Machine Injury Prediction by Simulation Using Human Models 

Dean H. Ambrose, John Bartels, Augie Kwitowski, 

Raymond F. Helinski and Sean Gallagher 

National Institute of Occupational Safety and Health (NIOSH), 

Pittsburgh Research Laboratory 

ABSTRACT 

This paper presents the results of a study using 

computer human modeling to examine machine appendage 

speed. The objective was to determine the impact of roof 

bolter machine appendage speed on the likelihood of the 

operator coming in contact with. A contact means two or 

more objects intersecting or touching each other, e.g., 

appendage makes contact with the operator’s hand, arm, head 

or leg. Incident investigation reports do not usually contain 

enough information to aid in studying this problem and 

laboratory experiments with human subjects are also not 

feasible because of safety and ethical issues. As an alternative, 

researchers developed a computer model approach as the 

primary means to gather data. By simulating an operator’s 

random behavior and machine’s appendage velocity, 

researchers can study potential hazards of tasks where it is not 

possible to perform experiments with human subjects. 

 Analysis information is helpful to the mining 

industry in terms of making recommendations that reduce the 

likelihood that roof-bolter operators experience injury due to 

contact with a moving boom. Data analysis of roof bolter 

simulations show that the virtual-operator’s response time has 

little effect on the number of contacts experienced. Based on 

frequency and cross-tabulation, regardless of other variables, 

contact incidents were always greater when the boom was 

moving up, were always greater on the palm, and were always 

greater for the boom part of the machine. Also, regardless of 

boom speed, the 25th-percentile-sized operators experienced 

more contacts than did other operator sizes. Furthermore, 

regardless of boom speed, the 152-cm mine seam experienced 

more contacts than did other seam heights tested. Results of a 

survival analytic approach suggest that controlling the boom 

speed is the most important factor in determining the risk of 

an operator making contact. Based on the data collected, 

boom speed greater than 41 cm/s results in a substantial 

increase in risk to the roof bolter operator and should probably 

be avoided. At speeds less than or equal to 25 cm/s are 

associated with a more modest relative risk, which represents 

an acceptable level of risk. Also, at speeds between 25 and 41 

cm/s, there could be a particular boom speed in this range at 

which a significant inflection point in the relative risk 

estimates occurs. Based on the results, this issue needs to be 

addressed by future research. 

INTRODUCTION 

 Several injuries to operators of underground coal 

mining equipment have led to an investigation of safe vertical 

velocities of a roof bolter boom-arm at the National Institute 

for Occupational Safety and Health (NIOSH), Pittsburgh 

Research Laboratory (PRL). Accident investigation reports 

from the Mine Health and Safety Administration do not 

usually contain enough information to aid in studying 

interactions between machines and their operator. Laboratory 

experiments with human subjects are also not feasible because 

of safety and ethical issues. With this in mind, researchers at 

PRL successfully developed for research a computer model 

that uses Jack® specialized computer simulation software. 

The model contains a virtual-mine environment that includes 

models of a roof bolter (Figure 1) and operator. Also, it 

experimentally mimics the human and machine actions that 

can cause a fatality or injury. The model generates contact 

data by means of simulation while exercising the model with 

several variables associated with the machine and its operator, 

such as: coal seam height, the operator’s anthropometry, work 

posture, choice of risky behavior, and machine’s appendage 

velocity. The resulting simulation database is studied by 

researchers to investigate appendage speeds and ways to 

decrease contacts or possible injuries to the miner by 

improving machine designs or the tasks themselves. 

 One of the most difficult problems faced by a model 

that generates human motions is trying to determine whether it 

accurately represents the actual mechanical system being 

studied. Researchers successfully incorporated within the 

model the randomness of the operator’s motion and path 

variance within that motion. This randomness gives NIOSH’s 

simulations the capability to realistically represent the 

operator’s motions and risky behaviors found while executing 

any machine task. Ambrose [2000], Ambrose [2001], 

Ambrose [in preparation], and Volberg [2002] reports in detail 

the roof bolter model development and the random virtualhuman motions used in the model. 

 Before collecting final simulation data, Bartels et al. 

[in preparation] test results on the roof bolter model were used 

to validate and ensure that parameter assumptions made for 

the computer-based simulation conform to actual field 

practice. Actual practice was determined through training 

videos, in-mine observations and videos, and working with a 

bolter manufacturer and experts. Bartels et al. [2001] and 
Bartels et al. [in preparation] studies verified operators’ 

response times, task motions, and field of view relative to the 

roof bolter boom-arm. Human subject’s tests with a full scale 

working mockup of a roof bolter boom-arm (Figure 2) and 

motion tracking system were used for collecting motion data 

that helped determine parameters for building valid and 

credible models. 

