HUMAN PERFORMANCE MEASUREMENT, MODELING AND SIMULATION FOR AN ASSE.docx

HUMAN PERFORMANCE MEASUREMENT, MODELING
AND SIMULATION FOR AN ASSEMBLY TASK
BY
POONAM LAXMAN DESHMUKH, B.E.
ABSTRACT
HUMAN PERFORMANCE MEASUREMENT, MODELING
AND SIMULATION FOR AN ASSEMBLY TASK
BY
POONAM DESHMUKH, B.E.
Master of Science in Industrial Engineering (major) and
Electrical & Computer Engineering (minor)
New Mexico State University, Las Cruces, New Mexico, USA
The primary objective of this project is to measure, model and
simulate the human/operator performance in a manufacturing
cell to improve the decision making process of the managers. It
is well known that people working in a manufacturing facility
suffer from stress, fatigue and physical exhaustion due to
repetitive manual labor. The purpose of this project is to
identify and measure the performance metrics that affect the
worker’s performance and help in making decisions about
rotating the workers in such a way that their capability matches
the task requirement. The project involved, conducting a pilot
study to identify the metric of operator performance, physically
modeling and simulating an assembly station of a manufacturing
cell in a laboratory, measuring the identified metric (dexterity)
in the simulated and real environment and compare the results

from both the environments to evaluate the simulated assembly
station. Using the simulated assembly station, measurements of
several different metrics can be performed in future. The
primary outcome of this project is the operator task capability-
requirement matrix for the assembly station in terms of
dexterity. The secondary outcome of this project is the
evaluation of the simulated assembly station using t - student
test.
Keywords: Human performance measurement, dexterity,
manufacturing cell, operator performance measurement,
modeling and simulation.
TABLE OF CONTENTS
TOPICS
Page
1. INTRODUCTION
12
1.1. Metric Identification
12
1.2. Measurement
14
1.3. Modeling
14
1.4. Simulation
16

2. RELATED RESEARCH
17
3. METHODOLOGY
24
3.1. Pilot Study
24
3.1.1. Equipment and Software
24
3.1.2. Experiment Design
24
3.1.3. Data analysis and plots
25
3.2. Simulation
27
3.2.1. Equipment
27
28

29
3.3. Main Study
29
3.3.1. Equipment
29
29
29
4. RESULTS
29
5. DISCUSSION
29
6. CONCLUSION
29
APPENDICES
A. Operator Consent Form
29
B. Manager Consent Form
29

REFERENCES
29
LIST OF FIGURES
Figures
Page
1 Fish Bone Diagram
13
2 Anatomy of Hand
15
3 Task Requirement - Capability Model
16
4 (a) Human Glove
23
4 (b) Biomechanics Sensor Glove
23
5 (a) Average reactions Time Plot
25
5 (b) Concentration Plot
25
6 (a) Purdue Pegboard

28
6 (b) Hand - Tool Dexterity Test Equipment
28
INTRODUCTION
It is well known that human performance degrades with
repetitive work and time. Repeatability of an assembly task in a
manufacturing cell is one of the stressful tasks for a worker.
The assembly task can be analyzed to determine the time when
an operator gets tired and has to be rotated to some other station
for improving the overall cell performance. Operator
performance can be analyzed through four main phases - human
performance metric identification, measurement, modeling and
simulation. There are several causes that can affect the human
performance as shown in figure 1.
Figure 1: Fish Bone Diagram (Cause and Effect Diagram) for
Operator Performance in a Manufacturing Cell
Small despcription of the project
Metric Identification:
A metric can be defined as an important aspect to focus on that
can be measured over a period of time to communicate vital
information for a given situation. There exist several human
performance metrics that can be measured either by using
sensors or test equipment or software. For identifying the
human performance metric we conducted a pilot study and
surveyed the workers working in a manufacturing cell at
Johnson Controls, Juarez, Mexico. For this project, we

identified various metrics that fit for an assembly task:
· Range of motion (if an operator has to stretch for grabbing
parts of assemblies),
· Hand-eye coordination (if an operator has to see a drawing and
then perform assembly)
· Two-arm coordination (if an operator has to use both hands
equally for the assembly), posture (if operator is standing or
sitting for a long time)
· Memory (if an operator has to remember a difficult or varying
sequence of assembly parts)
· Concentration and attention (if operator surroundings are
disturbing)
· Reaction time (if more assemblies have to be performed in less
time)
· Manual dexterity (if operator finger movements have to be
agile or fast
Manual dexterity is identified as the most important metric
among all the other metrics identified. Manual dexterity is
defined as the ability to quickly move hand, hand together with
arm, or two hands to grasp or to make precisely coordinated
movements of the fingers to manipulate, or assemble objects
during repetitive tasks. There are two main types of manual
dexterity - fine dexterity and gross dexterity. Fine dexterity
refers to the ability to manipulate objects using the distal parts
of the fingers. Gross manual dexterity or simply manual
dexterity involves less refined and less precise movements of
the hand and fingers.
Measurement:
Sensors and goniometers that measure real time data can be
developed for range of motion and posture. Test equipment can
be developed for response speed, dexterity and repeatability
measurement. Software modules can be written for
concentration, memory and attention measurement. Data gloves
that consist of sensors for measuring range of bend of the finger

joints can be used to measure dexterity. For measuring
dexterity, we used Purdue Pegboard test and Hand - Tool
dexterity test equipment.
Modeling:
Operator in a manufacturing cell can be considered as an
interactive system. Mital et al. (1993) described several
operators modeling approaches to quantify the relationships
between the imposed stresses and resulting strain. These
approaches are:
Epidemiological: Epidemiology is concerned with discerning
the injury patterns present in groups of people doing similar
tasks and using these patterns to predict the occurrence of
injury. The main task in epidemiological modeling is gathering
good physical histories of individuals being considered. The
model is designed on the basis of past and present observations
and histories to predict future injuries.
Biomechanical:In this model, human body is treated as a system
of links and connecting joints and each of the links is the same
length and possesses the same mass and moment of inertia as
their corresponding human segments. This approach relies on
compression and shear forces of the spinal cord and pressures
generated in abdominal cavity. Some of the measuring methods
include Goniometer for range of motion. Figure 2 shows the
anatomy of a human hand.
Physiological: During repetitive handling tasks, a worker’s
work capacity is limited by the capacity of oxygen and nutrients
being delivered to the tissues and muscles. Some of the
physiological measurements include heart rate, blood pressure,
and metabolic energy expenditure. The design criterion is the
metabolic energy expenditure. Some of the measuring methods
include heart rate monitor, BP apparatus, and calorimeter. This

