study of measurement of human body is called anthropometry.

Product design must consider: ease of use, comfort and safety in use

Optimization for ergonomics understanding how human body works

Design parameters = f( physical measurement)

e.g. Chair seat height

LUMBAR SUPPORT

SEAT PANCHAIR HEIGHT

CALF CLEARANCE

FOOT REST

LUMBAR SUPPORT

SEAT PANCHAIR HEIGHT

CALF CLEARANCE

FOOT REST

Human size variations need to know the statistics of anthropometric data

ERGONOMICS:METHODOLOGY

1. Optimal product is designed based on anthropometric measurements;

2. Statistical variations of expected users are estimated;

3a. Design is modified to allow critical parameters to be adjusted by user so as to ‘fit’ the individual need

3b. Size variations are provided to cover estimated market (e.g. shoe sizes)

or

DESIGN PRINCIPLES AND RULES OF THUMB

• Provide Adequate Space to Turn the Whole Body• Do Not Reach Behind• Avoid Extremes of Joint Movement• Avoid Postural Extremes• Allow Natural, But Changing Postures• Avoid Static Loading of Muscles• Preserve the Natural Curves of the Spine

DESIGN PRINCIPLES• Apply Force Dynamically, Conserve Momentum• The Lower the Reach Target, The Better the Shoulder Posture• Work Should Be 2 - 4 Inches Below Elbow Height, In General• Do Not Lift the Elbow• If Workers Must Reach Up and Out, Keep the Reach Below

Heart Level• Pivot Movement About the Elbow• Keep Arm Motions Within the “Normal” Working Area

DESIGN PRINCIPLES• Apply Force Dynamically, Conserve Momentum• The Lower the Reach Target, The Better the Shoulder Posture• Work Should Be 2 - 4 Inches Below Elbow Height, In General• Do Not Lift the Elbow• If Workers Must Reach Up and Out, Keep the Reach Below

Heart Level• Pivot Movement About the Elbow• Keep Arm Motions Within the “Normal” Working Area

DESIGN PRINCIPLES• Give Support To the Body• Avoid Contact Stresses• Reduce Repetition (enlarge jobs, rotate, mechanize)• Reduce Force • Power With Motors - Not With Muscles• Avoid Vibration - Especially Resonate Frequency Ranges

Ergonomics

Focus:Interaction of humans with “devices”

Objective:

To understand, evaluate, and thereby, to improvethe interface between the human and the device

Ergonomics == Human Factors

Outline

1. Examples of product design related to ergonomics issues

2. Case Study: digital images and JPEG

3. Methodology and tools useful for HF

Example 1. Office desk and Chair

Question: How do we decide the height of the desk?

Depends on:

(a) the height of the chair

(b) the size of the person who will use them

…

Example 1. Chair ergonomics..

LUMBAR SUPPORT

SEAT PANCHAIR HEIGHT

CALF CLEARANCE

FOOT REST

LUMBAR SUPPORT

SEAT PANCHAIR HEIGHT

CALF CLEARANCE

FOOT REST

(i) Seat pan length calf clearance (> 5cm) to 95% women

(ii) The chair height contact lower thigh with both feet on floor

(iii) The seat pan angle: ± 6

(v) Backrest lumbar support ~15-25 cm above seat level

(iv) The arm rests height elbow height at rest

Example 1. Chair ergonomics...

LUMBAR SUPPORT

SEAT PANCHAIR HEIGHT

CALF CLEARANCE

FOOT REST

LUMBAR SUPPORT

SEAT PANCHAIR HEIGHT

CALF CLEARANCE

FOOT REST

(i) Seat pan length: calf clearance (> 5cm) to 95% women

(ii) The chair height: contact lower thigh with both feet on floor

IMPLICATIONS1. Need for adjustability

2. Design of a “good” chair depends on the statistics of the users

Example 1. Chair ergonomics – user statistics

Design of a “good” chair depends on the statistics of the users

LUMBAR SUPPORT

SEAT PANCHAIR HEIGHT

CALF CLEARANCE

FOOT REST

LUMBAR SUPPORT

SEAT PANCHAIR HEIGHT

CALF CLEARANCE

FOOT REST

USA Germany Japan Netherlands

Males 175.5 174.5 165.5 182.5

Females 162.5 163.5 153.0 169.6

Problem 1. What statistics are sufficient?