 This paper discusses NIOSH’s success in achieving 

its expected outcome to examine the speed range of a machine 

appendage for different workplace scenarios and compare 

statistically which is optimal in awarding contacts to miners. 

Previous studies on workers job performance, machinery and 

work environment has identified miners’ risks and hazard 

exposures while bolting [Klishis et al.1993a, et al. 1993b]. 

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 [Turin 1995]. 

Seven recommendations to increase the safety of roof bolting 

operations were developed. 

 

RESEARCH

 The main question that needed answered is what 

range of boom-arm speeds minimizes the roof bolter 

operator’s chances of contact or possible injury while still 

doing his or her job safely. In order to effectively answer the 

question, a sufficient number of studies must be conducted to 

collect data on contacts and variables that influence them. A 

contact means two or more objects intersecting or touching, 

e.g., the boom-arm makes contact with the operator’s hand, 

arm, head or leg. 

 A computer-based model approach was used to 

generate and collect contact data between the machine and its 

virtual-operator while recording predictor variables, such as 

the seam height, operator's starting positions, operator 

postures, risk behaviors, anthropometry, and machine 

appendage velocity. Data collected by the roof bolter model 

consist of counting contacts and recording the variables and 

time when a contact happens. 

STUDY POPULATION

 Using the capability of the Jack® software to scale 

operator’s anthropometry, the roof bolter model used three 

virtual-human models representing operators that conform to 

25th-, 55th-, and 92nd-percentile males. Virtual-human models 

were chosen to closely match human subject data that was 

collected for model verification and validation. Bartels et al. 

[in preparation] reports in detail the laboratory experiments 

and results that supplied parameters for the roof bolter model 

and virtual-human. 

EXPERIMENTAL DESIGN

 The roof bolting operation was broken down into 

specific tasks. Klishis [et. al. 1993] 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 motion scenarios for the computer 

simulations. 

 Using 3D computer simulations of virtualenvironments containing a machine and virtual-human models 

to generate and collect data, the study evaluated the number of 

contact that occurred between the operator and roof bolter 

boom-arm while drilling a hole and installing a bolt. Data 

were collected every simulated frame (0.03 s). The following 

information was recorded per simulated frame to a separate 

output file for each simulation scenario execution: 

• Simulated time (s), 

• The operator’s distance (cm) from the boom-arm to help 

determine operator’s location from the boom prior to 

performing tasks, 

• The boom-arm distance (cm) from a reference point to 

help decide boom-arm movement, 

• Distance calculations (cm) between eight viewing-area 

reference points and a reference point on the boom-arm 

when the operator sees the boom, and 

• A number marking sequential contacts between limbs and 

boom-arm was recorded for each simulated frame. 

 The computer model contains seven variables. The 

seam height and operator’s working posture consist of 114-, 

152-, 183-cm to accommodate operators’ work postures of the 

right knee, left knee, both knees, and standing. Humansubject motion tests [Bartels et al. 2001 and in preparation] 

provided data that defined models of virtual-humans whose 

percentile interval ranged from 24th to the 92nd. Operator’s 

final anthropometry conformed to 25th-, 55th-, and 92ndpercentile males. Also, collected from the human-subject 

motion tests data were the operator’s starting locations prior 

to performing the motion tests. From this information unique 

starting location values for each subject were made as a 

function of seam height and postures in that seam. The four 

boom-arm speeds (18-, 25-, 41-, 56-cm/s) were selected from 

MSHA’s roof bolting machine committee [MSHA 1994]. 

 A behavior motion is a series of human motions that 

mimics a specific action. Studies on workers job 

performance, machinery and work environment identified 

miners’ risk and hazard exposures while bolting [Klishis et 

al.1993a and 1993b]. Using this information, researchers 

identified specific risky behaviors for the drilling operation 

and bolt installation, Table 1. Researchers were interested in 

behaviors occurring only when the machine appendage had 

movement; subsequently, other risky behaviors associated 

with operating a roof bolter were not used. A decision 

algorithm was integrated [Ambrose in preparation] within the 

model that randomly selects what behavior to use for a 

simulation execution. Numerical parameters used in the 

algorithm came from the percent of operator actions that 

resulted in hazard exposure. These parameters were based on 

statistical observations of bolter operator actions associated 

with unsafe acts [Klishis et al. 1993a, p. 21]. 

