Analyzing Factors Influencing Struck-By Accidents 

of a Moving Mining Machine by using Motion 

Capture and DHM Simulations 

John R. Bartels, Dean H. Ambrose and Sean Gallagher 

National Institute for Occupational Safety and Health (NIOSH) Pittsburgh Research Lab 

ABSTRACT 

Remote operation of continuous mining machines has 

enhanced the health and safety of underground miners in many 

respects; however, numerous fatal and non-fatal continuous 

miner struck-by accidents have occurred. In an effort to 

prevent these injuries, NIOSH researchers at Pittsburgh 

Research Laboratory studied the workplace relationships 

between continuous miner operators and various tramming 

tasks of the equipment using motion capture data, operator 

response times, and field of view data to evaluate the factors 

influencing operator-machine struck-by events (contact with a 

solid object) in a virtual mine environment. It is not feasible 

(nor ethical) to use human subjects to directly evaluate factors 

that precipitate such injuries. However, use of motion analysis 

data and digital human models can facilitate analysis of 

struck-by accident risk by allowing investigators to manipulate 

factors that influence injury. Factors included in this study 

included machine speed, direction of escape, the direction the 

operator was facing relative to the machine, work posture, 

distance from machine, and operator anthropometry. Close 

proximity to the machine, high machine tramming speeds, a 

right-facing orientation and operator positioning near the tail 

all resulted in high risk of being struck. 

INTRODUCTION 

The use of digital human models (DHMs) to analyze 

workplace hazards and improved workplace design are 

becoming more prevalent among human factors and 

ergonomics professionals (Badler et al., 2002; Brown, 1999; 

Chaffin, 2002; Colombo and Cugini, 2005; Ferguson and 

Marras, 2005; Zhang, X. and Chaffin, D.B., 1996). These 

DHMs can be driven by human motion analysis techniques, 

providing a means by which human-machine interactions can 

be analyzed. One particular advantage of DHMs is that they 

can be used to evaluate the risks associated with hazardous 

tasks (or interaction with potentially hazardous machinery) 

without risk of serious injury to a person. An example of this 

type of situation is one where struck-by or caught-between 

accidents might occur when a worker is operating tramming 

(moving) machinery. 

The Mine Safety and Health Administration (MSHA) reports 

that both fatal and non-fatal remote-control continuous miner 

(figure 1) accidents from 1999-2006 are averaging 254 per 

year during routine mining activities with the majority of 

accident victims working within the turning radius of mov ing 

continuous miners. 

Figure 1. Continuous miner operator by tail. 

The mining industry uses an educational 

aid called “red zones are no zones” (MSHA 2006) to help 

operators of remote control vehicles, such as continuous 

miners, to understand which areas around the machine to 

avoid (figure 2). 

Figure 2. Red Zone around a continuous miner. 

 However, fatalities and injuries still contin ue 

as results of contact with moving machines. 

MSHA is interested in reducing injuries and fatalities 

attributed to the operation of continuous mining machin es 

(Clark et al., 1998, Colley et al., 2006). As recently as 

February 2006, MSHA posted on its website informatio n 

regarding protection of continuous miner operators by usin g 
proximity warning devices to help recognize the red-zone 

strategy. 

In an effort to better understand the influence of these issues 

on the risk of injury, it was decided that a DHM using motion 

capture of operator movements would be a useful 

methodology. The purpose of this investigation wa s to analyze 

factors influencing struck-by accidents during tramming of a 

continuous mining machine using DHM simulations driven by 

actual human motion analysis using a variety of human 

subjects, postures, facing orientations, environmental 

constraints, and mining machine operational characteri stics. 

METHOD 

Motion Analysis Data 

Ten male subjects aged 32-59 years were recruited from local 

mines to perform realistic movements in a laboratory setting 

(figure3) that mimic getting out of the way of a moving 

continuous mining machine.

 Figure 3. Laboratory test setup; a subject finishes a trial run. 

 Experienced operators were used 

because they are accustomed to the unique movements a nd 

postures required in th e mining. The subje ct’s motions were 

measured by recording them using Motion Analysis 

Corporation’s (MAC) motion tracking and capturing system. 

MAC is an optical system that uses infrared sources a nd 

cameras to track reflective markers placed on a test subject. 