approach is based on the capacity-requirement principle.
Figure 2: Anatomy of Hand
Psychophysical:In this model, operators adjust their workload to
the maximum amount they can sustain without undue strain or
discomfort and without becoming unusually tired. The design
criteria are maximum acceptable frequency of handling and
maximum acceptable weight/force of handling. Some of the
measuring methods are weight lifting, strength testing.
Environmental: In this model, operators get affected by their
surroundings. Lighting, temperature, pressure, noise,
manufacturing cell layout, placement of workbenches and
machines, mood are some of the affecting factors. But as all the
workers are working in the same environment, most of these
factors are constant with respect to the workers.
Task Requirement - Capability: In this model, the managers try
their best to pull up the worker’s task capability towards the
task requirement. The task requirement is generally a
productivity chart with goals set. Task requirement is the
number of assemblies that “should” be performed by a worker in
a week, and the task capability is the number of assemblies that
a worker “can” perform in a week. Task requirement curve is
plotted before the week starts and the job capability curve is
plotted after the week ends and both curves can be analyzed for
enhancing the worker’s capability for following weeks. As the
margin between the task requirement and task capability of a
worker decreases, the stress on the worker increases and his or
her performance degrades gradually as shown in figure 3.
Figure 3: Task Requirement - Capability Model
Simulation:
An assembly station similar to the one at Johnson Controls was

emulated in the Human Performance Laboratory at New Mexico
State University. This assembly station was then physically
simulated so that there was no need of going to Johnson
Controls every time for data collection in future if the simulated
and real assembly stations were not significantly different. The
emulation consisted of creating an assembly station by
considering the lighting system, anti-fatigue mat, workbench,
sound, and temperature as in the manufacturing cell A19/A1 at
Johnson Controls. The simulation of the assembly station
consisted of assembling the parts by the subject in the lab as
assembled by Deysey (the worker at the assembly station in
Johnson Controls). The dexterity of subject and Deysey was
then compared to analyze the difference between the two
assembly stations to evaluate the simulated environment against
the real environment.
Describe a reason for having literature review road map
2. RELATED RESEARCH
Kondraske (1995a) proposed an elemental resource model
(ERM) that interfaces the tasks and the humans through demand
and availability of resources. ERM leads to the development of
instruments that measure human performance for several
different purposes (decision-making, manufacturing, medical,
rehabilitation progress, prediction). The ERM model for human-
task interface consists of a human model and the tasks imposed
on the pool of BEP (basic elements of performance). Entire
human in ERM is modeled as a pool of elemental performance
resources grouped into four domains (life sustaining,
environmental interface, central processing, and information),
first three of which are the physical systems (or functional
units) while the fourth one is an information base. A human is
represented as a set of available performance resources [RAij (t)
| Q], where i is the dimension of performance and j is a
functional unit, A is available and Q is the operating point.
There are three hierarchically levels of a given task - basic
element level, generic intermediate level and the high level. A

task is modeled in terms of demands imposed on elemental
resources and is represented as a set of performance resource
demands [RDij (t) | Q], where D is demand. A task is successful
when RAij >= RDij inequality is satisfied i.e. available
resources are greater than (are enough for) the resource
demands for a given task.
Kondraske (1995b, 1995c) and Kondraske and Khoury (1992)
derived a major component of the theoretical basis for the GSPT
(General Systems Performance Theory) workload model. Using
these constructs and equating workload to "stress" as defined in
GSPT, any human workload component (W) can be defined as
the fraction of available performance resources utilized during
task execution:
W (%) = (RDi/RAi) x 100
where RDi = amount of resource of type i demanded (i.e.,
utilized) and RAi = amount of resource of type i available.
This approach allows, by multiplying together the workload
quantities computed along separate performance dimensions, a
conceptually straight-forward method of representing workload
multi-dimensionally. Further, it provides the capability to
multiplicatively combine quantities representing workload of
subtasks or task components to derive composite measures for
predicting workload for upper-level tasks.
Abdel-Malek et al. (2000) discussed the metrics of human
performance and modeled them mathematically. The
analytically modeled mathematical equations can be
implemented in computer programs and by applying the data
collected, from several experiments, human performance can be
predicted and used for decision-making processes. The measures
or metrics discussed include reachability, dexterity, joint

functionality, ranges of motion, effort, energy, force, work and
power. The reachability can be measured by the volume
enclosed by the reach envelope and the range of motion.
Dexterity or orientability or manipulability is the degree of
possible orientations of an arm at a given target. The effort
needed to reach a destination is the displacement required for
each joint from initial position to the task completion. Energy is
measured in terms of the energy exerted or required by an
operator to perform a task. These metrics are useful to model
and evaluate human performance so as to automate the
ergonomic design process.
Abdel-Malek et al. (2005) presented a mathematical and
biomechanical model to solve the placement problem in an
ergonomic design. The placement of workers in an assembly
line should enable them to maximize dexterity, reach, and
minimize stress on joints for designing an assembly line
ergonomically. The ergonomic design process is an optimization
problem with many variables, dexterity being the major metric
or the driving cost function in a manufacturing environment.
The objective function is given by f = Dexterity (w), where w
(design variables) characterize the position and orientation of
the operator and f (design function) is a quantifying measure for
dexterity. As the human joints are constrained, every joint can
be characterized by an inequality constraint. The hand was
modeled by considering the upper arm, lower arm and hand as
three consecutive links and then analyzing the rotation of each
link with respect to the coordinate system of other two links.
Human performance Technology is an engineering approach to
attain the desired accomplishments through human performers.
The HPT approach focuses on three issues - problems,
opportunities and new situations. A model known as ADDIE
(analysis, design, development, implementation and evaluation)
was developed for teaching and learning purposes. The HP
technologists follow a stepwise method as in ADDIE to
implement a proper treatment for any problem. First step

consists of the problem definition which is the initiation of a
project. Second step is of analysis in which goals, techniques
and tools are identified. The analysis of a project is carried on
the organization level, the process level and the job or
performer level through interviews, observations, surveys, and
focus groups. Third step is of design and developments in which
a plan of action, design strategies, process developments are the
points of focus. Fourth step is of implementation and
maintenance in which the project is actually built or
implemented and undergoes several modifications according to
the changes in the design. Fifth step is of evaluation in which
the developed system is verified for its expected operation.
Pennathur et al. (2003) reported the results from an
experimental pilot study performed to quantify the manual
dexterity of older Mexican American adults. The Purdue
pegboard test, a two-arm coordination test, and a hand-tool
dexterity test were used in this study. The metrics identified
were two-arm coordination and hand-tool dexterity. Purdue
pegboard test was intended to measure two types of activities:
(1) gross movements of the hands, fingers and arms and (2)
finger dexterity, which can be considered as the ability to
integrate speed and precision with finely controlled discrete
movements of the finger. The two-arm coordination test was
intended as a measure of the ability to move both arms in a
simultaneous and coordinated manner. The movement involved
was that of the whole arm related to the ability of operating-
controlling and driving-operating. Hand-tool dexterity test was
used to measure the dexterity of participants when using
common hand tools. The hand-tool dexterity test was used to
measure the manipulative skill, independent of intellectual
factors. A t-Student test was conducted to compare the mean
responses from older adults with mean responses from younger
adults. All statistical analyses were carried out using Minitab
Version 13.31 statistical analysis software.
Valero-Cuevas et al. (2003) developed a method to evaluate the

