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A Simulation Approach Analyzing Random Motion Events

Between a Machine and its Operator

Dean H. Ambrose

National Institute for Occupational Safety and Health, Pittsburgh Research Laboratory

ABSTRACT

This paper presents an approach for representing and

analyzing random motions and hazardous events in a

simulated three-dimensional workplace, providing

designers and analysts with a new technique for

evaluating operator-machine interaction hazards in

virtual environments. 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. The project’s methodology includes

human factors design considerations, ergonomic

modeling and simulation tools, laboratory validation, and

collaboration with a mining equipment manufacturer.

Hazardous conditions can be analyzed in virtual

environments using collision detection. By simulating an

operator’s random behavior and machine’s appendage

velocity, researchers can accurately identify hazards, and

use that information to form safe design parameters for

mining equipment. Analysts must be discerning with the

model and not read more from the databases than what

the simulation model was designed to deliver.

Simulations provided an interesting approach to data

gathering in that there was no need for live subjects and

logistics – test sites and costs associated with

experiments–became insignificant. Collisions versus

speed, operators’ size, and risk behaviors proved the

versatility found in the data obtained from the model.

Preliminary results show that response time significantly

affects the number of collisions experienced by the virtual

subject. Also simulation data suggests that more

mishaps occur with hand-on-boom-arm risk behavior. 

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 was used as the primary means to

gather data on mishaps. In the model, mishap means

two or more object properties interacting. Consequently,

hazardous conditions were analyzed in virtual

environments using collision detection. 

The uncertainty or randomness inherent in the drilling

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 random motion, and one could easily visualize the path

of that motion. To model the random motion, the

sequence of someone drinking from a can of beverage

would reoccur until the can is empty, and each motionpath would differ slightly even though the motions look

alike. So the model would incorporate the randomness

of the motion and path variance within 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 motions give the model a realistic

representation of the operator’s motions and behaviors

found during any machine task. 

A model that includes any random aspects must involve

sampling, or generating random variate. The phrase

“generating a random variate” means to observe or

realize a random variable from some desired

arrangement of values of variables showing their

observed or theoretical frequency of occurrence.

Studies on workers job performance, machinery and

work environment has 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. A field study 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.
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BACKGROUND

Roof bolting is one of the most basic and the most

dangerous elements of underground 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,

bolting equipment operators install bolts to secure

sections of unsupported roof. A bolter crew’s typical work

sequence includes: general preparation and setup,

drilling a hole, and installing a bolt. The sequence

repeats until a section’s roof is secure. The roof bolter

operator does his or her job in a confined workspace

near moving machinery. 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. From 1992 to 1996 Health and

Safety Accident Classification injury data base showed

an average of 961 roof bolter operator incidents per year,

making roof bolting the most hazardous machine-related

job in underground mining.

To address safety issues, the Mine Safety and Health

Administration established a roof-bolter-machine

committee with members from the WV Board of Coal

Mine Health and Safety, NIOSH, and roof bolter

manufacturers. The committee studied 613 accidents

and 15 fatalities that attributed to inadvertent or incorrect

actuation of control levers while the operator was within

the drill head or boom pinch-point area (see figure 1).

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.

METHODOLOGY

A computer-based simulation approach was used to

generate and collect collision data between the machine

and its operator while dealing with many variables, such

as, the operator’s response time, knee posture, choice of

risk behavior, anthropology and machine’s appendage

velocity. 

Engineering Animation’s software, Transom JACK, was

the simulation tool chosen to develop a roof bolter model

for simulation. JACK is a human-centric visual

simulation software package. Jack’s software

architecture lets users extend it’s simulation functionality

by writing code with the Lisp programming interface and

Jack Command Language (JCL). 

The roof bolter model evolved from code developed in

Lisp and JCL that creates random human motion,

random motion goals for the hands and torso, and

random motion of events reflecting operator’s behavior.

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 only

the task of drilling the hole, because it involves more risk

behaviors: (1) hand on the drill bit (see figure 2a), (2)

hand on the boom arm (see figure 2b), (3) hand on the

boom arm and then hand on the drill bit, and (4) hand off

the boom arm and drill bit (see figure 2c). Also, video

footage of a roof bolter operation, in an actual

underground coal mine and a manufacturer’s training

video, were used to help develop the animated motions of

the operator in the model.

Figure 1. Artist concept of an operator caught within 

the boom arm pinch-point area.

The model allows investigators to experiment with

response variable behavior (number of collisions

between operator and machine) when manipulating the

variables. Table 1 identifies all of the variables

considered for the model. The operators’ response times

were withheld from the model because of proprietary

issues and the complexity of programming this during

simulation test runs. The response times were later used

in the data analysis phase. During simulation runs,

selected experimental conditions, shown in table 2, were

held constant. The resulting simulation lets investigators

generate, collect and analyze realistic data between a

machine and its operator. 

While watching animations produced by the software, the

model seems to accurately depict random motions. The

parameters used to generate random motions in the

model need to be validated. If the model is valid then the

decisions made with the model should be similar to those

that would be made by physically experimenting with the

roof bolter. Experiments on a full scale working mockup

of a roof bolter boom arm are currently being conducted

using human subjects to verify operator response times,

human motion data, and field of view [4] relative to the

bolter’s boom arm. Because the model’s validation

stages are in progress, the results reflected in this paper

include only preliminary simulation data.
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RESULTS

The model can generate 96 different scenarios that

mimic motions of the operator and machine during the

drilling task. The scenarios are defined by varying four

factors: four boom arm speeds [5], three operator

heights, four risk behaviors and two knee postures. 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. In the model’s program, an output

function sends each test run result to a computer file. In

addition to recorded data, each file contains (1) a

description of the test run scenario that characterizes

which working behavior is in use, (2) whether the

operator posture is leaning forward or is upright and (3)

whether the operator is kneeling on one knee or on both

knees. Table 3 shows the output file description. A

typical test series consists of 600 simulation test runs.

