Verification and Validation of Roof Bolter Simulation Models

for Studying Events Between a Machine and its Operator

John R. Bartels, Dean H. Ambrose and Richard C. Wang

National Institute for Occupational Safety and Health (NIOSH),

Pittsburgh Research Laboratory 

ABSTRACT

This paper presents the results of a study to verify and

validate a computer model that represents and analyzes

motions and hazardous events in a simulated threedimensional workplace. The purpose of the computer

model is to support research that is investigating the safe

speed range for the vertical movement of roof bolter

boom arms to reduce worker injuries in underground

coal mines. The information obtained for this paper is

based upon a project that is investigating means to

reduce workers risks of injury from exposure to mining

machinery. The methodology being employed by the

project includes human factors design considerations,

anthropometric modeling and simulation tools, laboratory

validation, engineering interventions, and collaboration

with industry and an equipment manufacturer. The

results of this study were used to (1) determine the input

parameters that are unique to the mining environment

and needed to develop a credible, computer-based,

human-machine interactive model, (2) develop test

methods to measure the required parameters, and (3) to

refine the human-machine interactive computer model.

INTRODUCTION

PROBLEM

There is currently no regulations or method of

determining the safe speed of roof bolter boom arms.

Three dimensional computer simulations provide

machine designers and safety analysts with an accurate

model for evaluating collision hazards concerning

operator-machine interaction. Computer simulations of

roof bolting tasks were conducted using bolter machine

and bio-mechanical human models that ran on

Unigraphics Solutions /Engineering Animation Incís

JACK simulation software. Computer simulation allows

multiple environments, virtual humans, and differing

scenarios to be studied, which would be dangerous and

time- and cost-prohibitive with field data studies. Before

collecting final data, preliminary results of the roof bolter

model need to be validated to ensure that parameter

assumptions made for the computer-based simulation

conform to actual field practice. This study verified

operatorsí response times, task motions, and field of

view relative to the roof bolter boom arm. Human

subjects tests with a full scale working mockup of a roof

bolter boom arm were used for collecting motion data

that helped determine parameters for building valid and

credible models.

BACKGROUND

After miner crews in underground coal mines have cut a

section of coal, roof bolter operators have the job of

installing steel rods (bolts) into the mine roof to control

cave-ins by securing sections of unsupported roof. Roof

bolting may be regarded as a fairly structured and

repetitive work situation. The entire bolting operation

must be completed in a confined environment, for

example, limited working height as low as 114.3-cm (45-

in), in the vicinity of moving machinery (Figure 1). The

problem becomes more pronounced the lower the seam

height becomes. The confined environment requires the

operator to work in awkward postures and to perform

tasks requiring fast reaction times to avoid being hit by

the moving machine appendages. Further compounding

the problem is the low lighting conditions found in mines

and the restricted visibility due to the protective canopy

on the bolter machine. These conditions combine to

make roof bolting one of the most dangerous

occupations in underground mining. For the years 1992

through 1996, the Mine Safety and Health Administration

(MSHA) injury database showed there was an average of

961 roof bolter operator accidents per year, representing

16% of all equipment related accidents, in underground

coal mines.

In order to address safety issues, a roof-bolter-machine

committee was established by MSHA in 1994. The

members of the committee were composed of MSHA,

the West Virginia Board of Coal Mine Health and Safety,

NIOSH, and roof bolter manufacturers. This committee

studied 613 accidents that occurred during drilling and

roof-bolt installation. The committee also looked at 15

fatalities attributed to inadvertent or incorrect actuation of

the feed control lever, while the operator was within the

drill head or boom arm pinch-point area. One outcome of

this committeeís study was the realization that there was
no data on the safe speeds for roof bolter booms

operating close to workers in a confined environment like

an underground coal mine. Emphasis was placed on

hazards related to the movement of the boom arm or

mast of a roof-bolting machine. The committeeís

objective was to identify hazards and recommend

solutions. The data-collection effort consisted of analysis

of: MSHA accident data; visits to underground mines and

interviews with experienced roof bolting machine

operators; discussions with roof bolting machine

manufacturers; interviews with workers injured while

performing roof bolting tasks; and reviews of research on

roof bolting safety. A set of recommendations to

increase the safety of roof bolting operations was

developed, in particular, reduction of the bolter boom

arm speed.