From measured infrared reflective characteristics, each m arker 

position and orientation is computed by EVaRT, specialized 

MAC software that converts and records this information for 

future analysis. 

Human motion d ata were obtained for various operator work 

postures and escape paths (directions) typically used when 

operators are tramming mining equipment such as continuous 

miners. The capture session was conducted for a posture of 

kneeling on both knees, a squatting posture and a standing 

posture that represented seam heights of 36-, 48- and 60-inch, 

respectfully. At a given signal, a test subject moved from a 

starting location (see figure 4, solid circle) as quickly as 

possible along a given path (numbered 1 thru 8) labeled on a 

carpet in the test posture.

Figure 4 . Test area layout showing path number and 

orientation in degrees. 

 The sequencing of path directio n 

was randomized for each subject. Prior to each path 

movement, the subject’s orientation was either facing the 

signal source (a researcher controlled light) or with th e signal 

source to the subjects left or right side. The signal source a lso 

emitted an infrared signature allowing it to be captured with 

the motion data. When a light signal source at ‘S’ was turned 

on it cued the test subject to move. The subject stopped 

moving once he crossed the outer portions of the dash-circle 

(figure 4) on the carpet (figure 3). Each subject complete d 

216 trial runs to accommodate the variables of operator’s wor k 

posture, facing orientation, and escape path direction. 

Development of Human-machine Model and Simulation 

Data Collection 

A human-machine model ( figure 5) was developed to measure 

parameters that would be used to predict struck-by events 

when the operator moves out of the way of the moving 

machine. 

Figure 5. Model contains a continuous miner model, DHM as operator and 

DHM’s peripheral vision views 

The parameters included: (1) the time when the 

machine first begins to move, (2) the time when the ope rator 

first begins to move, (3) the time when the operator is struc kby an object, (4) name of the object that struck the operator 

(pan, boom drum), and (5) the operator’s distance from the 

machinery when struck-by an object. Struck-by means a 

collision occurs between the operator and machine or the mi ne 

wall. The data collection in this process uses captured 
motions, simulated scripts and collision detection by usi ng 

JackTM modeling and simulation software. 

The human-machine model was setup to generate and collect 

data during simulations from two different work locations: 

cutting drum or conveyor tail (see figure 1). From these wo rk 

locations, simulation scripts were programmed to rotate the 

machine (4.77, 9.55 and 19.1 deg/s) until it came in contact 

with the virtual mine wall. In addition, during the machine’s 

simulated rotation, the captured motions of an operator were 

integrated into the virtual environment. This showed the 

operators actual movements, one of the eight escape paths 

from the machine, starting from a specific distance at one o f 

the work locations. 

The operators’ movements were constrained to a digital 

human model by using captured motion data of test subjec ts 

discussed in the previous section. The operator was placed at 

1, 2, and 3 foot intervals from the conveyor tail or cutting 

drum. Captured motions of the experienced equipment 

operators were replayed in Jack software in the various 

combinations of kneeling, squatting and standing work 

postures, facing orientations and escape path directions. Only 

escape paths 4, 5, 6 and 7 were used because they offered at 

least one direction that cleared the turning radius of the 

machine at each work location. In order to present a real istic 

operator response to the moving machine, researchers 

programmed the operator’s movement to delay the star t of 

movement according to reaction tim es (Table1) reported by 

Drowatzky (1981) for different age groups that ranged from 

0.19 to 0.24 seconds. 

Table 1 – Test subject information 

Subject Age, 

yrs 

Weight 

lbs 

Height, 

ft-in 

Experience, yrs Reaction 

time, 

Mining Machine sec

1 53 215 5-8 20 10 0.22 

2 37 242 5-10 10 10 0.20 

3 56 190 5-11 27 27 0.23 

4 51 235 5-10 20 17 0.22 

5 51 260 6-0 33 20 0.22

6 59 165 5-7 34 15 0.24

7 47 200 6-2 25 10 0.21

8 32 170 5-9 2 0.25 0.19 

9 33 220 6-1 4 1 0.19

10 49 195 6-1 7 1 0.21 

Also during the simulation, right and 

left peripheral vision views on the DHM (figure 5) and 

collision detection helped investigators to track whether the 

operator could detect movement in the machine that could 

stimulate a reaction. 