Problem 2. How to collect such statistics?

Problem 3. Statistics are time dependent: e.g. height of urban Chinese males: increased by 6 cm over the last 20 yrs

Example 2. Keyboard design

Extended periods of use of a computer in the wrong posture

repetitive stress injury (RSI)

Example 2. Keyboards: Carpal Tunnel Syndrome

why compression of the median nerve as it enters the hand. symptoms numbness of thumb and fingers, pain along the median nerve including hand, wrist, elbow, weakness of thumb.treatment rest, surgerymain cause flexed or extended wrists when keying!

Example 3. How to turn on the shower

Pull down this ring to turn shower on

tub-faucet

Non-intuitive design wasted time/user-discomfort

Example 4. Toilet flush (airport)

Non-intuitive design discomfort (for next user?!)

Example 5. Is the water too hot? Too cold?

Ergonomics

(i) Understanding of human physiology

(ii) Understanding of human psychology

(iii) Statistical data about populations

Goal:

-- Improve design (more efficient)

-- Improve design (safety, comfort)

Ergonomics Case Study: Improve design

Digital Image Files

What is the data composed of ? The “RGB-pixel model”

Digital Cameras (digicams) pictures in a digital “memory”

Digital image files: pixels

An image of a lion fish

What is the image made of ?

Digital image files: pixels

4x

4x

4x

4x

pixelPICture ELements

Digital image files: The RGB model

What is a color?

Store the wavelength, intensity at each pixel

Problem ? (Technical: how to display?)

The primary color theory: any color combination of primary colors (R, G, B)

at each pixel, decompose into primary color values, store R, G, B.

Digital image files: The RGB model

R = Red level: 8 bit number = 1 byteG = Green level: 8 bit number = 1 byteB = Blue level: 8 bit number = 1 byte

Original lionfish file: 1920x2560 = 4,915,200 pixels ( 5 Megapixel digicam)

1 Byte per color per pixel 4,915,200 x 3 x 1= 14,745,600 ~= 15 Mbytes

PROBLEMS:1. Large memory requirement

2. Slow transfer speed

need for COMPRESSION

Digital Image Files: compression

Strategy 1. Compress data without losing any information

LOSSLESS compression No need to understand human vision

Example: run-length-encoding

raster model: each pixel: 0 or 1

run-length-encoding: 0203,1403…,

203x191 pixels

Digital Image Files: compression

How we compress image files depends on how we “see” images

Understanding of human vision

more efficient compression technique

Strategy 2. Compress data by throwing away parts that we cannot see

Needs a good understanding of human vision

Digital Image Files: compression

Uncompressed: BMP (bitmap) 14 MByte

“Lossy” compression: JPG (JPEG)

High quality 3.67 MByte0.8 quality 0.83 MByte0.6 quality 0.5 MByte0.2 quality 0.2 MByte

Lossless compression: PNG 7.9 MByte

http://iesu5.ieem.ust.hk/dfaculty/ajay/courses/ieem101/lecs/hf/lionfish.html

JPG: How do we “see”

Do you believe what you see?

The Koffka ring

JPG: How do we “see”..

Do you believe what you see?

JPG: How do we “see”…

Do you believe what you see?

JPG: How do we “see” -- the eye

RODS: scotopic vision (in dark)

only ‘on’ in darkness

only distinguish “lightness”

CONES: photopic vision

JPG: How do we “see” -- the eye..

Trichromacy theory:different intensities of R- G- B- cones allows brain to “estimate”

frequency of the spectral light striking a zone

JPG: How do we “see” -- the eye...