The main question that needs to be answered is what

range of boom speeds minimizes the roof bolter

operatorsí chances of injury while still allowing the roof

bolter operator to perform his job effectively. This

question becomes even more important in light of

potential rules proposed by MSHA on improving the

design of roof bolters.

The information needed to answer this question is: 1)

When does the operator see the boom arm and drill

head during the roof bolting operation? 2) How

frequently are there collisions between the operator and

the roof bolter machine appendages? 3) What are the

distances between the operatorís hands, arms, legs and

head and the roof bolterís boom arm and drill head

during each of the bolter operatorís job tasks? 4) What

changes do various operator postures, such as kneeling

on one knee or two knees, make in these other

parameters?

In order to effectively answer these questions, a

sufficient number of studies must be conducted to collect

collision data that covers 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 computerbased, three dimensional solid object approach is being

used as the primary means to generate and collect the

data. Data collected in the roof bolter model consisted of

the counting of mishaps. In the model, a mishap means

two or more objects intersecting; for example, the boom

arm collides with the operatorís arm, hand or leg.

Hazardous conditions were collected in threedimensional computer environments using collision

detection. Consequently, limited laboratory experiments

were needed to provide accurate parameters for the roof

bolter model, and to validate the computer simulations.

The roof bolting operation was broken down into specific

tasks. Klishis et al observed the tasks and the amount of

time spent on each task. [5] The task list provided a

guide in developing the experimental design for

laboratory human subject tests and discrete movement

scenarios for the computer simulations. This computerbased simulation was used to generate and collect

collision data between the machine and itís operator

while recording many variables, such as the operator's

response times, operator postures, risk behaviors,

anthropometry, and machine appendage velocity. The

roof bolter model evolved from code developed in Lisp

and Jack-Command-Language that creates random

human motions, random motion goals for the hands and

torso, and random motion of events reflecting operator's

behavior.

The uncertainty or variability inherent in operator

movement required for the drilling and bolting tasks was

incorporated into the model to effectively determine the

likelihood of an operator being injured. In the model

random motion is generated, individual paths differed

slightly even though the motions look very similar. The

model incorporates variability in the motion and a path

variance within that motion. 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 variability. These random motions

give the model a realistic representation of the operatorís

motions and behaviors found during the control of any

roof bolter task. A model that includes any random

aspects must involve sampling, or generating random

variants. The phrase ìgenerating 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.

RESEARCH QUESTIONS ADDRESSED

1. Does the reduced lighting conditions in underground

coal mines reduce the optimum viewing area of a

worker? Federal regulation requires illumination levels

around a roof-bolting machine to be 0.06 fl (footLambert). It is unknown if these lighting conditions will

affect the area in which an individual can detect a

moving hazard. Optimum viewing areas in which roof

bolt operators can detect moving hazards were

measured, using standard optometristsí equipment,

under normal lighting conditions and under mine

illumination conditions

2. What is the whole body response time of an

individual performing bolting tasks in various postures

and various mine seam heights ranging from 114.3-cm

(45-in) to 182.9-cm (72-in)? Due to the confined

environment of mining, the postures of bolter operators

are unlike the postures of workers in other occupations.

What effect does restricted space have on individual

response times required to perceive and avoid hazards?

When equipment operators detect a machine hazard,

they want to get out of the way. In spite of awkward

positions and postures imposed on operators by their

confined environment, they require quick responses to

dangerous situations. To assist in the response time

investigation, operator motion was recorded, while they

avoided the moving appendages of the roof bolting

machine.
3. How well does the computer simulation of roof bolter

tasks match the actual movements of individuals (what is

the motion envelope for each of the bolting tasks)?