The initial simulation database contained 19,440 cases that 

were collected from ten virtual subjects where a number of 

cases involved instantaneous contact information such as 

collisions occurring at time zero. This happened in the 

simulation if the operator was already in contact with the 

machine before it moved or at the instant it began to move. 

Therefore, the resulting database for analysis comprised of 

14,308 cases depicting 1,296 possible test scenarios involving 

machine speeds and direction of rotations, work postures and 

facing orientations, work locations and distances from the 

machine, and escape paths. 

Variables 

Several independent variables were investigated including the 

machine speed, operator’s distance away from the machine, 

operator’s work posture and facing orientation, operator’s 

work location and escape path direction. Dependent variables 

included time of a collision event, part of the body contacted 

and part of the machine contacted. 

Survival Analysis Model 

A Cox regression model was used to examine the influence of 

the independent variables on the time to contact. Cases where 

no contact was made were treated as censored observations. 

The main effects and all two way interactions were analyzed 

using a likelihood ratio stepwise technique. Proportional 

hazard checks were performed at all stages of the analysis. 

Type I error was set at 0.05. 

Table 2 - 

summary 

Frequency and cross-tabulation struck-by 

Work location 

% struck-by 

Tail 

76.8 

Drum 

67 

Work Location 

– Distance1 

% struck-by 

D-11 

87.1 

T-21 

87.1 

D-21 

86.1 

T-11 

82.8 

T-31 

71.7 

D-31 

66.1 

Speed, deg/s 

% struck-by 

19.1 

89.9 

9.55 

73 

4.77 

71.7 

Speedrotation2 

% struck-by 

19 

CCW 

91.1 

19 

CW 

88.9 

9 

CCW 

81.7 

9 

CW 

64.5 

4 

CCW 

59.6 

4 

CW 

50.1 

Time3

 (s) 

Cumulative4 

% 

0.1 

24.6 

0.5 

69.9 

1 

85.4 

2 

96.4 

3 

97.5 

4 

99.7 

Body part 

% struck-by of 

total5 

Arms 

61.4 

Head 

23.8 

Legs 

11.4 

Feet 

3.4 

1

Distance from work locations (Tail T-1,3 and Drum d-1,3) in feet 2

 CW = clockwise; CCW = counterclockwise 

3

Time intervals during machine rotation 

4

Cumulative percent of total struck-by events 

5

Percent of total struck-by events 
RESULTS 

Frequency and Cross-tabulation 

Frequency and cross-tabulation analyses considered 14,308 

simulations. Of the simulations examined, 10,254 exhibited 

struck-by events between the operator and the continuous 

miner equipment. A table of struck-by events for some of the 

factors was compiled in Table 2, for example, the operator 

was struck 76.8% of the time when working at the tail 

location. The table also includes cross-tabulations, for 

instance, the operator was struck 91.1% of the time when the 

machine moved counterclockwise at a speed of 19.1 deg/s. 

The cumulative percent of total struck-by events per time 

while the machine moved and percent of the total struck-by 

events of when a body part on the operator was most 

frequently contacted are other information in the table. The 

two major factors that cross tabulations showed to have an 

effect on the operator’s ability to avoid being struck by the 

machine were the operators distance from the machine and the 

speed of the machine. The effect of the operators distance can 

be seen in figure 6.

0% 

20% 

40% 

60% 

80% 

100% 

1ft from 

drum 

1ft from 

tail 

2ft from 

tail 

2ft from 

drum 

3ft from 

tail 

3ft from 

drum 

Figure 6. Percent struck vs. distance from machine. 

 The differences in incident rates between a 

distance of 1-ft and 2-ft is small but at 3-ft the incident rate is 

significantly reduced. Figure 7 shows the effect of machine 

speed on incident rates. 

Figure 7. Percent struck vs. machine speed. 