 Operator’s chance of avoiding a contact was also 

evaluated to ensure a near-miss would not be considered a 

contact. This required knowledge of when the operator sees 

the moving boom-arm and the operator’s reaction time to get 

out-of-the-way of the boom. Bartels et al. [2001] and Bartels 

et al. [in preparation] studies provided data to determine “fast” 
and “slow” reaction times of operators as a function of seam 

height, work posture and operators’ anthropometrical data, 

Table 2. 

 Researchers originally used for the operator’s 

viewing area Humantech [1996] cone with an oval shape 

directrix to experiment with the virtual-human’s visiontracking capabilities. For acceptable viewing in reduced 

lighting conditions found in underground mines, Mine Safety 

and Health Administration’s minimum lighting requirements 

mandate illumination levels of 0.06 fL. The viewing area was 

modified from Bartels et al. [2001] test results on human 

subjects that determined the optimal viewing area and 

accurate field of vision for the virtual-human in underground 

mines, Figure 3. 

 During simulation executions, recorded data included 

time of contacts and when the boom-arm is in and out of the 

operator’s view. Subsequently, during data post-processing 

of the contact database, a contact-check algorithm compared 

time-pairings of when the boom-arm is in-view and out-ofview to determine suspected near-miss. The results provided 

researchers with information that identified contacts that the 

operator could avoid. 

Table 3 summarizes the factors that were used to 

generate 4,200 observations that comprised the research 

database. Noting that the database represents the equivalence 

of actual field observations of roof bolting work in 

underground coal mines for a period of 9.48 eight-hour shifts. 

When using the virtual-environment, simulations were 

executed on each percentile-operator while performing 1 of 28 

possible roof bolting scenarios. Twenty-eight scenarios 

consisted of combinations of the seam height, work posture, 

and boom-arm speed. 

MEASUREMENTS 

 

 Virtual-operators were given specific instructions as 

to how to perform the bolting tasks for each of the simulation 

scenarios. In each condition, the operator was required to 

work in the starting posture throughout the tasks. Three 

kneeling postures were used only in the lower seams. The 

standing posture was used in the highest seam. The standing 

postures for the two taller operators were flexing more toward 

the right-side and forward to accommodate the work space 

and proper right-hand alignment with the machine controls. 

This posturing was also observed during laboratory tests that 

collected human subjects’ motion data for validating the 

model. The random starting position between the operator and 

boom-arm were based on seam height and operator’s work 

posture according to results from human-subject laboratory 

tests. Each virtual-operator faced perpendicular to the long 

side of the boom-arm, and the machine controls were always 

to his right. The virtual-operator grabbed the tools (steel, bolt, 

or wrench) with the right hand, passed the tool off to the left 

hand and grabbed them with both hands to finish setting the 

tool in the drill-head and or hole in the mine ceiling (or mine 

roof as it is called.) Once the preparation for the drilling or 

bolt installation task was completed, the right hand was 

positioned on the appropriate lever that controlled the boomarm’s vertical movement. During the boom-arm movement, 

the left hand’s motion would be 1 of 4 possible risky 

behaviors as defined in Table 2. When the virtual-operator 

and machine interacted and resulted in touching, the event was 

defined as a contact. Researchers were interested in only 

contacts occurring when the machine appendage was moving. 

Furthermore, the model included random operators’ motions 

before and after the boom-arm appendage moved. These 

motions helped to improve motion accuracy through random 

positioning of the arm and hand just before or after appendage 

movement. Also, these motions made the overall model 

(Figure 4), when simulated, look visually realistic. 

 Three separate computer workstations were used in 

the data-gathering phase of the study. Using different 

workstations did not influence simulation outcomes and a 

copy of the simulation model ran perfectly on all computers. 

No changes or modifications to the model were necessary for 

any of the workstations used in data collection. The data 

collection phase took four months to complete. 

RESULTS

 The simulation would continue to completion even 

though it was possible for a single simulation to have multiple 

contacts and avoids. The presence of multiple incidents in a 

single simulation execution meant that data analysis could be 

done on either a data set containing avoids and all contacts 

(all-of-the contacts) or one-incident per simulation execution 

(one-run-one-contact). Consequently, researchers made two 

separate sets of data from the initial post-processed database. 

 Table 4 compares the two sets of data. This 

comparison showed that the source of incidents and the 

relationship of the variables associated with the incident did 

not differ significantly for the two. The one-run-one-contact 

data set was also considered by researchers more accurately 

representative of the real-world situation, as an operator 

would most likely stop or at least pause after being struck with 

a moving machine appendage. The set of data containing only 

one-run-one-contact also lent itself to other types of data 

analysis techniques such as survival. 