4. What is the operatorís position and orientation with

respect to the bolterís controls and boom arm

appendage? The starting position of the operator

defines the movement envelope generated by the

computer simulation for the virtual humans.

STUDY POPULATION

The study population for the computer simulation covers

the 5th through 95th percentile male. The study population

represents the target population, which is 99% male,

however two female miners were among the study

volunteers and were used to represent the 20th to 30th

percentile male operators due to the rarity of female

operators. Since the objective of the laboratory tests is

not to duplicate the entire simulation population, but only

to verify that the simulation represents an accurate

picture of the real world, a small sample of 12 subjects

were tested. Movements of the virtual human will be

compared to those of their test subject counterpart to

evaluate the performance of the model. The optimum

viewing area tests used 12 local subjects from NIOSH’s

Pittsburgh Research Laboratory (PRL) since no special

mining skill was involved. The response time and human

motion data testing were conducted using 12 subjects

from the local office of the United Mine Workers of

America (UMWA), which included two female volunteers

which were included in the study, to accurately duplicate

the skills and experience involved in operating mining

equipment. The anthropometrics for the 12 subjects

used in motion and response time studies are listed in

Table 1.

DATA COLLECTION

Laboratory tests were performed on human subjects and

the results were used to compare laboratory and

computer simulation results for basic input parameters to

the roof bolter model (Figure 2). Also, the optimal

viewing area (vision cone) and human motion data were

used to evaluate the accuracy of the computer

simulation. Operatorsí response times were used to

quantify the effects of parameter values that are used

with the computer simulation to determine occurrences

of collision and collision avoidance.

FIELD Of VISION IN REDUCED LIGHTING

For acceptable viewing in reduced lighting conditions,

MSHA minimum lighting requirements mandates

illumination levels of 0.06 fl. Testing was required to

determine the viewing area, accurate field of vision and

awareness of hazards with this background lighting and

a cap lamp. Measurements were made using standard

optometristsí peripheral vision measuring equipment.

Visual acuity was tested using Snellen charts. If subjects

normally used glasses or contact lenses to correct their

vision, the subject wore these items during the vision

tests. Dependent measures included tests of peripheral

vision (using a modified Peripheral Vision Chart), and

visual acuity (using Snellen eye charts).

Five tests were performed with each subject for both the

visual acuity and field of visions. Using normal

procedures for visual acuity testing, the subject was

seated 609.6-cm (240-in) away from the Snellen charts

and a standardized procedure was used to determine the

smallest row of letters that the subject could read under

the experimental conditions.

The five vision test conditions consist of a normal lighting

level, a 0.06 fl level, 0.06 fl levels with a cap lamp, 0.03 fl

level, and 0.03 fl levels with a cap lamp. Test subjects

were asked to wear a standard hard hat and, for two

tests, a hardhat and cap lamp. The test subjects were

placed in a seated position. The background lighting was

adjusted to normal, 0.06 fl, 0.06 fl wearing a cap lamp,

0.03 fl and 0.03 fl wearing a cap lamp. The subjects were

asked to place their head against a rest 60.96-cm (24-in)

from a modified peripheral vision chart. Test subjects

were instructed to indicate when they detected the

movement of a white ball under each of the three

different lighting conditions. Tests were conducted at 45

degree increments above and below the horizontal plane

(on both sides) and vertically from directly above the

head. The angle at which the subject becomes aware of

the white ball in their field of vision was recorded for each

experimental condition.

HUMAN RESPONSE IN ROOF BOLTING POSTURES

Human motion response times were measured for the

operator postures unique to operating a roof bolter.

Appendix A describes the equipment constructed and

used for this human subjects testing. Operator test

postures in 114.3-cm (45-in) and 152.4-cm (60-in) seam

heights were performed with the operator kneeling,

leaning forward with the head tilted to one side and

looking at a drilled hole location. The operatorís test

posture in a 182.9-cm (72-in) seam height would be to

stand if possible or hunch over to accommodate the

standing posture. The tests trials were repeated three

times for each seam height and in the following postures:

kneeling on one knee at a time and kneeling on both

knees. The tests were repeated for 182.9-cm seams in a

standing or stooping posture. The human motions were

measured and recorded using the motion tracking

system. Appendix B describes the motion tracking

system used in the laboratory tests.