Figure 2a. Operator’s risk behavior, hand on steel bit.
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Figure 2b. Operator’s risk behavior, hand on boom arm.

Figure 2c. Operator’s risk behavior, hand off steel bit and boom arm.
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Table 1. Variables considered in the simulation model 

An important phase of data analysis is to create a

database of each test series. This requires several

steps. First, count the number of collisions (‘raw’) that

occur in each test run. Second, determine the number of

collisions (‘avoid’) in each test run that the operator could

have avoided by using a predetermined human response

time, taking 250 msec or 400 msec [6, 7, 8] to get out of

the way of a moving boom arm once seen. Third,

calculate the collision totals to be used for evaluation by

taking the difference between ‘raw’ and ‘avoid,’ resulting

in collisions (see table 4, ‘hit’) represented as four scatter

plots (figures 3a, 3b).

A scatter plot gives strong support for using regression

analysis. Regression analysis shows the relationships

between independent variables and one dependent

variable, such as taking into account the values of the five

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.

Along with regression analysis, a nesting technique is

used on each collision database to group factors

together, forming relationships that, when plotted, give

meaningful trends. For example, a collision-versusspeed plot for a desired risk behavior would be depicted

by three separate lines. Each line is identified by the

operator’s height, which includes both response times

and knee postures. For each test series, unique

collision-versus-speed plots were created using nesting

techniques and varying the levels of the risk behavior.

Simulation data regressions for a 114.3 cm (45 in) coal

seam height and a 152.4 cm (60 in) coal seam height,

are graphed in figures 4a, 4b through 8a, 8b.

Analysis of the databases reveals what happens when

‘variables’ change as they relate to the interactions

between the operator and the machine: 

• number of collisions versus boom arm speeds, 

the significance of the 

• operator’s response time, 

• knee posture, 

• choice of risk behavior, and 

• anthropology. 
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Table 2. Simulation Test Chart
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Table 3. Output File Description
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Table 4. Simulation Scenarios from Experimental Observations (sample listing)

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 (figures 4a, 4b). Also, simulation data indicates

that lower seam heights have more mishaps and are

more sensitive to the two response times in this

experiment. Factors such as age, strength or other

constraints relating to a person’s reaction time could be

used to generate a tailored response time. Experimental

data (figures 5a, 5b thru 8a, 8b) indicates that more

mishaps occur with risk behavior {0,1}, hands on the

boom arm. The lower seam height significantly affect

mishaps only in one risk behavior {0,1}, hands on the

boom arm. 

CONCLUSION

Ergonomists who provided technical support for this work

were overwhelmed with the infinite possibilities of test

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. The model requires further

enhancements to streamline its efficiency. For example,

the model’s code undergoes numerous but minor

changes to accommodate all test series and setups of

each simulation test run. Automating the modification

procedure that changes the code would improve the

model’s ease of use. Also, at present there is no

automatic scheme in the model to detect when the boom

arm enters and leaves an operator’s viewing area.

Response times rely on this information. Thus the time

element used to ascertain what collisions could have
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been avoided is performed manually by examining each

data run file and ‘observing’ when this event occurs.

Automating it would quicken data analysis and virtually

eliminate any possible inaccuracies manual observations

could cause.

The following general recommendations can be made

upon the outcome of this work. The model is only as

good as the system it defines; basically certain

parameters must be 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, increasing the model’s

ease of use will be essential if industry finds value in the

simulation approach presented in this paper as a

research tool.

Figure 3a. Collision totals of scenarios vs. machine 

boom arm speed in a 114.3 cm seam.

Figure 3b. Collision totals of scenarios vs. machine 

boom arm speed in a 152.4 cm seam.

Figure 4a. Collisions vs. machine boom arm speed and 

operator response time in a 114.3 cm seam.

Figure 4b. Collisions vs. machine boom arm speed and 

operator response time in a 152.4 cm deam.

Figure 5a. Collisions vs. machine boom arm speed and 

operator at risk behavior {0,0} in a 114.3 cm 

seam.
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Figure 5b. Collisions vs. machine boom arm speed and 

operator at risk behavior {0,0} in a 152.4 cm 

seam.

Figure 6a. Collisions vs. machine boom arm sped and 

opeator at risk behavior {0,1} in a 114.3 cm 

seam.

Figure 6b. Collisions vs. machine boom arm speed and 

operator at risk behavior {0,1} in a 152.4 cm 

seam.

Figure 7a. Collisions vs. machine boom arm speed and 

operator at risk behavior {1,0} in a 114.3 cm 

seam.

Figure 7b. Collisions vs. machine boom arm speed and 

operator at risk behavior {1,0} in a 152.4 cm 

seam.

Figure 8a. Collisions vs. machine boom arm speed and 

operator at risk behavior {1,1} in a 114.3 cm 

seam.
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Figure 8b. Collisions vs. machine boom arm speed and 

operator at risk behavior {1,1} in a 152.4 cm 

seam.

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