 Analysis also indicates that the reaction time of the 

operator did not significantly affect the outcome of the 

simulation, Table 5. The number of incidents for an operator 

with slow reactions differed from those for an operator with 

fast reactions by less than 1% in both data sets. 

 Ambrose et al. [in preparation] discusses in detail 

frequency and cross-tabulation, and survival analyses. All 

analysis was conducted using only the occurrences for the 

operator with slow reactions that included one-incident per 

simulation execution (one-run-one-hit). 

FREQUENCY & CROSS-TABULATION ANALYSES

 A table of incidents was compiled for variables used 

in the simulation in order to determine their effect on operator 

injuries such as contacts between the operator and machine. 

Frequency analysis is the simplest method to observe how 

different categories of values are distributed in the sample 

database. Customarily, if a data set includes any categorical 

data (e.g., seam heights, appendage speeds, work postures, 

etc.) then one of the first steps in the data analysis is to 

compute a frequency table for those variables. Crosstabulation is a combination of two (or more) frequency tables 

arranged such that each cell in the resulting table represents a 
unique combination of specific values of cross-tabulated 

variables. Thus cross-tabulation allows researchers to 

examine frequencies of observations that belong to specific 

categories on more than one variable. By examining these 

frequencies, researchers can identify relations between crosstabulated variables and provide information on trends in 

preparation to use other statistical approaches on the database. 

Summary

• In summary, the frequency-fixed variable analyses 

indicated that following: 

o Speed of the boom-arm have the greatest effect 

on the number of incidences with a sharp 

increase in contacts for faster boom speeds 58% 

for 41 and 56 cm/s. 

o 152-cm seam height had the most contacts, 60% 

of the total number of contacts, and 30% of the 

near misses. 

o Operators’ posture indicated that a posture on 

both knees was the worst with 33% of the 

incidents. 

o Anthropometry did not show a large difference 

for any one size individual; however, the 25thpercentile individual did have slightly more 

incidents than the other size individuals. 

• In summary, the frequency-random variable analyses 

indicated that following: 

o The palm is the closest body part to the moving 

boom-arm and was associated with 71% of all 

incidents. 

o The boom would be the closest moving machine 

part to the operator and accounted for 77% of all 

incidents. 

o Regardless of other variables, contact incidents 

were always greater when the bolter arm was 

moving up, were always greater on the palm, and 

were always greater for the boom part of the 

machine. 

• In summary, the frequency-conditional variable analyses 

indicated that following: 

o Considering operator behavior during drilling, 

49.9% of all contacts occurred for the hand-onboom behavior with the arm going up. 

o Considering operator behavior during drilling, 

45.6% of all contacts occurred for the hand-onboom behavior with contact being made with the 

palm part of the body. 

o Considering operator behavior during drilling, 

49.5% of all contacts occurred for the hand-onboom behavior with contact being made with the 

machine boom. 

• In summary, the cross-tabulation-fixed variable analyses 

indicated that following: 

o Regardless of boom speed, the 25th-percentile 

sized operators experienced more contacts than 

did other operator sizes. 

o Regardless of boom speed, the 92nd-percentile 

sized operators experienced fewer contacts than 

did other operator sizes. 

o Regardless of boom speed, the 152-cm seam 

experienced more contacts than did other seam 

heights. 

• In summary, the cross-tabulation-fixed-random variable 

analyses indicated that following: 

o Considering all contacts, 43.8% occurred for 

the152-cm seam with the arm going up. 

o Considering all contacts, 46.9% occurred for the 

152-cm seam with contact being made with the 

machine boom. 

SURVIVAL ANALYSIS

 Survival analysis is known as event history analysis 

or time-to-event analysis in the social sciences. Time-to-event 

data are increasingly common in health research, particularly 

in longitudinal or cohort studies where the onset of certain 

health outcomes is observed. Analyses of this type involve 

the amount of time (such as to the first contact) that a subject 

is at risk while under observation. Using analysis techniques 

on longitudinal event data will probably come up against two 

intractable problems: (1) censoring, the sample database 

contains some cases that do not experience an event (contact) 

and (2) many predictor variables (e.g., seam height, 

appendage speed, work postures) change in value over time. 

Survival methods are explicitly designed to deal with 

censoring and time-dependent covariates in a statistically 

correct way. 