Test subjects were asked to position themselves in a bolt

insertion position with respect to the wooden mockup

roof bolter. The right hand was situated on the bolter

controls and the left hand on the drill steel. The head

was above the boom, and the subject looked at the drill

hole. At a given verbal signal, the test subjects were

instructed to move themselves from the motion envelope

of the bolter boom (move from a forward leaning position

with extended arms to a vertical position with arms

resting at the side). The timing of the verbal cue was

random so that the subject was not able to anticipate

when to start moving. For each experimental condition,
three repetitions of the test were performed. At least two

minutes of rest was provided between repetitions. This

information was used to determine if the simulated

human could have avoided a collision with the bolter

boom.

HUMAN MOTION ENVELOPS

The human motion envelopes were measured for the

operator postures unique when operating the roof bolter.

Operator test postures in 114.3-cm (45-in) and 152.4

(60-in) seam heights were performed with the operator

kneeling posture either on one or two knees. Operatorís

test posture in 182.9-cm (72-in) seam height would stand

if possible or hunch over to accommodate the standing

posture. The subjects were supplied with standard

mining safety equipment consisting of a hardhat,

kneepads and safety glasses. The human motion

envelopes were measured and recorded using a motion

tracking system.

Test subjects were asked to position themselves in a

specified posture with respect to the working wooden

mockup roof bolter. At a given signal they completed a

roof bolting sequence. The specific roof bolting tasks

were: insert drill steel, raise boom to drill hole, lower

boom and remove drill steel, put bolt (using a wrench if

needed) in chuck, and raise boom to install bolt, torque

bolt and lower the boom and remove wrench. Typically a

complete bolting sequence can be completed in 25 to 30

seconds. The sequence was repeated three times in

each posture in each of the seam heights

DATA ANALYSIS

A randomized block experimental design was utilized for

all phases of the study. Dependent measures in this

experiment 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 was used to identify those conditions where

significant differences existed.

FIELD OF VISION IN REDUCED LIGHTING

The results of analysis were averaged for the 12 subjects

and a vision area for the unique lighting conditions of

underground mining environments was developed, which

accounted for the use of a 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 minerís cap

lamp and hard hat were the most significant in terms of

input to the simulation model. Typical results are shown

in Table 2 and Figure 3.

The most significant reduction in a subjectís 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.

HUMAN RESPONSE 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. Three trials were captured with

the motion tracking system. The operator reaction times

were then averaged for each test subject. Summery

results were obtained from Figure 4.

The data for the head and hands were considered the

most significant for reaction characterization because

these are the body parts most likely to be injured.

Engineering parameters were calculated for these

sensors. Table 3 shows a sample of typical results.

Average and maximum velocity and acceleration were

calculated for each of the sensors.

The range of variation is what one might expect from

human motion, maximum speed and acceleration

increase as the working space increased. When the data

is viewed as a function of scale, the variations in reaction

parameters were reasonable. This range was averaged

by anthropometrical size, based on the National Health

Examination Survey [9], and used as the reaction

response for the digital human 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 ENVELOPES

In order to provide input parameters to the virtual human

simulation, the data from the motion tracking and

capturing system was divided into six discrete tasks: (1)

loading the drill steel into the bolter arm; (2) drilling the

roof; (3) lowering the bolter arm; (4) loading the roof bolt

into the bolter arm; (5) bolting the roof; and (6) lowering

the bolter arm. The discrete points in the data where

these particular events occurred were 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 5).

The motion data for each of the three trials by a single

test subject were then analyzed using ANOVA. For

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 independent

variable. Standard deviations at each centimeter of

bolter boom movement were determined and the

maximum standard deviation was selected as the seed

number for range of variability for the virtual human

movement.