S-D test used for quantifying the dynamic interaction between
fingertip force magnitude (strength) and directional control
(dexterity) during a pinch against a pinch meter. The metrics
identified were pinch force magnitude (strength) and direction
(dexterity). The method was based on the ability of participants
to use pinch to fully compress a compression spring prone to
buckling. A sufficiently slender compression spring will buckle
when shortened below a critical length. The S-D test consists of
asking participants to use key and opposition pinch to attempt
to fully compress a set of springs with plastic end caps
embodying a wide range of combinations of strength and
dexterity requirements. After the participant attempts to
compress each spring three times to its solid length using key or
opposition pinch, a binary score is used to record if they
succeeded at least once. The pinch force necessary to compress
the spring to solid length defines the strength requirement. The
ability to compress the spring without buckling defines the
dexterity requirement. The subject pool consisted of 42
participants: 18 unimpaired adults under the age of 40 yr, 10
unimpaired adults over the age of 40 yr and 14 adults with
carpo-metacarpal osteoarthritis (CMC OA) without neurological
co-morbidities such as carpal tunnel syndrome. For both pinch
styles, the S-D scores inside the core region were significantly
higher for the older adults than for the CMC OA participants.
Pinch meter readings were not significantly different across
subject groups for either pinch style. S-D score was at least
94% reproducible. Moreover, the S-D test distinguished
between CMC OA participants and asymptomatic older adults,
while maximal pinch strength from pinch meter readings did
not.
Beebe et al. (1998) presented a silicon-based force sensor
packaged in a flexible package and described the sensors
performance on human subjects. The metrics identified were
Finger and hand force, pinch force for grasping activities.
Silicon tactile sensors were used for measurement. The sensing
element consisted of a circular silicon diaphragm over a sealed

cavity with a solid Torlon dome providing force-to-pressure
transduction to the diaphragm. The sensor design is based on a
silicon diaphragm structure instrumented with ion-implanted
piezo-resistors in a Wheatstone bridge configuration. The
applied force is distributed across the diaphragm via the solid
dome. The distributed force deforms the diaphragm giving rise
to an output voltage proportional to the applied force for small
deflections. At high forces, the diaphragm deflection is
restricted by the cavity bottom limiting the maximum stress and
extending the useful range of the sensor.
Dipietro et al. (2003) investigated the feasibility of using the
Humanware Humanglove, a 20-position sensors glove, to
measure finger’s range of motion (ROM), with particular regard
to measurement repeatability. The metrics identified were
fingers’ ROM for measuring repeatability. The Humanglove is a
sensorized elastic fabric glove designed and commercialized by
Humanware and is used for measuring the finger ROM. The
Humanglove is equipped with 20 Hall Effect sensors that are
distributed. Each sensor measures data related to a DOF of the
hand. Four tests were conducted to measure repeatability - Mold
Grip and Glove on Between Data Acquisition, Mold Grip and
Glove off between Data Acquisition, Hand Flat and Glove on
between Data Acquisition, Hand Flat and Glove off between
Data Acquisition. The repeatability of measurements taken from
the Humanglove is adequate to recommend the system for
several applications in the field of rehabilitation engineering.
The Humanglove can function as goniometric device for digit
ROM acquisition. Moreover, as an additional advantage, the
glove dynamically acquires data simultaneously from 20 hand
degree of freedoms (DOF), including abduction and adduction
(ABD/ADD) of fingers and thumb.
Welsh et al. (2001) evaluated the space suit glove to be used in
and extravehicular activity (EVA) by using the standardized
dexterity tests to provide objective measures of glove
performance. The objective was to determine the effects of

gloves on hand performance, to collect and examine range of
motion data using an experimental data glove and to establish
baseline data that will be used in future research. The metrics
identified were performance time, range of motion, dexterity,
strength, fatigue, and comfort. Hand performance data was
collected for barehanded, unpressurized, and pressurized glove
conditions for each standardized dexterity test. Hand tool test,
Minnesota dexterity test and Purdue pegboard test were used to
evaluate the biomechanics sensor data glove. The Statistical
Analysis Software (SAS) package was used for the data
analysis. The data glove was found to be an economical means
of accurately measuring joint angles during simulated EVA
tasks.
Figure 4: (a) Humanglove and (b) Biomechanics Sensor Glove
Kamieniarz et al. (1999) developed a computer program for the
purpose of objectivisation of children hand dexterity. The
program is composed of 6 subtests checking the control of the
upper limb joints and the fine finger dexterity needed for mouse
control. The metrics identified were control of the upper limb
joints and the fine finger dexterity. Six tasks were developed to
check manipulation dexterity of the hand - called blocks,
labyrinth, ball, circle, board and centres. The program was
written in MS Visual Basic 5.0 and operated on PC platform in
Windows 95 system. The user interface is worked out in Polish.
An analysis was made to check if the results differ significantly
for boys and girls and the difference between computer-
familiar children and computer-unfamiliar children. ANOVA
and t-Student test were used for the statistical data analysis.
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Description of sensors and their use
METHODOLOGY
Pilot Study:
To identify the metric we conducted a pilot study on eight
operators (working on A19/A1 manufacturing cell) and four
managers at Johnson Controls, Juarez, Mexico. The main tasks
during the pilot study were - video taping of the assembly
station, the leveling & calibration station and the packaging
station, the operator survey, the manager survey and measuring
twice (before lunch and before end of the shift) the
concentration, memory and reaction time of eight operators
through simple computer games.
Equipment and Software: Digital camcorders, memory, reaction
time, concentration software games.
Experiment Design: We used three digital camcorders to record
the activities at the assembly station, the leveling & calibration
station and the packaging station. These videos were transferred
to DVDs (with the use of iMovie and iDVD software on the
Macintosh OS) to examine and identify the metric. From the
videos, “manual dexterity” was identified as the metric to be
measured during the main study. Eight operators were asked to
take a survey (Appendix A) which had questions about their
daily tasks and discomforts in Spanish. Four managers were

