was identified by the start and stop points of a motion sensor
mounted on the drill boom. To identify these points, a graph of the
acceleration of this sensor was overlaid on the graph of the boom
movement. The points of maximum acceleration mark the start and
stop points of the boom (Figure 6.)

Figure 6. Determination of task starting points.

Three trials of motion data collected for each test subject were
analyzed using ANOVA. For discrete tasks 2, 3, 5 and 6, the
position of the moving boom was used as the independent variable
and the change in a scalar vector from the boom sensor to the body
point sensor being studied was used as the dependent variable,
Standard deviations of bolter boom arm movement were
determined and the maximum standard deviation was selected for
range of variability for the virtual human movement. The boom
arm has no movement in task 1 and 4; therefore, both were not
critical for the object of the study. Data was classified by
anthropometrical size and an example of the result is shown in
Table 4,

The results of motion variance analysis produced a scattered range
of variation, which at first glance does not produce a consistent
pattern, When the data is viewed as a function of scale, the range
of variation was small; the variation in movement was reasonable
for a repetitive task in a confined environment. The variation in
motion also tended to increase as seam height increased providing
more workspace. The difference in movement between tests
ranged from 2 cm to 30 cm. Model’s random seed numbers are
calculated from the human motion envelope data, which is close to
the originally assumed variance of motion used in the random
number generation in the model.

Human-machine initial start posture

Using the human motion envelops data, an average starting
position for the subject’s knees and back motion sensor was
determined and a standard deviation for these points determined.
The results were then categorized by the subject’s height position
along the anthropometrical scale and averages obtained for 10
percentile increments. Typical results are shown in Table 5. This
information provides the human model with a realistic starting
position for the simulated bolting sequence and a valid range of
variation in initial position for generating randomness in multiple
simulation runs.

MODEL OUTPUT and SIMULATION RESULTS

The roof bolter model can generate 864 different scenarios that
mimic motions of the operator and machine during the roof bolting
tasks. The scenarios are defined by varying six factors: four boom
arm speeds [5], two machine control configurations, three operator
heights, four risk behaviors, three postures and three mine seam
heights. After the model generates motions, it records collisions
that happen between the machine and its operator during a
simulation test run. Distances between the operator’s body parts
and one or more of the six reference points on the boom arm are
measured and recorded. The simulation’s run time when the
moving boom arm enters in the operator’s viewing area is
recorded. All information is collected every tenth of a second
throughout a simulation test run and a output function sends
results to a computer file. A typical test series consists of 600
simulation test runs.

An important phase of data analysis is to create a database of each
test series. This requires several steps. First, count the number of
“raw” collisions that occur in each test run. Second, determine the
number of “avoid” collisions in each test run that the operator
could have avoided by using a predetermined human response
time, taking 250 msec or 400 msec to get out of the way of a
moving boom arm once seen [7]. Third, calculate the collision
totals for evaluation by taking the difference between “raw” and
“avoid,” resulting in “hit” collisions represented as four scatter
plots (see figures 7a, 7b). A scatter plot gives strong support for
using regression analysis. Regression analysis (using Mircosoft
Excel) shows the relationships between independent variables and
one dependent variable, such as taking into account the values of
the six factors in the model and predicting collision trends. With
one independent variable (speed), the regression analysis plots a
line of “best fit” through a scatter plot of independent-dependent
(speed-collisions) value pairs.

Collisions versus speed, operator’s size, and risk behaviors
demonstrate the versatility found in the data obtained from the
model. Response time significantly affects the number of
collisions experienced by the virtual subject (see figures 8a, 8b).
Also, preliminary simulation data indicates that lower seam heights
have more mishaps and are more sensitive to the two response
times. Factors such as age, strength or other constraints relating to
a person’s reaction time could be used to generate a tailored
tesponse time. Because the model’s verification and validation
stages are in progress, this paper reflects only preliminary
simulation data,


Table 3. Operators’ Hands and Head Response Times.