The data for tasks 1and 4 did not lend themselves to this
method of statistical analysis due the lack of a consistent

independent variable. These data were analyzed by

taking a standard deviation of the whole series of data

points consisting of a scalar vector from the stationary

boom sensor. Since the data for these tasks is not

critical for the objective of the study (operator collisions

with a moving bolter arm), this was considered adequate.

This data was then classified by anthropometrical size

for incorporation into the model. Typical results

summarized by anthropometrical size, seam height, and

operator postures are shown in a small example Table 4.

The results of motion variance analysis produced a

scattered range of variation, which at first glance does

not produce a consistent pattern. This range of variation

was small, when the data is viewed as a function of

scale, 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 increased working space. The

difference in movement between tests ranged from 2 cm

to 30 cm. This range is the seed number, which is close

to the originally assumed variance of motion used in the

computer-based human model studies.

HUMAN-MACHINE INITIAL START POSTURE

Using the HUMAN MOTION ENVELOP data, an average

starting position for the subjectís knees and back sensor

was determined and a standard deviation for these

points determined. The results were then categorized by

the subjectís height position along the anthropometrical

scale and averages obtained for 10 percentile

increments. Typical results are shown in Table 5.

Measurement locations of the back and knees provided

initial postures of the operatorís body relative to the

machine. A value of ëangle back Yí, i.e. 17.77, provides

the angle at which the body is to the boom arm. The

ëback Xí value, -89.94-cm, provides a distance in the

coordinate X direction from the boom arm. This

information provides the digital human model with a

realistic starting position for the simulated bolting

sequence and a valid range of variation in initial position

for multiple simulation runs

CONCLUSION

Equipment manufacturers require specific engineering

data to provide ergonomically correct designs for worker

protection. Digital simulations provide a virtual operator

with no limitations; the required tasks can be performed

repeatedly in infinite test scenarios. This allows the

design parameters to be evaluated with out the need for

extensive and potentially dangerous field studies.

However, to provide valid models, adequate data

defining the human movement and variation of

movement are required. This paper shows the methods

used to provide that input in to the unique environment of

underground mining. The techniques used are also

applicable to a range of industrial applications. The

model will still need to be verified once the parameters

have been incorporated and the results compared to field

and lab studies. The data obtained in these human

subject tests is also a database that will be used in the

eventual validation of the final digital model.

ACKNOWLEDGMENTS

Bill Rossi and Ray Helinski provided technical support in

operating the motion tracking and data collection system.

Mary Ellen Nelson, Al Cook, and Mary Ann Rossi

assisted in the laboratory human subject tests. 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

mockup 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.

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1993,143 pp.

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Model for Reduction Through Trainingî, Mining

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220pp.

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1024, 1978, 421 pp.

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Hazards in Underground Coal Minesî, Bureau of Mines

RI 9568, 1993, 25 pp.

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Academic Press, NY, NY, 1980, 418 pp.

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Mines IC 8886, 1982, 337 pp.

APPENDIX A - MOCKUP ROOF BOLTER

A model of a typical roof bolter arm was constructed

from wood, Figure 6. In all other respects, this model

resembled the actual equipment used underground. It

was dimensionally identical and moved in the same

manner. The use of wood for the construction material

was dictated by the requirement for the Flock of Birds

motion tracking system, which is sensitive to ferrous

metals. The only metal parts in the model were some

small pins and shafts at the articulating joints and the

hydraulic cylinders.

The wooden bolter boom arm mockup has four hydraulic

actuators; one each for boom rise, swing, sump and

stabilizer jack. Normally, these actuators are pinned, or

otherwise connected, at each end to transmit generated

forces to the structure. High pressure 175.75 kgf/cm2

(2,500 psi) was required in the hydraulic system in order

to provide the necessary flow and speed control.

Although the actuators were of small diameter, they still

were capable of generating high forces due to this

pressure requirement. It was not feasible to monitor a

pressure rise resulting from an inadvertent contact

between the test subject and the mockup. Such a

contact would have resulted in a practically undetectable

pressure change even though forces high enough to

cause injury would be present. There fore a mechanical

means to limit the forces available from the mockup was

devised. The hydraulic actuators on the bolter arm were

all assembled using a slip clutch connector, which limits

the amount of force that the actuator can produce.