asked to take a survey (Appendix B) which had questions about
decision-making policies, operator trainings, operator rotations
and scheduling in English. The reaction time game consisted of
a click of a mouse as soon as the operators see an object on the
computer screen. This game was a direct indicator of the eye-
hand coordination as it measured the time (in milliseconds)
between a visual stimulus (object on the screen) and a response
(mouse click). The memory game consisted of counting the
moves the operators took to arrange 8 scrambled blocks in a 3x3
matrix form. This game was a direct indicator of problem
solving capability in less number of trials and it was found to be
a difficult test for most of the operators. The concentration
game consisted of correctly guessing 2 same pairs out of 5
covered pairs of cards in less number of guesses. This game was
a direct indicator of the short term memory and remembering
capacity. This was also a difficult game for most of the
operators. It was found that the operators do not really have to
work fast and think for their tasks, but they have to have eye-
hand coordination, two-arm coordination, hand-tool dexterity
and assembly sequence remembering capacity. So dexterity and
concentration were the metrics identified. But dexterity
dominates concentration in case of the assembly tasks because
there is more requirement of dexterously assembling the parts
than remembering sequences which don’t have to be
remembered after a few days assembly experience.
Data Analysis: From the reaction time plot shown in figure 6
(a), it can be observed that the reaction times before lunch were
lower and by the end of the shift they were higher. It is good to
have a lower reaction time for an operator. The workers work
fast in the morning and work slow by the end of the day as they
are tired.
Figure 6: (a) Average reaction Time Plot and (b) Concentration
plot
From the concentration plot shown in figure 6 (b), it can be

observed that more the experience of a worker in the facility
more is his/her concentration except for one outlier (operator
3). Workers who are working for more number of years in the
facility, guessed the same card pairs in less number of guesses
while those with less number of years in the facility, guessed
the same card pairs in more number of guesses. For less
experienced workers, the concentration was more before lunch
and less at the end of the shift while for more experienced
workers, the concentration was almost same throughout the day.
From the operator’s survey, it is understood that the workers did
not know the technical terms for the operations they were doing,
the machines on which they were working and the sub-
assemblies or parts they were using. All the workers like
rotation, and going from one cell to another as they all like
change in work. They all were uncomfortable in their standing
positions as they stand all the day from 7.00 am to 4.30 pm and
so they all need chairs. They feel that they do not have enough
breaks (4 breaks a day) and they get more tired on Mondays and
Fridays and mostly during the evenings. When a worker from
one cell was relocated to another cell, one of the remaining
workers had to cover up the work of the relocated worker. The
worker, who covered up the relocated worker’s work, always
worked on his/her previous work and his/her own work. They
think that music and sitting can improve their productivity and
can reduce their boredom. They have to work fast, remember
sequences of assembling parts and do not have to reach distant
parts while assembling. All the experienced workers (more than
2-3 years) have had injuries and the main body part affected
was the wrist. All the workers are trained for 6 months and they
give exams for each level gradually.
From the manager’s survey, it is understood that dexterity,
concentration and reaction time were the most important metrics
for human performance measurement. The managers generally
do not look for who is doing mistakes in the cell. They look at

the outgoing quality audit, scrap, first article sheet or the EOL
audits to verify the non-defective products. They rotate/relocate
workers based on the worker’s experience, certifications, ability
to demonstrate a particular operation and observations. They do
not use any software for their decision-making. The managers
arrange monthly trainings, safety trainings, offline meetings,
loan workshops and exams for certification in the facility to
improve the worker’s performance and productivity.
Simulation:
The assembly station at Johnson’s Control was simulated at
Human Performance Laboratory at New Mexico State
University. A work table resembling a workbench was used for
the assembly task and the dexterity tests. Assembly (according
to the steps followed and parts used at Johnson Controls) of
thermostats was conducted on the simulated work table. This
simulated environment will enable us in future to conduct tests
on the lab instead of at Johnson Control’s facility. The main
tasks during the simulation were - assembly task and the
dexterity tests. After every 20 real assemblies, the Purdue
pegboard and hand too dexterity tests were applied to see if
practice would improve the fine and gross dexterity.
Equipment: Purdue Pegboard for assessing fine dexterity
(Model No.32020) and Hand-Tool Dexterity Test equipment
(Model No.:32521) for assessing gross dexterity equipment
from Lafayette Instruments as shown in figure 7 were used.
Figure 7: (a) Purdue Pegboard and (b) Hand Tool Dexterity Test
Equipment
Experiment Design: The Purdue Pegboard test consisted of 4
tests - Right hand, left hand, both hands and the assembly tests.
The right hand test consisted of inserting pins from the cup into

the holes on the pegboard by using the right hand and counting
the number of pins inserted in 20 seconds and number of pins
dropped. The left hand test consisted of inserting pins from the
cup into the holes on the pegboard by using the left hand and
counting the number of pins inserted in 20 seconds and number
of pins dropped. The both hands test consisted of inserting pins
from the cup into the holes on the pegboard by using both hands
and counting the number of pin pairs inserted in 20 seconds and
number of pins dropped. The assembly test consisted of
inserting a pin from the cup into a hole with the right hand and
simultaneously picking a washer from the cup with the left
hand, then putting the washer on top of the pin on the pegboard
with the left hand and simultaneously picking a bolt from a cup
with the right hand, then placing the bolt over the washer with
the right hand and simultaneously picking another washer with
the left hand and placing it on the bolt and then counting the
number of assemblies made in 50 seconds, number of missed or
wrong sequences and number of pins dropped. The hand-tool
dexterity test consisted of unscrewing the screws on one side of
the equipment and screwing it onto the other side of the
equipment.
Data Analysis: From the Purdue pegboard test, it can be
observed that as more number of real assemblies were made, the
finger movement became efficient and faster (as more pins were
found to be inserted into the holes), the range of bend of the
fingers improved (as pick and place became easier), and lesser
mistakes were made (as less pins were being dropped). From the
hand-tool dexterity test, it can be observed that with more
number of dexterous tasks done, speed of performing that task
is increased and tools can be used comfortably.
During the simulation, the student (doing the assembly task of
the worker) faced some difficulties initially like - distraction by
background noise, pain in the legs/ankle, and then after some
time faced strain on the eyes, pain in the back and shoulder.