Table 5. Start Position on Both Knees for a 50-60" Percentile

Max. Operator in a 114,3-cm seam height.
Subject #,| [7°84 JEtapsea| MO ACCelsratiOm| average Speed, emis| {Measurement Location Mean (om. | Standard
peed, [El cms ndar
Position [TM==E| SS Distance Back 53.08 Pete 7.06
- - stance x I
Head] ro) Right Irena] eh | Right Distance Left Knee 48.06 17.22
and| Hand Hand | Hand c ;
Sere Distance Right Knee 60.77 14.22
Both | 39-18 | 0.667 [392.9] 973 | 120.8]26.9]37.61| 4.25 BACK X 89.94 2.24
BACK Y 60.14 9.18
Left | 23.19 | 0.411 [332.7] 493 | 626.4] 16.4] 21.02] 22.7 BACK Z 240.54 2.28
“Angle Back X 94.47 2.87
Right | 32.76 | 0.667 [394.3] 679 | 72.98 |24.4[ 16.84] 2.53 “Angle Back Y 1777 6.04
Subject ‘Angle Back Z 97.72 3.45
Both. | 60.67 | 0.622 |749.1]2978| 272.3]33.3]53.47| 13 Left Knee X “48.20 33
Left Knee Y 26.43 7.25
Len | 97.90 | 0.733 |1438]2029] 533.8 | 49.6 | 69.82] 17.2 Left Knee Z 75.63 61
Right [101.75] 0.944 |1796|1678] 1609 | 54.1] 31.36] 240 Ane ma Brees 2388 38
Angel Left Knee Z =100.58 B31
Table 4, Standard Deviation of Motion for $0th-60" Percentile Right Knee X a2 283
Operator in a 114.3-cm seam height. Right Knee Y 25.64 135
Right Knee Z, 247.56 3.53,
Operator] Boling Cycle [StdDev ]SidDev_[StdDev “Angel Left Knee X 86.84 3.84
pperator| “Task HEAD | LEFT RIGHT ‘Angel Left Knee Y 28.27 2.62
(em) _| HAND (cm) | HAND (cm) Angel Left Knee Z =104.78 3.58
TinsertDril_| 5.57 1227 13.44
2 Drill Roof | 3.68 9.20 2.59
Both [3 Lower Boom | 2.93 16.02 2.87
Knees [4Insert Bolt | 4.49 13.54 21.38
SBolt Roof _| 2.09. 3.12 3.29
Lower Boom | 2.56 8.43 5.59
Linsert Drill__| 6.03 12.05, 17.04
2 Drill Roof _| 3.48 13.91 12.64
Left [3 Lower Boom | 3.22 8.45 13.52
“Knee [4 Insert Bolt | 5.67, 12.74 23.93,
SBolt Roof | 3.83 15.51 1116
6 Lower Boom | 4.23 3.98 10.53,
Linsert Drill | 5.40 6.49 6.18
2 Drill Roof _| 4.09 715 28.06
Right [3 Lower Boom | 6.11 18.52 14.84
Knee [4insert Bolt | 8.23 11.28 16.12
SBolt Roof | 3.22 6.50 3.77
6 Lower Boom | 4.71 6.44 3.27


The uncertainty or variability inherent in the drilling and bolting
tasks were incorporated into the model to effectively determine the
likelihood of an operator being injured. To model the random
motion, individual paths differed slightly even though the basic
motions look very similar. Thus, for a machine and operator, the
operator's various risk behaviors, motions for each risk behavior,
and motion paths associated with each motion behavior, and
moving machine appendages have some degree of randomness.
These random motions give the model a realistic representation of
the operator's motions and behaviors found in actual underground
goal mine roof bolting practice. A model that includes any random
aspects must involve sampling, or generating random variants.
The phrase “generating a random variant" means to observe or
realize a random variable from some desired arrangement of values
of variables showing their observed or theoretical frequency of
occurrence. To determine the range of these. differences,
laboratory motion tests were conducted using experienced roof
bolter operators.

VERIFYING MODEL PARAMETERS

Input parameters used to generate random motions in the model
were validated. Experiments on a full scale working mock up of a
roof bolter boom arm were conducted using human subjects and
Ascensions Flock of Birds motion tracking system to verify
‘operators response times and human-motion data relative to the
bolter’s boom arm. The tracking system position accuracy is 1.52-
cm (0,6-in) and angular accuracy is 0.2 degrees. Separate vision
tests were conducted using human subjects in a laboratory setup
with lighting conditions found in underground coal mines. A
randomized block experimental design was used. Dependent
measures in the experiments were analyzed using an Analysis of
Variance (ANOVA), using a significance level of 95%, to
determine whether significant differences existed between the
experimental conditions. If the ANOVA indicated that a
significant difference existed, the Neuman-Keuls multiple range
test were used to identify those conditions where significant
differences existed.