Hue discrimination:ability to distinguish between two different wavelength’s of light

Lightness discrimination:ability to distinguish between two different levels of “lightness”

Lightness ~~ grey level

Lightness discrimination is MUCH more sensitive than Hue discrimination

Reasons:(a) lightness is estimated by (R+G) response of cones, and also from RODS(b) many more rods than cones

JPG: How do we “see” -- the eye….

Weber’s law:Our ability to discriminate “levels of lightness” depends n the ratio of lightness

Shades that are in geometric series “look” equally spaced in lightness.

arithmetic geometric

JPG: How do we “see” -- the eye…..

Hue discrimination vs Lightness discrimination

n cy

cles

n cycles

both patterns are n/ cycles per degree

eye

n cy

cles

n cycles

both patterns are n/ cycles per degree

eye

JPG: How to eliminate what we cannot see?

1. Intensity changes are much more significant than hue changes

2. Intensity change steps are logarithmic

PROBLEM: Technically, it is easier to handle R- G- B- shades

Why ?

(a) Recording instruments (digicams) sensors can sense “colors”

(b) Display instruments can handle RGB values better

JPG: How to eliminate what we cannot see..

Converting R G B Y Cb Cr

Luminance(lightness)

Chrominance(chroma) components

must be invertible mapping

YCbCr

0.299 0.587 0.114-0.169 -0.331 0.50.5 -0.419 -0.0813

RGB

=

JPG: How to eliminate what we cannot see…

YCbCr

0.299 0.587 0.114-0.169 -0.331 0.50.5 -0.419 -0.0813

RGB

=

JPEG compression:

Step 1. Convert RGB data into YCbCr dataStep 2. Sub-sample and quantize Cb and Cr dataStep 3. Compress resulting stream (run-length encoding)

file-size reduction

Higher compression: Step 2 sub-sample more, sub-sample Y also

JPG: Details -- How to Sample, Sub-sample?

Break the image into “tiles” of NxN pixels.

Store data of each tile

8

10 6

4 7

7 7

7

Example:

2x2 tile: 4 values average = 7 combine tiles into ‘block’ with value 7.

JPG: Details -- How to Sample, Sub-sample..

Sub-sampling and quantization basics

-2

-1

0

1

2

3

4

5

0 2 4 6 8

y1=2

y2=cos(x)

y3=.5cos(2x)

y4=.25cos(4x)

y5=.125cos(8x)

T=y1+..+y5

T2=y1+..+y4

How Fourier analysis works for 1-D functions

JPG: Details -- The Discrete Cosine Transform

DCT function:

)2/1(cos)2/1(cos),(4),(1

0

1

0

jnqi

npjiAqpf

n

i

n

j

8x8 pixel blocks

rows

01

…

7

columns: 0 1 … 7

DCT

DCT

DCT

Quantization(Larger lookup)

Quantization(Sub-sampled)

Quantization(Sub-sampled)

run-length-encoding(lossless compression)

JPEG

JPG: Details -- Quantization and encoding

JPG: Conclusions

1. Understanding of human sensory system is important for better product designs

2. Levels of adjustability useful for variations among users

-- older person with poor sight might prefer higher compression

NOTE:You don’t need to know details of DCT, and the exact mathematicsof the transformation

Important ideas:sub-sampling: ignore some data, or replace multiple values by the averagequantization: instead of storing exact value, round up/down to nearest step

Methods and tools in Ergonomics

The study of measurement of human body is called anthropometry.

Product design must consider: ease of use, comfort and safety in use

Optimization for ergonomics understanding how human body works

Design parameters = f( physical measurement)

e.g. Chair seat height

LUMBAR SUPPORT

SEAT PANCHAIR HEIGHT

CALF CLEARANCE

FOOT REST

LUMBAR SUPPORT

SEAT PANCHAIR HEIGHT

CALF CLEARANCE

FOOT REST

Human size variations need to know the statistics of anthropometric data