A Cox regression model (time-to-event regression 

analysis) was conducted to evaluate the factors influencing the 

time to a worker being hit. The hypothesized time-to event 

regression model is given: h(t|z) = ho(t|z)exp(β1z1 + β2z2 + 

β3z3 + β4z4 + β5z5 + β6z6 + β7z7 + β8z8 + β9z9 + β10z10 + β11z11 + 

β12z12 + β13z13+ β14z + β15z15+ β16z16+ β17z17+ β18z18+ β19z19) 

where: βk = coefficients for variables used in the model, z1 = 

boom speed 25 cm/s, z2 = boom speed 41 cm/s, z3 = boom 

speed 56 cm/s, z4 = drilling behavior: hand on drill, z5 = 

drilling behavior: hand on boom, z6 = drilling behavior: hand 

on drill and boom, z7 = Boom moving upwards, z8 = 

Posture/Seam: right knee/144-cm, z9 = Posture/Seam: right 

knee/152-cm, z10 = Posture/Seam: left knee/144-cm, z11 = 

Posture/Seam: left knee/152-cm, z12 = Posture/Seam: both 

knees/144-cm, z13 = Posture/Seam: both knees/152-cm, z14 = 

bolting behavior: hand on bolt, z15 = bolting behavior: hand 

on boom, z16 = bolting behavior: hand on bolt and boom, z17 = 

Operator Location (cm), z18 = 25th-percentile worker, z19 = 

95th-percentile worker. 

 A forward selection procedure was used in model 

development. Treatment (z1) was forced into models beyond 

the first (univariate) step. In subsequent steps, variables were 

selected for inclusion on the basis of the Akaike Information 

Criterion (i.e., the model whose variable resulted in the lowest 

Akaike Information Criterion (AIC) was selected at each 

successive step of the model-building process). The modelbuilding process ceased when the lowest AIC for a step was 

greater than the lowest AIC obtained in the previous step. 

 A primary assumption of the time-to-event regression 

model was that the hazard proportions associated with model 

variable comparisons did not differ significantly with respect 

to time during the period of analysis. This assumption was 

checked for all variables at the univariate stage of the model-
building process. If the assumption did not appear tenable, the 

interaction between the variable and the natural logarithm of 

time was included in the model whenever that variable was 

entered into the regression models. A final check of the 

proportional hazards assumption was performed once the final 

model was determined. 

 Probabilities that risk ratios were significantly 

different from one were calculated using the Wald statistic for 

covariates with one degree of freedom. Probabilities for 

variables with multiple degrees of freedom were obtained by 

subtracting the log likelihood for the reduced model from the 

full model, and obtaining a chi-square with the appropriate 

degrees of freedom. Alpha levels were set at 0.05 for all 

cases. 

Summary

 One of the primary interests in performing this 

survival analysis was to determine the impact of boom speed 

on the chance of experiencing a contact in these simulations of 

roof bolter activities. Results indicate that boom-arm speed 

factor was the most influential in terms of affecting the chance 

of a contact occurring and the time at which such a contact 

might occur. Moreover, results of this analysis indicate that 

there is a significant jump in chance of being contacted at the 

two highest boom speeds – 41 and 56 cm/s – compared to the 

lower speeds examined – 18 and 25 cm/s. The former were 

associated with a marked, and perhaps unacceptable, increase 

in the amount of chance of being contacted, while the chance 

for the latter is much more modest. What cannot be evaluated 

from this data is the dangers associated with speeds in the 

intermediate range – between 25 and 41 cm/s. It is quite 

conceivable that there exists a point at which a sharp increase 

in chance of being contacted is evident. If such an inflection 

point is found, it could provide the basis for a design criterion 

that could provide a reasonable degree of protection to 

workers. All that can be said from the current analysis is that 

boom speeds above 41 cm/s seem to entail significant chance 

of being contacted, and should probably be avoided. Speeds 

25 cm/s or below result in a much lower exposure to being 

contacted, which represents an acceptable hazard level. 

 Covariates such as operator behaviors (placing their 

hands on the boom, drill, or bolt), postures and seam height 

combinations, boom direction, operator location and worker 

anthropometry also were significant factors in the time-toevent regression analysis. Workers were more likely to 

experience a contact when the boom was moving in an 

upward direction, especially early in the roof-bolting task. 