Field of vision in reduced lighting

The results of analysis were averaged for the subjects and a vision
area [4] for the unique lighting conditions of underground coal
mining environments developed, which accounted for the use of a
miner’s cap lamp and the reduction of viewing area by the use of a
standard hard hat. The results of the tests in 0.06fL lighting with a
hard hat were the most significant in terms of input to the
simulation model. Typical results are shown in Table 2. Figure 5
shows the vision area when wearing the hard hat for normal
lighting (214), reduced lighting (0.6fL) and in the original roof-
bolter simulation. The most significant reduction in a subject's
vision cone appeared to be a result of the reduction of the viewing
area caused by the hard hat. The rods of the eye, which become
more active in low light and allow night vision, were also the most
sensitive to movement in the cone of vision. The response of the
eye rods was only slightly diminished.

_ crane ligne
act ALP6EL. with cap Lamp
—2— simulation

Figure 5. Vision cone viewing areas in degrees.
‘Human response in roof bolting postures

‘The motion tracking system was used to collect human response
data in roof bolting postures. Human response time is categorized
by three discrete events: (1) the recognition of the initialization
signal; (2) the cognitive interpretation of the signal; and (3) the
actual reaction. Since events 1 and 2 are well documented, our
main concern was the response in the confined and limiting mine
environment, which had not been previously studied. The data for
the head and hands were considered the most significant for
reaction characterization, because of the need of the model and
Klishis et al [1,2] revealed them as most likely involved in a
mishap. Table 3 gives two examples of response times calculated
for each subjects’ hand and head motions. The range of variation
is what one might expect from human motion, maximum speed and
acceleration increases as the working space increases. When the
data is viewed as a function of scale, the variations in reaction
parameters were reasonable to the findings of Etherton [7]. This
range was averaged by anthropometrical size [8] and used to
analyze data from the model. The reaction time of operators is
significant when determining if an operator will be able to avoid a
moving object posing a hazard.

Human motion enyelops

In order to analyze input parameters for the virtual human model,
the data from the motion capturing system were divided into six
separate tasks: (1) loading the drill steel into the bolter arm; (2)
drilling the roof; (3) lowering the bolter arm; (4) loading the bolt
into the bolter arm; (5) bolting the roof; and (6) lowering the bolter
arm. The discrete points in the data where these events occurred


50 =
ieee
a
| . ,
30) = |
'
0  e # x
0 4
0 _ 1
WT 24 408 55.6
SPEED, cm/sec

seam height 1143
2 equals one unique scenaria

Figure 7a. Collision totals of scenarios vs boom arm speed in a
114.3 cm seam.

4B -———
3 *
in
a
5) bl x x
10) ie x
6
77 24 406 558
, cree
seam height 152.4
°° equals one unique scenario

Figure 7b. Collision totals of scenarios vs boom arm speed in a
152.4 cm seam.

<<< COLLISIONS ner

77 Ba 8 88
‘SPEED. cm sec
sewn height 114.3.em

Tesporae hme — 250 ms = 400 my

Figure 8a. Collisions vs boom arm speed and operator response
time ina 114.3 cm seam.

won height 1524 cre
vesponee time = = 250m 40 ms

Figure 8b. Collisions vs boom arm speed and operator response
time in a 152.4 cm seam.

CONCLUSIONS

Ergonomist who provided technical support for this work were
overwhelmed with the infinite possibilities of simulation scenarios,
because there were no limitations placed on the virtual human
operator. Simulations also provided an interesting approach to
data gathering in that logistics~ mine sites and costs associated
with experiments~became insignificant. Preliminary results
showed evidence that the approach discussed in this paper is useful
to study complex and instantaneous nature of mishaps between
operator and machine. Actual practices, i.e., operator risk
behaviors controlling roof bolters in underground mines were
included in the model. Random motions of hands, arms, legs, and
head make the model’s human behavior realistic. Researchers
developed random seed numbers for the model using data from
experiments on human subjects working a roof bolter mock up.