Later she could concentrate easily and started counting parts
frequently to reach the pre-decided goal of making 20
assemblies. Thus, she had some dexterity capability but she kept
assembling faster to reach the assembly task requirement, pre-
decided by the managers.
Main Study:
Equipment and Software: Purdue Pegboard, Hand Tool
Dexterity test equipment
Experiment Design:
Data Analysis:
RESULTS
DISCUSSION
As expected, the dexterity data from simulated and real
environment for assembly station should not be significantly
different using t - student test. Thus we are evaluating or
assessing the simulated assembly station for dexterity. We may
use the simulated assembly station for all other performance
metrics that will be identified in future.
CONCLUSION
· Test equipment give an accurate but offline measurements for
fine dexterity
· Future work on dexterity can be carried out in the laboratory
=> simulated environment is evaluated and is not significantly
different from the real environment in terms of dexterity
· Depending upon the dexterity data, the managers can easily
decide as to which operator is suitable for an assembly task

APPENDICES
A
Operator Consent Form:
Operator Survey Questionnaire
February 18, 2005 at Johnson Controls, Juarez, Mexico
This survey will take about 10 minutes. Please circle the
appropriate answer
1. What are your activities?
2. Do you do the same activities everyday?
Yes
No
3. How many years of experience do you have?
1-3
4-6
7-9
4. At what time do you start and end working - shift?

5. Do you get tired mentally (M) or physically (P)?
M
P
6. Which activities make you more tired?
7. What time of the day do you feel maximum tired?
Morning Afternoon Evening
8. On which days do you put more efforts to work?
Mon Tue Wed Thu Fri Sat
9. On which day do you feel more tired and why?
Mon Tue Wed Thu Fri Sat
10. How many breaks do you take during the day?
11. Do you feel that you have enough breaks?

Yes
No
12. How do you find the work environment? (select as many as
applicable)
Nice boring bossy relaxing stressful mention another
13. How often do you feel under stressed?
everyday or just sometimes
14. What can make your work easier?
15. When you feel tired, what can be done to get you back to
work?
16. Do you think work here is safe?
Yes
No
17. Do you get bored of your activities?

Yes
No
18. Did you get any training after you joined this company?
Yes
No
19. Does lighting or sound make you tired?
Yes
No
20. Do you have to reach far things and can you reach them
easily? Yes
No
Yes
No
21. Do you have to remember many things
Yes
No

and do you remember them easily?
Yes
No
22. Do you have to work fast and can you work fast?
Yes
No
Yes
No
23. How often do you forget the sequence of your activity?
Too often
sometimes
almost never
24. Do you get injured?
Yes

No
25. How often?
Too often
sometimes
almost never
26. Is your standing/sitting position comfortable?
Yes
No
27. Do you change your position very often during work?
Yes
No
28. Can you concentrate on your activities?
Yes
No

B
Manager Consent Form:
Cell Manager or Team Leader Survey Questionnaire
February 18, 2005 at Johnson Controls, Juarez, Mexico
This survey will take about 10 minutes. Please circle the
appropriate answer
1. What are your activities in the company?
2. Do you do same activities everyday?
Yes
No
3. How many years of experience do you have as a manager?
1-3
4-6
7-9
4. At what time do you start and end working - shift?
5. Do you get tired mentally (M) or physically (P)?
M
P

6. Which activities make you more tired?
7. At what time of the day are you most tired?
Morning Afternoon Evening
8. Do you schedule trainings for the workers and if yes, how?
Yes
No
9. How do you verify that the product is non-defective?
10. Can you find out easily who does mistakes?
Yes
No
11. How do you find who did the mistake if a product is
defective?

12. What action do you take when you see that the workers are
getting tired or bored?
13. What skills do you look for at each station? Which metric is
useful at which station?
14. Do workers get stressed mentally (M) or physically (P)?
M
P
15. Rate the following metrics according to their importance for
workers in your facility
· Memory
· Reaction time
· Concentration
· Attention
· Dexterity
· Range of motion or reachability
· Posture
16. Do you change the worker’s position when they are tired,
Yes

No
why, where and how often?
17. How do you decide as to which worker should work at
which station?
18. What techniques or strategies do you use to measure the
worker’s performance?
19. Do you use any software for the relocation of workers?
Yes
No
REFERENCES
Abdel-Malek, K., Yang, J., Yu, W., Duncan, J. (2000). Human
Performance Measures: Mathematics.
http://www.engineering.uiowa.edu/~amalek/papers/humanperfor
manceSAE.pdf
Abdel-Malek, K., Yu, W., and Duncan, J. (2005). Human
Placement for Maximum Dexterity. SAE Digital Human
Modeling and Simulation, June 14 - 16, 2005.

Beebe, D. J., Denton, D. D., Radwin, R. G., and Webster, J. G.
(Feb, 1998). A Silicon-Based Tactile Sensor for Finger-
Mounted Applications. IEEE Transactions on Biomedical
Engineering, Vol. 45, No. 2, p. 151 - 159.
Dipietro, L., Sabatini, A. M., Dario, P. (April, 2003).
Evaluation of an instrumented glove for hand-movement
acquisition. Journal of Rehabilitation Research and
Development, Vol. 40, No. 2, p. 179 - 190.
Kamieniarz, M., Stryla, W., Haglauer, P., Kamieniarz, G.
(1999). Standardized Computer Tests for Assessment of
Children Manual Dexterity. Computational Methods in Science
and Technology, Vol. 5, p. 25 - 38.
Kondraske, G. V. (1995a). An elemental resource for human-
task interface, International Journal of Technology Assessment
in Health Care, Vol. 11, No. 2, p. 153 - 173.
Kondraske, G. V. (1995b). Human performance engineering:
Section overview. In J. Bronzino (Ed.), The Biomedical
Engineering Handbook, Boca Raton: CRC Press, pp. 2143 -
2145
Kondraske, G. V. (1995c). A working model for human system-
task interfaces. In J. Bronzino (Ed.), The Biomedical
Engineering Handbook. Boca Raton: CRC Press, pp. 2147 -
2164.
Kondraske, G. V., and Khoury, G. J. (1992). Telerobotic system
performance measurement: Motivation and methods, SPIE
Cooperative Intelligent Robotics in Space III, Bellingham, WA:
SPIE, pp. 161-172.
Mital, A, Nicholson, A. S., Ayoub, M. M. (1993). A Guide to
Manual material Handling, Taylor & Francis, London,
Washington DC.