Kneeling postures generally resulted in increased chance of 

being contacted compared to standing in a 182-cm seam; 

however, kneeling on the left knee in a 144-cm seam much 

lower chance of making contact. Kneeling on the right knee 

in the same seam height entailed significantly higher chance 

of being contacted. Positioning of the workers further from 

the boom resulted in lower chance of being contacted; 

however, this could also impact the workers ability to perform 

the roof bolting task. Larger workers were 20% more likely to 

be contacted, while smaller workers were about 6% less likely 

to make contact with the boom. Drilling behaviors such as 

placing the hand on the boom or drill resulted in a higher risk, 

while bolting behaviors (occurring later in the bolting cycle) 

increased the time to and event occurring. 

It should be noted that this survival analysis was 

developed using a main effects model only. It is quite 

possible that the factors examined in this report have 

interactive effects (for instance, boom speed could have more 

of an impact on the chance of being contacted when certain 

postures are adopted). The large number of simulations, 

computational demands of running Cox regression models and 

of checking proportional hazard assumptions, and the large 

number of interactions (120) possible make analysis of these 

interactions extremely time consuming. 

CONCLUSION

 Researchers at PRL successfully developed a 

computer model that generates data by means of simulation 

while exercising the model with several variables associated 

with the machine and its operator, such as coal seam height, 

the operator’s anthropometry, work posture and choice of 

risky behavior, and machine’s appendage velocity. The 

resulting simulation database is comprised of 4,200 

observations. The database represented the equivalence of 

actual field observations of roof bolting and corresponds to a 

work period of 9.48 eight-hour shifts. 

 Data analysis was conducted using only the 

occurrences for the operator with slow reactions that included 

one-incident per simulation execution (one-run-one-contact). 

Researchers on this project believe the use of such 

simulations, treated with statistical procedures such as 

frequency, cross-tabulation, and survival analysis provide 

extremely useful tools to evaluate the hazards of tasks where it 

is not possible to perform experiments with human subjects. 

Results of this analysis could help in terms of making 

recommendations that reduce the likelihood that roof-bolter 

operators experience injuries due to contact with a moving 

boom. 

 Analysis indicates that the reaction time of the 

operator did not significantly affect the outcome of the 

simulation. The number of incidents for an operator with 

slow reactions differed from those for an operator with fast 

reactions by less than 1% in both data sets. 

 Significant results from frequency analyses showed 

that the speed of the boom-arm have the greatest effect on the 

number of incidents with a sharp increase in contacts for faster 

boom speeds 58% for 41 and 56 cm/s. The seam height of 

152-cm gave the most contacts, 60% of the total number of 

contacts, and 30% of the near misses. Operators’ posture 

indicated that a posture on both knees was the worst with 33% 

of the incidents. Anthropometry did not show a large 

difference for any one size individual; however, the 25thercentile individual did have slightly more incidents than the 

other size individuals. The palm is the closest body part to the 

moving boom-arm and was associated with 71% of all 

incidents. The boom would be the closest moving machine 

part to the operator and accounted for 77% of all incidents. 

Regardless of other variables, contact incidents were always 

greater when the bolter arm was moving up, were always 

greater on the palm, and were always greater for the boom 

part of the machine. Significant results from cross-tabulation 

analyses showed that regardless of boom speed, the 25th-
percentile sized operators experienced more contacts than did 

other operator sizes. Regardless of boom speed, the 152-cm 

seam experienced more contacts than did other seam heights. 

 Results of a survival analytic approach suggested that 

controlling the boom speed is the most important factor in 

determining the chance of an operator making contact. Also, 

boom speed was the most influential variable in terms of 

explaining the time to an event (contact) occurring. Increases 

in boom speed resulted in increased chance of a contact 

throughout the period of the simulation. The chance of being 

contacted at the higher speeds – 41 and 65 cm/ is generally 5 

to 8 times greater than at 25 cm/s, and 8 to 25 times greater 

than at 18 cm/s. Based on the data collected in this simulation 

analysis, the boom-am speed greater than 41 cm/s result is a 

substantial increase in the chance of making contacted with 

the roof bolter. Also results showed that at speeds less than or 

equal to 25 cm/s resulted in a more modest chance of being 

contacted, which represents an acceptable hazard level. 

 Other factors have also been shown to have a 

significant influence on the chance of being contacted, as 

detailed in the report. Researchers on this study do plan to 

continue examination of the data and to uncover significant 

interactions should they exist. While the current research 

cannot speak to the dangers associated with speeds between 

25 and 41 cm/s, there could be a particular boom speed in this 

range at which a significant inflection point in the relative 

contact estimates occurs. Based on the results of this data 

analysis, this is an issue that needs to be addressed by future 

research. 