Verification data analysis showed that following results: (1) the
most significant reduction in a subject’s field of vision appeared to
bea result of the reduction of the viewing area caused by the hard
hat; (2) the model needed the data from the head and hands;
therefore considered the most significant for reaction
characterization; (3) the results of motion variance analysis
produced a scattered range of variation that when viewed as a
function of scale, and (4) starting position information provided a
realistic and valid range for the human model to initiate simulated
bolting sequences,

The following general recommendations can be made upon the
current outcome of this work. The model is only as good as the
system it defines; basic parameters were validated using real
subjects, Second, analysts must be discerning with the model and
not read more from the databases than what the model was
designed to deliver. Finally, the modified model will still need to
be validated using field and lab studies once the correct random-
motion seed numbers have been incorporated.

REFERENCES

{1] Klishis, M. J., Althous, R. C., Layne, L. A., Lies, G.M. 1993.
A Manual for Improving Safety in Roof Bolting, Mining Extension
Service, West Virginia University, Morgantown, WV. August. pp.
143,


Random Motion Capture Model for Studying Events Between a Machine and its Operator

: Dean H. Ambrose
National Institute for Occupational Safety and Health (NIOSH)
Pittsburgh Research Laboratory (PRL), Pittsburgh, PA 15236

DAmbrose@cdc.gov

Keywords: random human-motion, anthropometrics model,
machine-operator hazards, mine safety

Abstract

This paper presents a technique for representing and analyzing
random motions and hazardous events in a computer simulated
three-dimensional workplace, providing machine designers and
safety analysts with a new technique to evaluate ways to reduce
operator-machine interaction hazards. Technical data in this paper
is based upon a project striving to reduce workers’ risks from
being hit by underground mining machinery in a confined space.
By simulating motions of an operator's random behavior and a
machine’s appendage, researchers can accurately identify hazards,
and use that information to form safe designs for mining
equipment appendage velocity. Validating the model provided
improvements in the operator’s optimal viewing area, work task-
starting positions, and operator's motions for a more accurate
random behavior. Preliminary simulation results provided (1) an
interesting approach to research data gathering in that there was no
need for live subjects and test sites and costs associated with
experiments become insignificant and (2) that the model was
versatile by showing it was capable of accurately mimicking the
range collision forces versus speed, operators’ size, and risk
behaviors found in actual industrial situations and showed (1) that
response time significantly affects the number of collisions
experienced by the virtual subject and (2) that analysts must be
discerning with the model and not read more from the databases
than what the simulation model was designed to deliver.

INTRODUCTION

Several injuries to operators of underground coal mining
equipment have led an investigation of safe velocities of a roof
bolter boom arm at the National Institute for Occupational Safety
and Health (NIOSH), Pittsburgh Research Laboratory (PRL).
Researchers considered studying actual mishaps but empirical data
cannot be collected from the incidents. They also considered
laboratory experimentation but the complexity and danger made
experimentation impractical. Therefore, a computer-based, three-
dimensional solid model simulation approach is being used as the
primary means to gather data on mishaps. Simulations used roof
bolter machine and biomechanical human models that ran on
Unigraphics Solutions-Engineering Animation Inc.’s JACK
simulation software. In the computer model, mishap means two
or more object properties interacting. Consequently, hazardous
conditions were analyzed in virtual environments using collision
detection.

The model requires input data that closely matches an actual roof
bolter machine operating characteristics such as dimensions and
speeds as well as data that accurately reflects human physical
characteristics (see figure 1.) Researchers obtained this data using
a roof bolter machine mock up and human subjects at PRL. The
subjects were asked to perform prescribed motions with the mock
up that simulated actual practice. Actual practice was determined
through training videos, in-mine observations and videos, and
working with bolter manufacturer and experts.