Pennathur, A., Contreras, L. R., Arcaute, K., and Dowling, W.
(2003). Manual dexterity of older Mexican American adults: a
cross-sectional pilot experimental investigation, International
Journal of Industrial Ergonomics, Vol. 32, p. 419 - 431.
Pictures of Purdue pegboard and hand tool test dexterity
equipment form Lafayette Instruments
http://www.lafayetteinstrument.com/evaldexterity.htm.
Population Leadership Program, Project of Public Health
Institute, office of Population.
Valero-Cuevas, F. J., Smaby, N., Venkadesan, M., Peterson, M.,
Wright, T. (2003). The strength-dexterity test as a measure of
dynamic pinch performance. Journal of Biomechanics, Vol. 36,
p. 265 - 270.
Welsh, M. H., and Akin, D. L. (2001). The Effects of
Extravehicular Activity Gloves on Human Hand Performance,
2001 Society of Automotive Engineers, Inc.
PAGE
11
ERGONOMIC FACTORS INVOLVED IN
OPTIMUM COMPUTER WORKSTATION DESIGN

A PRAGMATIC APPROACH
Presented by:
Harry C. Sweere
Chairman, Chief Scientist
Ergotron, Inc., and
Constant Force Technology, LLC
1181 Trapp Road
St. Paul, MN 55121
(651) 681-7600
(651) 681-7710 (Fax)
[email protected]
www.ergotron.com
www.cftproducts.com
Revised 6/14/02
mailto:[email protected]
http://www.ergotron.com/
http://www.cftproducts.com/
1
ERGONOMIC FACTORS INVOLVED IN
OPTIMUM COMPUTER WORKSTATION DESIGN
A PRAGMATIC APPROACH

Ergonomics: Application of scientific knowledge to the work
place in an effort to improve the
well being and efficiency of workers.
“The future…depends on how we develop human interfaces that
create a
match between the internal rhythms of the operator and the
computer.”
– Dr. Joel Orr, Computer Graphics Consultant –
Background
Over the past several years numerous scientific papers have
been written on the ergonomic
factors involved in computer workstation design. This paper
will not attempt to duplicate the
large base of scientific knowledge and the many ergonomic
studies already well documented.
The goal of this paper is to offer a practical guide to
interpreting published ergonomic guidelines
and the anthropometric data that can be used to create a user
friendly, ergonomically correct
computer work environment.
Many factors are involved in the design of a computer
workstation such as:
• VDT adjustability
• Keyboard placement/adjustability
• Worksurface adjustability
• Chair design/adjustability

• Foot rests
• Wrist rests
• Glare screens
• Lighting, task lighting
• Ease of adjustability
• Accessibility to components
• Human Computer Interfaces (HCI’s)
• Space savings
All of the above issues concern themselves with the reduction
or elimination of a class of
physical disorders associated with poor ergonomic design
known as Musculoskeletal Stress
Disorders (MSD’s), which result in:
• Eye, neck and back strain
• Fatigue, headache
• Wrist, hand, elbow and shoulder diseases
Carpal Tunnel Syndrome
Tenosynovitis
Tendonitis
Synovitis
Some of the primary causes of eye, neck and back strain, which
cause visual problems and
wrist, hand, elbow and shoulder diseases are:
• Improper VDT screen height and the inability to adjust the
screen height to individual
preferences
• Improper VDT viewing distance and the inability to adjust the

same
2
• Improper VDT viewing angle and the difficulty of adjusting
the viewing angle especially of
larger monitors
• Improper keyboard vertical, fore and aft and tilt positioning.
A user survey conducted several years ago (Grandjean, et al,
1983) indicated that monitor
positioning was a prime factor in assuring a computer operator’s
comfort. The results of that
survey are shown below:
Response
Survey Questions Yes No
Is a height adjustable screen useful? 97% 3%
Should the screen distance be adjustable? 97% 3%
Should the inclination (tilting) of the screen be adjustable? 92%
8%
Although not included above, the desirability of providing
keyboard placement and height
adjustability has subsequently been documented extensively
because of the high incidence of
Carpal Tunnel Syndrome and other health issues associated with
keying functions.
Because of the key role played by the monitor and keyboard,
this paper will focus on the
relevant anthropometric data dealing with various classes of

operators and the application of a
series of scientifically sound ergonomic rules to determine
suggested mounting heights for
these important components of computer workstations.
The Expanding “Workstation Environment”
History/Background
Recent advances in networking technology have allowed users
to bring computers to the point
of use in many applications and locations heretofore not
considered a computer workstation.
Accordingly, the desktop can no longer be considered the only
focus for the corporate
Ergonomist or the ergonomic workstation designer. Computers
on the factory floor, in
warehouses and in hospital rooms are some examples of non-
office computer workstations that
have become quite common over the last several years. Many
of these new applications
require a different man-machine interface from the traditional
seated desk arrangement
considered in much of the published literature. New work
positions such as standing, and sit-
stand have been added, along with a variety of computer
component mounting options including
wall mount, ceiling mount, floor mount and mobile applications.
In most of these applications,
component adjustability is even more important than in the
office environment. In many cases
several people may operate the same equipment on the same or
different work shifts as
opposed to the desktop environment which may be fairly static
once the equipment is set up for

a particular operator.
In each case good ergonomic design principles must be applied
to give the operator or a range
of operators the optimum man-machine interface and the
adjustability required to prevent
discomfort and prevent workplace injuries. In many cases the
specialized video display
mounting technology developed for these work environments
can be applied to the desktop to
provide ergonomic adjustability and space saving benefits for
this environment as well.
3
New Display Technology
The advent of new flat screen VDT technology offers new
opportunities to provide improved
ergonomics in office and specialty computer workstation
environments. The smaller size and
lighter weight of these devices has fostered the development of
new mounting solutions that can
more easily address the age-old problems of screen height
adjustability, screen distance and
screen tiltability. Now VDT and other manufacturers can
provide low-cost, vertically adjustable
desk stands and easy monitor tilt capability, to address the
average range of operators in either
sitting or standing applications. In addition, specialty devices
are available to provide
reasonable cost solutions to address special ergonomic issues
such as providing screen

distance adjustability, comfortable viewing for bifocal users,
sit-stand applications and providing
vertical adjustability for the 5% female – 95% male range of
operators (see chart on Page 4).
Ergonomic Ground Rules
The recommended mounting heights, and range of adjustability
required to provide comfortable
use by a range of operators, shown in this paper are based upon
the following ergonomic
ground rules gleaned from the available scientific literature and
published standards on this
subject:
Screen Height
The recommended screen height for VDT monitors (except in
special circumstances such as
bifocal use) is that the top of the monitor screen should be set at
or slightly below
(approximately 1”-2”) the eye height of the user when the user
is sitting or standing in a
comfortable, relaxed position. Whenever possible the screen
height should be variable to
accommodate personal preferences throughout the day.
Screen Tilt
Ideally an upward tilt with the bottom of the screen tilted
toward the operator provides optimum
viewing because it provides a consistent focal length when
scanning from the top of the screen
to the bottom. A tilt range of 12° to 20° is ideal depending
upon the size of the monitor. Note:
When upward tilt is used, special care must be taken to

minimize screen glare.
Screen Distance from Operator
Normally the monitor screen should be placed as far away as
possible from the operator,
consistent with the ability to read the information presented on
the screen. (The normal focal
length for most people exceeds 30” or greater, however, from a
practical standpoint a
recommended viewing distance from 18” to 28” is mentioned by
several ergonomic standards.)
A good rule of thumb for most installations is that the monitor
screen should be placed at arms
length, with the provision to move the monitor back and forth to
suit individual needs being the
ideal.
Keyboard Height/Positioning
Keyboards should be placed at a height that allows the operator
to operate the keyboard with
the forearms level and hands sloping slightly downward. A
negatively tilting keyboard, allowing
the operator to “keep the wrinkles out of the top of the wrists”
is ideal. Fore and aft positioning
of the keyboard should be consistent with allowing the hands to
move easily over the keyboard
4
with forearms level and elbows at the sides, maintaining a 90° -
110° angle between upper and
lower arms.