These were adjusted and tested prior to any human

subjects testing to insure that they would perform as

intended. If contact between the human subject and the

moving bolter mockup should occur, the clutch slides on

the hydraulic actuator and prevents any injury. These slip

clutches were installed on all of the hydraulic actuators in

the mockup.

The perimeter of the boom arm mockup was protected

from contact with the test subjects with a series of five

pairs of class 2 laser emitter diodes and sensors. This

system of sensors was connected to a fast dump

hydraulic valve. Note that using the sensors to shut off

the hydraulic pump would have been ineffective because

the pump would continue to produce flow until the motor

had coasted to a stop; this would have taken several

seconds. The use of a fast acting hydraulic dump valve

provided nearly instantaneous halt of the mockupís

motion. In the event of an operator coming close to the

moving parts of the bolter mockup, the light curtain would

be broken. This would cause the dump valve to open

and remove all hydraulic pressure, effectively stopping all

movement of the mockup. The electronic interface

between the laser sensors and the dump valve was

designed to ìlatchî a break in the sensors light beam.

Thus, motion of the bolter mockup would not start again

just because the light beam became unobstructed.

Once any of the sensors had been tripped, a reset switch

had to be depressed to reactivate the system hydraulics.

The electronics interface had an indicator display that

showed which of the sensors had been tripped. These

safety features also were tested extensively to insure the

utmost in the human subjectís safety.

The final mechanical precaution was to construct the

simulated mine roof from suspended Styrofoam in the

unlikely event that the test subject should contact or be

trapped between the moving bolter arm and the

simulated roof. The lightweight Styrofoam would move

out of the way.

In order to assure safe operating speeds, the boom arm

speed was limited to 55.88-cm/s (22-in/s) the maximum

speed that the roof bolter computer model is set. Normal

bolter boom arm speeds can vary (i.e., 17.28- to 55.88-

cm/s) depending on the size of the machines hydraulic

pump. The hydraulic system for this mockup was

designed with flow adjustments on all actuators. These

flow controls allowed each of the mockupís actuator

speeds to be controlled independently. The test speed

was set for 40.64 cm/s (16-in/s), the speed MSHA is

considering as a regulated safe speed.

APPENDIX B - MOTION TRACKING AND DATA

CAPTURING SYSTEM

The structure of the UniGraphics Solutions-Engineering

Animation Inc., JACK software makes it ideal for use with

virtual reality (VR) input and output devices. The use of

VR enables the user to become the virtual human figure

and inhabit the virtual environment. VR features could

become very useful to build valid and credible simulation

models. This is possible with the use of VR interfaces

that supports sensors, such as using Ascension Flock of

Birds with JACK software.

One of the best ways to model human movement is to

use a real human to generate motion. The Flock of Birds

(FOB) motion tracking system and JACK human

modeling software provides these capabilities. Realistic

human movements can be recreated in real time using

position and orientation information from the Flock of

Birds to drive the virtual human figure in a JACK

simulation environment. Using FOBís sensor

information, Jackís MoCap module enables automatic

anthropometrical scaling and storage of subjects and the

collection and recording of the subjectís motions.

The FOB is a six degree-of-freedom measuring device

that can be configured to track the position and

orientation of up to 60 sensors by the transmitter
simultaneously. Each sensor can make from 30 to 144

measurements per second of its position and orientation

when the sensor is found within 10 feet of its transmitter.

Ascension provided PRL with a range of frequencies that

would optimize a sensorís sensitivity when tracking

motion around metallic objects. Tests were run on the

bolter mockup around those areas containing metallic

objects. PRLís investigators optimized the sensor

sensitivity for the test conditions by setting them to 68.3

measurements per second. The FOB determines

position and orientation by transmitting a pulse DC

magnetic field that is simultaneously measured by all

sensors in the Flock. From the measured magnetic field

characteristics, each sensor independently computes its

position and orientation and makes this information

available to a host computer. A FOB setup consisting of

more than four sensors are configured into a Motion Star

model.