Figure 1. Roof Bolting Machine

The uncertainty or randomness inherent in the bolting task can be
compared to someone drinking a can of beverage. The occurrence
of lifting the can to one’s mouth and placing it back onto the table
top is considered a motion-path, and one could easily visualize the
path of that motion. To model a random motion, the sequence of
someone drinking from a can of beverage would reoccur until the
can is empty, and each motion-path would differ slightly even
though the motions look alike. So the model would incorporate
the randomness of the motion and path variance by changing the
values that define that motion. Thus, for a machine and operator,
the operator’s various risk behaviors, motions of each risk
behavior, and motion-paths associated with each motion behavior
and moving machine appendages have some degree of
randomness. These random motion-paths give the model a
realistic representation of the operator’s motions and behaviors
found during underground mine roof bolting.

Klishis et al study on workers job performance, machinery and
work environment identified miners’ risk and hazard exposures
while bolting [1, 2]. More than two-dozen bolting related
problems (including specific human behaviors) were recognized as
potential situations that could lead to injury or exposing workers to
injury: Approaches to avoid these situations were suggested and
applied at mining operations to evaluate specific problems in roof
bolting tasks. Turin conducted a human factors analysis of hazards
related to the movement of the drill head boom of a roof-bolting
machine [3]. Seven recommendations to increase the safety of roof
bolting operations were developed.


{2] Klishis, M. J., et al, 1993, Coal Mine Injury Analysis: A
Model for Reduction Through Training, Mining Extension
Service, West Virginia University, Morgantown, WV. August. pp.
220.

[3] Turin, F. C. 1995. Human Factors Analysis of Roof Bolting
Hazards in Underground Coal Mines. Bureau of Mines RI 9568,
Pittsburgh Research Laboratory, Pittsburgh, PA, pp. 25,

[4] Humantech, Inc. staff. 1996. Ergonomic Design Guidelines
for Engineers Manual. Humantech, Inc.,Ann Arbor, MI. pp. 22.

[5] MSHA; A roof-bolter-machine committee convened by
MSHA, (with members from the WV Board of Coal Mine Health
and Safety, NIOSH, and roof bolter manufactures), studied 613
accidents that occurred during drilling and roof-bolt installation,
August 1994 (not published).

[6] Azuola F., Badler N. I, Ho P., Huh S., Kokkevis E., Ting B.,
1996. Jack Validation Study: Final Report. University of
Pennsylvania, Center for Human Modeling and Simulation,
Philadelphia, PA. Grant No. DAMD 17-94-J-4486-P50002, pp.
138,

[7] Etherton J. 1987. Model of Human Reaction Time to,
Dangerous Robot Arm Movements, NIOSH. Proceedings of the
Human Factors-31" Annual meeting, p. 191-195.

[8] Staff of Anthropology Research Project 1978. Anthropometric
Source Book Volume II: Handbook of Anthropometric Data.
NASA Reference Publication 1024, pp. 421.

ACKNOWLEDGMENTS

Bill Rossi and Ray Helinski provided technical support in
operating the motion tracking and data collection system. John
Bartels, Mary Ellen Nelson, Al Cook, and Mary Ann Rossi
assisted in the laboratory human subject tests. John Bartels,
Richard Wang, Augie Kwitowski and Mike Pazuchanics helped
with post data processing. Joe DuCarme, Mary Ellen Nelson, Al
Cook, George Fischer, Albert Brautigam provided technical
expertise in the design and fabrication of the roof bolter mock up
and platform, and associated electrical and hydraulic control
systems, Ergonomist for this work were Kim Cornelius and Sean
Gallagher. All of the above associates are employees of the
National Institute for Occupational Safety and Health (NIOSH) at
Pittsburgh Research Laboratory, Pittsburgh, PA.

DISCLAIMER

Any reference to specific products mentioned in this paper does
not imply endorsement by the National Institute for Occupational
Safety and Health (NIOSH.).

Table 1. Variables Considered in the Model.

Independent variables

Anthropometrics scale: 5", 50” and 95" male percentile subjects

Work postures: one knee, two knees, standing, and start position
from the boom arm

Operator’s response time

Mine seam height: 114.3cm, 152.4 cm, and 182.8 cm

Operator’s random body motion

Operator’s optimal viewing area

Risk behaviors associated with drilling and bolt installation

Machine control panel configurations

Boom speeds: 17.78 cm/s, 25.40 cm/s, 40.64 cm/s & 55.88 cm/s

Dependent variables

Collisions between the operator and selected machine appendages

Distances between operator's body parts to reference points on the
machine

Time-event-signal when the operator sees the moving boom arm

Table 2. Vision Cone in Reduced Lighting.