Screen/Keyboard Height Variance
Anthropometric data for the average range of male to female
operators indicates that the top of
the monitor screen to centerline of the keyboard placement
should range from 20” to 22” with
21” being a good set-up for most applications.
Visual Representation of Anthropometric Height Range
Anthropometric Data: Relating to Screen and Keyboard Height
Positioning
Anthropometric Data* (in inches) for Average Range of
Operators

* Based on an 1988 Anthropometric Survey of US Army
Personnel
Ergonomic Design Factors Gleaned from the Above
Anthropometric Table
Note 1
• Eye height variance for the average range of male to female
operators
• Design criteria used for design of a 6” vertically adjustable
VDT mounting apparatus
Average Male
Average Person
Average Female
5% Female
95% Male
Average Range
of Operators
- 50% -

Stand Sit Variance Stand Sit Variance Stand Sit
Av. Female 59.4 44.0 15.4 38.8 23.0 15.8 20.6 21.0
Av. Person 61.7 46.1 15.6 40.4 24.9 15.5 21.3 21.2
Av. Male 64.4 48.5 15.9 42.5 27.0 15.5 21.9 21.5
Variance F/M 5.0 4.5 3.7 4.0
Height VarianceEye Height Elbow Height
Eye/Elbow
1 2
5 4
3
5
5
Note 2
• Elbow height variance for the average range of male to female
operators
• Design criteria used for design of most adjustable keyboard
mounts
Note 3
• Eye to elbow height variance for the average range of male to

female operators
• Optimum top of screen to centerline (C/L) of keyboard
relationship for most computer
workstations – average 21”
Note 4
• Eye height to eye height variance for the average standing to
sitting person
• Ideal mounting height for fixed height workstation
components that must interface to a range
of operators
• Design height adjustment range for a minimum height
adjustable screen/ keyboard sit-stand
workstation
Note 5
• Eye and elbow height variances for the average standing male
to the average sitting female
• Design height adjustment range for a sit-stand workstation
designed to accommodate the
average range of male and female operators – 20”
Anthropometric Data* (in inches)
for 5% Female and 95% Male Range of Operators

* Based on an 1988 Anthropometric Survey of US Army
Personnel
Ergonomic Design Factors Gleaned from the Above
Anthropometric Table
Note 1
• Eye height variance for the 5% female to 95% male operator
• Ideal eye height adjustment range for a broad range of
applications
• Design criteria used for design of 12” or greater vertically
adjustable VDT mounting
apparatus
Stand Sit Variance Stand Sit Variance Stand Sit
5% Female 55.7 40.8 14.9 36.5 20.8 15.7 19.2 20.0
95% Male 68.6 52.1 16.5 45.4 29.5 15.9 23.2 22.6
Variance 12.9 11.3 8.9 8.7
Eye Height Elbow Height
Eye/Elbow

Height Variance
1 2
4
3
6
Note 2
• Elbow height variance for the 5% female to 95% male operator
Note 3
• Eye/elbow height variance for the 5% female to 95% male
operator. Range 19.2” to 23.6”,
average = 21.2”
Note 4
• Eye height variance for the 5% sitting female to the 95%
standing male
• This data indicates that to design a sit-stand workstation to
address the 5% to 95% range of
female to male operators would require 27.8” of vertical
adjustability
Design of an Ergonomically Correct Sitting Computer
Workstation

The following chart illustrates a computer workstation
environment with monitor and keyboard
positioning designed according to the above ergonomic ground
rules and anthropometric data.
Notes:
1. Viewing distance – operator’s eye to screen – 24”
2. Operator eye height – average male 48.5”, average female
44” or as determined by

designer
3. Top of monitor screen – 1” below eye height
7
4. First line of text on monitor screen – 5° below horizontal line
of sight
5. Ideal viewing cone – 5° to 35° below horizontal line of sight
6. Center line of text on screen – 15° to 20° below horizontal
line of sight
7. Bottom of monitor screen – dependent upon size of monitor.
Screen heights for
standard 4 x 3 format monitors are 15” D = 9”; 17” D = 10”;
18” D = 11”; 20” D = 12”
8. Tilt angle of monitor – 12° to 18° dependent upon size of
monitor, larger monitors require
more tilt to provide equal focal length
9. Worksurface height (29”) – shown in relation to bottom of
monitor screen for average
female operator
10. Worksurface height (29”) – shown in relation to bottom of
monitor screen for average
male operator
11. Anthropometric eye height variance, male to female, sitting
application – 4.5”
12. Anthropometric eye height to C/L of keyboard variance –
21”
Note: The average eye heights shown are useful when
designing a height adjustable

workstation or desk stand for the average range of operators.
However, the dimensional
relationships can be utilized to design a static workstation for
any height operator.
Examples of Ergonomically Adjustable Workstations whose
Design is Based on the
Above Anthropometric Data

Factory workstation designed to address the 5% female
Tilt +15°/-15°
(3
(5
55”
(1397mm)
Screen height adjustment
12”
05mm)
21”
33mm)
r
Screen/keyboard height
variance
Floo
to 95% male population.
Hospital workstation designed to provide a minimum sit-stand
workstation for the average range
of personnel.

Hospital workstation for standing application, wh
correct screen to keyboard centerline relationsh
above and 2” below the average adjustment ran
broader range of users.
Height adjustment
range
Screen/keyboard
variance
8
ich provides 9” of vertical adjustability and
ip. Note: 9” adjustment range is allocated 2”
ge to provide viewing and keying comfort for a
9
Flat Panel Monitor Technology Offers Potential for Improved
Ergonomic Benefits
Flat Panel Arm Office Systems Furniture Automation

Flat Panel Monitor shown at 41” screen height, which is the eye
height of the 5% female
operator in a sitting position.
height of the average operator
in a sitting position.

height of the 95% male
operator in a sitting position.
10
Flat Panel Arm Office Systems Furniture Automation
Top view. Flat Panel Monitor installation featuring 5% - 95%
vertical adjustability, fore and aft
adjustability, monitor tilt and space area for reference materials.