The Motion Star model could consist of a chassis of up

to 20 FOB sensors. PRLís Motion Star system currently

has only 12 sensors. It takes a minimum of eleven

sensors to track the motion of a human adequately and

the twelfth defined the roof bolter boom arm (see Table

6). Fasteners used by industry attach the sensor to each

wrist, elbow, knee, and foot, and to the neck, head and

lower back (see figure 7). Because each sensor has its

own independent computer, the measurement rate is

independent of the number of sensors. The Ethernet

interface was used for FOBís communication with a host

computer that ran JACKís software module MoCap.

Table 1. Anthropometrics size of subjects

Subject Height, cm Weight, kg Age Gender Percentile Range

1 180.3 84.9 47 m 83.4 80-90

2 174.5 81.6 54 m 50.1 50-60

3 176.4 80.6 41 m 60.04 60-70

4 175.8 81.4 44 m 57.89 50-60

5 178.9 84.3 49 m 78.07 70-80

6 182.7 88 49 m 91.25 90-95

7 168.9 77 53 f 24.651 20-30

8 168.7 76.4 47 f 24.301 20-30

9 176.9 83.4 50 m 62.69 60-70

10 182.4 89.9 47 m 90.13 90-95

11 176.1 83 44 m 58.9 50-60

12 173.4 79.3 48 m 48.91 40-50

1Male percentile used to categorize female subjects

Based on National Health Examination Survey9
Table 2. Vision cone in reduced lighting

BOTH EYES

 DEGREES Normal light 21fL .06fL .06fL w/lamp/hat .03fL .03fL w/lamp/hat

0 70.02 63.55 65.43 65.8

4 68.20

45 61.04 63.07 58.06 55.9

4 55.94

315 65.94 65.01 67.17 62.9

5 66.04

90 54.28 53.13 34.51 46.1

7 32.01

270 61.39 57.03 57.38 50.3

9 59.04

180 60.45 61.93 63.55 61.3

9 65.22

135 60.83 59.81 64.36 53.5

6 52.25

225 65.94 65.01 67.17 60.2

6 64.80

LEFT EYE

 DEGREES Normal light 21fL .06fL .06fL w/lamp/hat .03fL .03fL w/lamp/hat

0 53.97 57.03 56.31 49.9

0 59.66

45 53.56 45.00 36.87 47.2

9 38.37

315 39.45 52.25 52.70 51.3

4 55.56

90 43.47 35.71 28.44 38.3

7 28.44

270 61.39 56.67 56.31 50.3

9 57.03

180 67.86 62.70 64.25 61.3

9 62.45

135 54.38 49.90 52.25 49.4

0 51.34

225 65.64 62.95 62.45 57.7

2 66.04

RIGHT EYE

 DEGREES Normal light 21fL .06fL .06fL w/lamp/hat .03fL .03fL w/lamp/hat

0 66.80 64.25 64.47 60.8

3 61.39

45 59.19 60.26 61.25 53.9

7 52.25

315 67.43 64.36 66.04 62.9

5 68.20

90 42.51 48.63 35.31 40.1

6 30.26

270 59.66 52.91 59.04 48.3

7 59.35

180 49.40 48.37 51.34 47.8

3 56.13

135 51.34 54.78 42.51 48.3

7 39.81

225 65.74 62.95 62.45 57.7

2 66.04
Table 3. Example of operator reaction parameters in a 114.3-cm (45-in) seam height

Subject Position

Maximum

Speed of

Head, cm/sec

Elapsed Time,

sec (*)