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8. MSHA Staff, [1994]. Coal Mine Safety and Health RoofBolting-Machine Committee: Report of findings. MSHA 

- Roof Bolting Safety, Coal Mine Safety and Health, 

Division of Safety, Arlington, VA., pp. 28. 

9. Klishis, M. J., Althous, R. C., Layne, L. A., Lies, G. M., 

[1993a]. “A manual for improving safety in roof 

bolting,” Mining Extension Service, West Virginia 

University. August, pp. 143. 

10. Klishis, M. J., et. al., [1993b]. “Coal mine injury 

analysis: a model for reduction through training,” Mining 

Extension Service, West Virginia University. August, pp. 

220. 

11. Turin, F. C., [1995]. RI 9568 Human factors analysis of 

roof bolting hazards in underground coal mines, Bureau 

of Mines publication, September, pp. 22. 

12. Volberg O. and Ambrose, D.H. [2002]. Motion editing 

and reuse techniques and their role in studying events 

between a machine and operator. San Diego CA: 

Presentation and Proceedings of the Society of Computer 

Simulation International (SCS) Advance Simulation 

Technologies Conference 2002, p 181-186, ISB:1-56555-

248-2 
Table 1- Behavior list for the drilling the hole and installing a bolt 

Operation Behavior Description 

Hand off the boom arm and hand off the drill steel bit 

Hand on the drill steel bit 

Hand on the boom arm Drill 

Hand on the boom arm and then hand on the drill steel 

bit 

Hand off the boom arm and hand off the bolt or wrench 

Hand on the bolt or wrench 

Hand on the boom arm 

Bolt 

Hand on the boom arm and then on the bolt or wrench 

Table 2 - Reaction times of operators used in the roof bolter model 

seam height 

cm (in) 114 (45) 152 (60) 183 (72) 

work posture right knee left knee both knees right knee left knee both knees standing 

reaction time 

(ms) fast slow fast slow fast slow fast slow fast slow fast slow fast slow 

25th 436 736 356 656 376 676 370 670 376 676 356 656 374 674 

55th 401 701 366 666 397 697 333 633 392 692 353 653 376 676 

operator 

percentile 

92nd 330 630 384 684 349 649 403 703 424 724 375 675 388 688 

Table 3 - Factors that determined the number of observations per seam height 

factors 

observation 

totals 

seam 

height 

cm operator boom speeds work postures 

simulation 

executions 

1,800 114 3 4 3 50 

1,800 152 3 4 3 50 

600 183 3 4 1 50 

overall 4,200 
Table 4 - Comparison of one-contact per execution versus all contacts 