Flat Panel Monitor shown in position optimized for bifocal
users.
Low-cost, Full-range Ergonomically Adjustable Desk Stands
Newly developed, low-cost linear counterbalance technology
will now allow flat panel monitor
and other manufacturers to provide full ergonomic adjustability.
Design of an Ergonomically Correct Desk Stand to meet the
average range of male and
female users

Dual Stacked Fla
Dual stacked Flat
In general, the mo
ability to create a t
because it is more
t
Eye Height
St
Av. Female 5
Av. Male 6
Variance F/M
Landscape Portrai
11
t Panel Monitor Configurations
Panel Monitors do not lend themselves to standard ergonomic
height rules.
nitors should be set as close to the tabletop as possible,

consistent with the
op to bottom parabolic to improve sight lines. Lower is better
than higher
comfortable for the operator to look down than look up for
sustained periods.
in Inches
and Sit
9.4 44.0
4.4 48.5
5.0 4.5
12
Conclusion - Summary
Improving the Human Interface with Computers
Ergonomic studies done years ago indicate that screen
positioning and keyboard adjustability
are some of the most important factors in providing a
comfortable work environment and
preventing a broad range of MSD’s associated with computer
use. Disorders such as eye, neck
and back strain, fatigue, headaches, and wrist, hand, elbow and
shoulder diseases such as
Carpal Tunnel Syndrome can all be dramatically improved
through use of good ergonomic
design.
The foregoing paper is based upon sound ergonomic ground
rules and scientific anthropometric
data, which can be used by computer workstation designers to
help provide an optimum human

interface for their computers. However, the science of
ergonomics is constantly evolving,
therefore all values and recommendations in this paper are
provided as guidelines only.
Workstation designers are urged to consult with a certified
Ergonomist who is familiar with the
applicable anthropometric data and computer workstation
ergonomic standards for corrobora-
tion of the recommendations made for each application.
13
Chairman, Chief ScientistRevised
6/14/02BackgroundResponseIs a height adjustable screen
useful? 97% 3%The Expanding “Workstation
Environment”History/BackgroundNew Display
TechnologyErgonomic Ground RulesScreen HeightKeyboard
Height/PositioningScreen/Keyboard Height VarianceVisual
Representation of Anthropometric Height RangeAnthropometric
Data: Relating to Screen and Keyboard Height
PositioningErgonomic Design Factors Gleaned from the Above
Anthropometric TableNote 1Note 2Design criteria used for
design of most adjustable keyboard mountsNote 3Note 4Note
5Ergonomic Design Factors Gleaned from the Above
Anthropometric TableNote 1Eye height variance for the 5%
female to 95% male operatorNote 2Note 3Note 4Flat Panel
Monitor Technology Offers Potential for Improved Ergonomic
BenefitsFlat Panel Arm Office Systems Furniture
AutomationFlat Panel Arm Office Systems Furniture
AutomationLow-cost, Full-range Ergonomically Adjustable
Desk StandsDual Stacked Flat Panel Monitor
ConfigurationsImproving the Human Interface with Computers

1
Computer Workstation Ergonomics Checklist
Chair Response Suggestions for “No” Responses
1. Familiar with all chair adjustments Yes No
Try all adjustments to increase comfort. Locate user manual or
check
the web site of the chair manufacturer
2. Height is appropriate - feet are flat
on the floor and thighs are somewhat
parallel to the ground
Yes No
Raise or lower the chair so that hip, knees, thighs and feet are
properly positioned
If feet cannot be placed flat on the floor a footrest may be
required
3. The low back is supported by the
back of the chair Yes No

Check to see if the backrest can be raised up or down so that the
low
back has sufficient support.
4. Seat depth is adequate such that
there is a little space between the
calf and the seat
Yes No Check to see if the seat will slide in/out or the back
will move in/out
5. Armrests can be adjusted so they are
not in the way when keying Yes No
Check to see if the arms can be lowered or moved out of the
way
while keying
6. Casters are appropriate for the
flooring (ie rubber casters for vinyl,
concrete/hard wood floors)
Yes No Contact the chair vendor to replace the casters

2
Keyboard/Mouse Response Suggestions for “No” Responses
1. Keyboard and mouse height are
about the same height as the elbows
Yes No
Adjust the keyboard and mouse if on a tray to match elbow
height
or
Adjust the chair so elbow height matches keyboard and mouse
height
(a footrest may be required to support the feet with chair raised)
2. Keyboard and mouse are positioned
directly in front of the body
Yes No
Consider moving the computer or changing workstation
configuration so that keyboard, monitor, and mouse are directly
in
front of the body

3. Mouse is as close to the keyboard as
possible Yes No
Consider using a keyboard tray with room for the mouse or use
a
mouse bridge
4. Wrist rest is used only for resting
palms of hands and is not used while
keying
Yes No
Remove the wrist rest and move keyboard to the edge of the
work
surface
3
Monitor: Response Suggestions for “No” Responses
1. Top of screen is about the same
height as the eyes

Yes No
If the monitor adjusts
- raise or lower it
If the monitor does not adjust
- raise by adding phone books, paper reams, or monitor
risers
- lower by removing items beneath the monitor
* Note if wearing bifocals, the monitor should instead be as low
as
possible
2. Screen is about an arm’s length
away Yes No
Move monitor closer
or
Push monitor further back
3. Monitor is positioned directly in
front of the individual
Yes No
Position monitor directly in front
Consider the use of a flat screen if space constraints do not

allow
proper monitor placement
4. Monitor is positioned so that it does
not face or back up to a window Yes No
Move monitor so that it is angled 90 degrees from windows
5. The screen is clean Yes No
Periodically use a screen cleaner
4
Miscellaneous Response Suggestions for “No” Responses
1. Frequently used items (phone,
calculator, reference books) are
within easy reach
Yes No
Move items so they are closer, request longer cords if this limits
movement

2. Lighting is sufficient (low lighting in
computer areas, brighter light for
documents)
Yes No
Reduce overhead lighting and supplement with small task lights
NOTE: You should follow up on all “No” responses. Please
contact the Duke Ergonomics Division for assistance if
necessary at
[email protected], or 668-ERGO. *Adapted from the NREL
ES and H Ergonomic Workstation Evaluation Checklist
Comments:
mailto:[email protected]

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