Maximum

Acceleration,

cm/sec2 

Average

Speed,

cm/sec

 Head Left Hand Right Hand Head Left Hand Right Hand

6 Both Knees 39.18 0.667 392.91 973.04 120.88 26.91 37.61 4.25

 Left Knee 23.19 0.411 332.70 493.99 626.46 16.40 21.02 22.70

 Right Knee 32.76 0.667 394.30 679.48 72.98 24.42 16.84 2.53

10 Both Knees 60.67 0.622 749.18 2978.87 272.38 33.34 53.47 13.04

 Left Knee 97.90 0.733 1438.55 2029.96 533.81 49.61 69.82 17.28

 Right Knee 101.75 0.944 1796.46 1678.98 1609.13 54.10 31.36 24.03

(*) Time to Reach Maximum Speed of Head

Table 4. Standard deviation of motion 50th - 60th percentile in a 114.3-cm (45-in) seam height

TASK NO Std Dev

HEAD (cm)

Std Dev LEFT

HAND (cm)

Std Dev RIGHT

HAND (cm)

1 Insert Drill 5.57 12.27 13.44

2 Drill Roof 3.68 9.20 2.59

3 Lower Boom 2.93 16.02 2.87

4 Insert Bolt 4.49 13.54 21.38

5 Bolt Roof 2.09 5.12 3.29

Both Knees

6 Lower Boom 2.56 8.43 5.59

1 Insert Drill 6.03 12.05 17.04

2 Drill Roof 3.48 13.91 12.64

3 Lower Boom 3.22 8.45 13.52

4 Insert Bolt 5.67 12.74 23.93

5 Bolt Roof 3.83 15.51 11.16

Left Knee

6 Lower Boom 4.23 3.98 10.53

1 Insert Drill 5.40 6.49 6.18

2 Drill Roof 4.09 7.15 28.06

3 Lower Boom 6.11 18.52 14.84

4 Insert Bolt 8.23 11.28 16.12

5 Bolt Roof 3.22 6.50 3.77

Right Knee

6 Lower Boom 4.71 6.44 3.27
Table 5. Starting position for 114.3-cm (45-in) seam height

 on both knees for 50th-60th percentile

Mean (cm) Standard

Deviation (cm)

Distance Back 93.08 7.06

Distance Neck 81.11 17.53

Distance Left Knee 48.06 17.22

Distance Right Knee 60.77 14.22

BACK X -89.94 2.24

BACK Y 60.14 9.18

BACK Z 240.54 2.28

Angle Back X -94.47 2.87

Angle Back Y 17.77 6.04

Angle Back Z 97.72 5.45

NECK X -52.94 4.78

NECK Y 97.01 10.07

NECK Z 238.28 5.71

Angel Neck X -99.41 17.19

Angel Neck Y 63.84 5.39

Angel Neck Z 106.17 23.91

Left Knee X -48.20 2.31

Left Knee Y 26.43 7.25

Left Knee Z 215.63 3.61

Angel Left Knee X 99.66 3.43

Angel Left Knee Y 30.07 5.27

Angel Left Knee Z -100.58 13.31

Right Knee X -42.17 2.83

Right Knee Y 25.64 7.35

Right Knee Z 247.56 3.53

Angel Left Knee X 86.84 5.84

Angel Left Knee Y 28.27 2.62

Angel Left Knee Z -104.78 3.58

Table 6 ñ Sensor locations

Sensor Location

1 Head

2 Waist

3 Neck

4 Left Elbow

5 Right Elbow

6 Left Wrist

7 Right Wrist

8 Left Knee

9 Right Knee

10 Left Foot

11 Right Foot

12 Boom Arm
Figure 1 Roof Bolter Operator in Low Seam

Figure 2. Vision cone in the roof bolter model
Figure 3. Vision cone in degrees
Figure 4. Head reaction in three trials

HEAD REACTION

115

120

125

130

135

0 5 10 15 20

Time, seconds

Distance, cm

Figure 5. Determination of task starting points

0.00

100.00

200.00

300.00

400.00

500.00

600.00

700.00

800.00

time

2.274.536.809.07

11.3313.6015.8718.1320.4022.67

Boom

Acceleration

Figure 6. Roof bolter boom arm mockup
Figure 7. Sensor locations on subject