BOTH EYES Subject 60.96-cm from focus point, becomes
aware of ball at angle (deg)
Angular path of 21fL Normal | . O6fL 06fL
ball , degree light : wiamp
O and 360 70.02 63,55 65.43
45 61.04 63.07 58.06
315 65.94 65.01 67.17
90 54.28 53.13 34.51
270 61.39 57.03 57.38
180 60.45 61.93 63.55
135 60.83 59.81 64.36
225 65.94 65.01 67.17
LEFT EYE ‘Subject 60.96-cm from focus point, becomes
aware of ball at angle (deg)
Angular path of || 21fL Normal O6fL 06fL
ball , degree light : wilamp
and 360 53.97 57.03 56.31
45 53.56 45.00 36.87
315 39.45 52.25 52.70
90 43,47 35.71 28.44
270 61.39 56.67 56.31
180 67.86 62.70 64.25
135, 54.38 49.90 52.25
225 65.64 62.95 62.45
RIGHT EYE Subject 60.96-cm ftom focus point, becomes
aware of ball at angle (deg)
Angular path of 21fL Normal O6fL 06fL
ball , degree light : wlamp
Oand 360 66.80 64.25 64.47
45 59.19 60.26 61.25
315 67.43 64.36 66.04
90 42.51 48.63 35.31
270 59.66 52.91 59.04
180 49.40 48.37 51,34
135, 51.34 54.78 42.51
225 65.74 62.95 62.45



BACKGROUND

Roof bolting is one of the most basic and the most dangerous
elements of underground coal mining operations. It is the
principle method of roof support in mines, which is essential to
ventilation and safety. After miner crews remove a section of the
coal seam, roof bolting machine operators install bolts (steel rods)
to secure areas of unsupported roof from caving in. A bolter
crew’s typical work sequence includes: general preparation and
setup, drilling a hole, and installing a bolt. General preparation is
a miscellaneous category that includes setting up temporary roof
supports, scaling, handling ventilation material, handling supplies,
emptying dust box, examining the workplace, and rock dusting.
Drilling bolt holes involves inserting the drill steel in the chuck,
drilling the hole, remove the steel, adding extension steels,
changing the bits. Bolt installation involves making up bolt
assemblies, bending bolts, inserting bolt in the hole, aligning bolt
in wrench, raising the bolt, and torque the installed bolt. The
sequence repeats until a mine section’s roof is secure. Roof
bolting may be regarded as a fairly structured and repetitive work
situation. There is an established work cycle that rarely does get
followed; because, a lot of variable external influences, like
variability in geology, interruptions from co-workers and
supervisors, machine malfunctions, supplies variability, etc. The
roof bolter operator is under consistent production pressure to
install as many bolts in one 8-hour shift as possible and to work
being alert to all of the dangers.

The roof bolter operator does his or her job in a confined
environment, i.e. limited working height as low as 114 cm and
close proximity and low visibility to a moving drill head mounted
ona boom arm 182-cm in length (see figure 2.)

Figure 2. A Roof Bolter Operator’s Work Posture and
Underground Coal Mine Workspace Environment

This restricted work environment puts the operator in awkward.
postures for tasks that require fast reactions to avoid being hit by
the moving machine parts. Restricted visibility due to a protection
canopy and low lighting conditions further complicates the task.
Health and Safety Accident Classification injury data base showed
an average of 961 roof bolter operator incidents per year over a
four year period, making roof bolting the most hazardous machine-
related job in underground mining, representing 16% of all
equipment related accidents in underground coal mines.

To address safety issues, MSHA (Mine Safety and Health
Administration) established a roof-bolter-machine committee with
members from the West Virginia Board of Coal Mine Health and
Safety, NIOSH, and roof bolter manufacturers. The committee
studied 613 accidents and 15 fatalities that were attributed to
inadvertent or incorrect actuation of control levers while the
operator was within the drill head or boom pinch-point area (see
figure 3). One major outcome of this study was the realization that
there is no data on safe speeds for booms operating close to
workers in confined environments like an underground coal mine.
The NIOSH-PRL is endeavoring to determine what boom speed
minimizes the roof bolter operator's chances of injury while still
doing his or her job effectively. ‘This question becomes even more
important in light of potential rules proposed by MSHA on
improving the design of roof bolters.