ONE-RUN-ONE-CONTACT ALL Contacts 

variable respons

e 

near misses contacts near misses contacts 

SEAM HEIGHT slow 114>152>183 152>114>183 114>152>183 152>114>183 

(cm) fast 114>152>183 152>183>114 114>152>183 152>183>114 

SUBJECT SIZE slow 55>25>92 25>55>92 25>55>92 25>55>92 

(anthropometeric percentile) fast 55>25>92 25>55>92 25>55>92 25>55>92 

POSTURE slow L>R>B>S B>R>L>S L>R>B>S B>R>L>S 

L-left,R-right,B-both,S-stand fast L>R>B>S B>R>L>S L>R>B>S B>R>L>S 

SPEED slow 25>56>41>18 41>56>25>18 25>41>18>56 41>56>18>25 

(cm/sec) fast 25>41>56>18 41>56>25>18 25>41>56>18 41>56>18>25 

DRILLING BEHAVIOR slow B>DB>N>D N>B>BD>D B>DB>N>D B>DB>N>D 

Hand on B-boom,D-drill,N-none fast B>DB>N>D N>B>BD>D B>DB>N>D B>DB>N>D 

BOLTING BEHAVIOR slow N>B>BT>BBT N>B>BBT>BT N>B>BBT>BT B>BBT>N>BT 

Hand on B-boom,BT-bolt,N-none fast N>B>BT>BBT B>BBT>N>BT N>B>BT>BBT B>BBT>N>BT 

BOOM DIRECTION slow U>D U>D U>D U>D 

D-down,U-up fast U>D U>D D>U U>D 

BODY PART slow P>L>A>H P>A>H>L P>L>A>H P>A>H>L 

A-arm,L-leg,H-head,P-palm fast P>L>A>H P>A>H>L P>L>A>H P>A>H>L 

SIDE slow L>R>H L>H>R L>R>H L>H>R 

L-left,R-right,H-head fast L>R>H L>H>R L>R>H L>H>R 

MACHINE PART slow B>D B>D B>D B>D 

B-boom,D-drill fast B>D B>D B>D B>D 

Table 5 - Results of slow response versus fast for simulation executions 

Slow operator, all incidents Slow operator, one incident pre simulation 

Frequency Percent Cumulative Frequency Percent Cumulative 

avoid 1657 27.62 27.62 735 17.50 17.50 

contact 2320 38.67 66.28 1442 34.33 51.83 

none 2023 33.72 100.00 2023 48.17 100.00 

Total 6000 100.00 4200 100.00 

Fast operator, all incidents Fast operator, one incident pre simulation 

Frequency Percent Cumulative Frequency Percent Cumulative 

avoid 1661 27.68 27.68 770 18.33 18.33 

contact 2316 38.60 66.28 1407 33.50 51.83 

none 2023 33.72 100.00 2023 48.17 100.00 

Total 6000 100.00 4200 100.00 
Table 6 - Final Model Detail 

Variable df Beta SE p-value RR 95% CI 

BOOM SPEED (cm/s) 

25 

41 

56 

25 *ln(time) 

41 *ln(time) 

56 *ln(time) 

6 

-.970 

-.320 

1.276 

.581 

.911 

.509 

.445 

.551 

.704 

.168 

.225 

.299 

.029 

.561 

.070 

.001 

.000 

.089 

.379 

.726 

3.582 

1.788 

2.487 

1.664 

.158-.907 

.247-2.136 

.901-14.237 

1.287-2.483 

1.599-3.869 

.925-2.993 

DRILL BEHAVIOR 

Hand on Drill 

Hand on Boom 

Hand on Both 

Hand on Drill*ln(time) 

Hand on Boom*ln(time) 

Hand on Both*ln(time) 

6 

7.999 

9.214 

9.372 

-2.359 

-2.618 

-2.637 

.807 

.587 

.741 

.291 

.197 

.275 

.000 

.000 

.000 

.000 

.000 

.000 

2977.250 

10040.63 

11754.22 

.095 

.073 

.072 

612.769-14465.512 

3174.801-31754.527 

2749.898-50242.489 

.053-.167 

.050-.107 

.042-.123 

BOOM UP 

Boom Up 

 Boom Up*ln(time)

2 5.979 

-1.693 

.409 

.155 

.000 

.000 

394.968 

.184 

177.351-879.610 

.136-.249 

POSTURE/SEAM (cm) 

 Right 114 

 Right 152 

 Left 114 

 Left 152 

 Both 114 

 Both 152 

 Right 114*ln(time) 

 Right 152*ln(time) 

 Left 114*ln(time) 

 Left 152*ln(time) 

 Both 114*ln(time) 

 Both 152*ln(time) 

12 

-5.649 

-2.052 

1.486 

-2.309 

-2.852 

-1.893 

2.534 

.921 

-.789 

1.077 

1.115 

.888 

.746 

.567 

1.659 

.565 

1.025 

.585 

.304 

.226 

.744 

.218 

.434 

.217 

.000 

.000 

.370 

.000 

.005 

.001 

.000 

.000 

.289 

.000 

.010 

.000 

.004 

.129 

4.420 

.099 

.058 

.151 

12.599 

2.513 

.455 

2.936 

3.051 

2.430 

.001-.015 

.042-.390 

.171-114.110 

.033-.301 

.008-.430 

.048-.474 

6.947-22.849 

1.614-3.911 

.106-3.911 

1.916-4.499 

1.304-7.136 

1.590-3.715 

BOLTING BEHAVIOR 

Hand on Bolt 

Hand on Boom 

Hand on Both 

3 -.167 

-.465 

-.544 

.087 

.072 

.083 

.055 

.000 

.000 

.846 

.628 

.580 

.713-1.004 

.545-.724 

.494-.682 

OPERATOR LOCATION 

 Operator Location 1 -.028 .009 .002 .972 .955-.989 

ANTHOPOMETRY 

25th -percentile 

92nd -percentile 

2 -.060 

.180 

.078 

.072 

.438 

.012 

.942 

1.198 

.808-1.096 

1.04-1.380 
Figure 2. Full-scale wooden roof bolter boom arm setup for data collection;

the mannequin illustrates motion sensor locations on human subjects

Figure 1. Actual roof bolting machine
Figure 3. Angular data of the original and modified cones for the virtual-operator

Figure 4. - A view from the display monitor of the roof bolter model