Figure 3. Artist concept of an operator caught within the
boom arm and canopy pinch-point area.

The information needed to answer the question is: 1) When does
the operator see the moving boom arm and drill head during the
bolting operation? 2) How frequently are there mishaps between
the operator and moving machine appendages? 3) What are the
distances between the operator’s hands, arms, legs and head and
the moving boom arm and drill head during each of the operator's
job tasks? 4) What changes do various operator postures, such as
kneeling on one knee, two knees or standing, make in the previous
three questions?

In order to effectively answer these questions, a sufficient number
of studies must be conducted to collect data on mishaps that cover
all of the variables. Laboratory and field experiments examining
these situations are difficult because of the complexity and the
instantaneous nature of the occurrences. Therefore, a computer-
based, three-dimensional solid object approach is being used as the
primary means to generate and collect the data. Data collected by
the roof bolter model consist of counting mishaps. In the model, a
mishap means two or more objects intersecting, e.g. the boom arm
collides with the operator’s hand, head or leg. Mishaps were
collected in three-dimensional computer environments using
collision detection. Consequently, limited laboratory experiments
were needed to provide input parameters (accurate field of vision
[4], human response in roof bolting postures, human motion
envelopes of body appendages and initial work starting postures)
for the roof bolter model, and to validate the computer simulations.


Early model input parameter values were guesses to allow the
model development to continue.

MODEL DEVELOPMENT

The roof bolting operation was broken down into specific tasks.
Klishis et al [1,2] observed the tasks and the amount of time spent
on each task, The task list provided a guide in developing the
experimental design for laboratory human subject tests and model
movement for computer simulations. Basic bolting motions in the
model were created from training videos, in-mine observations and
videos and critiques from bolter manufacturers and experts. The
simulation approach generates and collects collision data between
the machine and its operator while recording with many variables,
such as, the kneeing or standing posture, choice of risk behavior,
anthropology and machine’s appendage velocity. JACK
simulation software was the simulation tool chosen to develop the
roof bolter model; it is a human-centric visual simulation software
package and the software’s architecture lets users extend it’s
simulation functionality. The roof bolter model evolved from code
developed in Lisp programming interface and Jack Command
Language (JCL) that creates random human motion, random
motion goals for the hands and torso, and random motion of events
reflecting operator’s behavior and machine appendage speed [5].

The behavior motion parameters are based on statistics of machine
and human actions that could cause injuries or fatalities in a
bolter’s workspace. The highest percent of hazardous acts were
found in two bolter tasks: drilling the hole and installing a bolt [2].
The model contains risk behaviors involving both drilling and bolt
installation: (1) hand on the drill steel or bolt (see figure 4a), (2)
hand on the boom arm (see figure 4b), (3) hand on the boom arm
and then hand on the drill steel or bolt, and (4) hand off the boom
arm and drill steel or bolt (see figure 4c).

Table | identifies the variables considered for the model. During
simulation runs selected experimental conditions were held
constant to allow researchers categorizing changing variables to
make it easier for data analysis. The model allows investigators to
experiment with response variable behavior (number of collisions
between operator and machine) when changing the variables. The
operators’ response times were used in the database analysis.

Jack’s human motion kinematics is well defined and validated. [6]
The software’s manipulation process defines how the model’s
operator is to achieve the final posture for the whole body or head,
back, hand, arm or leg. The motion that the operator goes through
to achieve a final posture is described only through Jack’s motion
system. For example, the manipulation values for xyz-orientation
angles and xyz-positional coordinates define the final posture
position of the operator. Then the human motion system’s
algorithm generates and animates the motion-path to achieve this
final posture. Since the motion system is neither completely
discrete nor completely continuous gives rise to construct a model
with aspects of both discrete-event and continuous simulation. A
unique, combined discrete-continuous simulation was
accomplished by built-in random manipulation values within the
model before transformed into a motion-path by the human motion

system. Jack’s motion system would reflect the variance in that
motion-path as defined by these values.

Figure 4a. Operator's risk behavior, hand on steel

4 ey)
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Be eee

Figure 4c. Operator's risk behavior, hand off steel & boom