0% 20% 40% 60% 80% 100% 

19.10 deg/sec CCW 

19.10 deg/sec CW 

9.55 deg/sec CCW 

9.55 deg/sec CW 

4.77 deg/sec CCW 

4.77 deg/sec CW 

There is an almost linear reduction in 

rates as machine speed is reduced. There also were a greater 

incidents of struck by when the machine rotates counter 

clockwise with the operator located in the tail position which 

has a greater linear velocity than the drum at the same 

rotational speed. These results are not surprising but do 

indicate that recommendations on speed and operator distance 

from the 

machine could reduce operator injuries. The interaction of 

parameters was further explored in the survival analysis. The 

interaction of the parameters was quite complex with the other 

independent variables having an effect on the incidences of 

operators being struck by the machine. The difference in 

incidents due to the escape path of the operator is shown in 

figure 8.

Figure 8. Percent struck vs. escape direction. 

0% 

20% 

40% 

60% 

80% 

100% 

180° 45° 135° 90° 

 Operator posture and facing direction also affected 

the frequency of struck-by incidents, but appeared to have the 

greatest affect in combination with each other. In order to 

explain these interactions, a time based survival analysis was 

used to determine the significance of the variable interactions. 

Additional data was collected from the simulations such as the 

parts of the body most frequently struck (figure 9) and the 

machine parts most frequently contacted. 

Figure 9. Body part struck. 

0 50 1000 150 200 2500 3000 

right foot 

left lower leg 

left foot 

right lower leg 

left upper leg 

right upper leg 

right lower arm 

left lower arm 

left upper arm 

right upper arm 

head 

Upper parts of the 

body tend to be struck most often due to the shape of mining 

machine particularly when at the tail which can only contact 

the operator’s upper body. This would also explain why 

stature did not have a significant influence on incidents. These 

factors will be used in the development of work place 

interventions. 

Relative Risk 

Based on the results of the Cox regression analysis, a model 

was developed that incorporated the main effects and 

interactions of the independent variables on the time to 

contact. All the main effects were found to be significant and 
12 of 15 two-way interactions were significant, resulting in a 

complex risk model. In addition, the proportional hazards 

assumption was rejected in almost all cases, save for the 

machine speed variable, indicating that the hazards associated 

with most of these variables were changing with time. The 

proportional hazards assumption is the assumption that 

hazards associated with a factor are remaining relatively 

constant with time. For example, if a given factor halves your 

hazard at time 0, it also halves your hazard at time 1, or time 

0.5, or at any other time during the measurement period. 

Due to model complexity, analyses of relative risk were 

calculated while the machine was in motion at 0.1 sec, 1 sec 

and 3 sec. The times were chosen based on the first time 

recorded, the time when 98% of the cumulative incidents have 

occurred and a time in the middle. Using only the five lowest 

and the highest five relative risks calculated at these times in 

the simulation, Table 3, 4 and 5 were compiled and included 

for each time period, showing distance, speed, work posture, 

facing orientation, work location, escape direction and relative 

risk. 

Table 3 – Lowest and highest relative risk at time 0.1 seconds. 

ft Speed 

deg/s Posture Facing Location Dir deg 

Relative 

risk 

3 4.77 standing front Drum 45 0.0017 

3 4.77 standing front Drum 90 0.0023 

Lowest 

3 4.77 standing front Drum 135 0.0023 

3 4.77 standing front Drum 180 0.0025 

3 

1 

9.75 

19.1 

standing 

standing 

front 

right 

Drum 

Tail 

45 

180 

0.005 

45.79 

1 19.1 standing right Tail 135 42.62 

Highest 

1 19.1 standing right Tail 90 41.55 

1 19.1 standing left Tail 180 32.89 

1 9.75 squatting right Tail 180 30.64 

Table 4–Lowest and highest relative risk: at time 1 second. 

ft Speed 

deg/s Posture Facing Location Dir 

deg 

Relative 

risk 

2 4.77 squatting front Drum 90 0.039 

2 4.77 squatting left Drum 90 0.0407 

Lowest 

2 4.77 squatting right Drum 90 0.0423 

1 4.77 squatting front Drum 90 0.0447 

1 

3 

4.77 

9.75 

squatting 

squatting 

left 

right 

Drum 

Tail 

90 

180 

0.0467 

42.6 

3 9.75 squatting left Tail 180 40.97 

Highest

3 9.75 squatting front Tail 180 39.21 

1 9.75 squatting right Tail 180 30.5 

1 9.75 squatting left Tail 180 29.34 

Table 5 – Lowest and highest relative risk: at time 3 seconds. 

ft Speed, 

deg/s Posture Facing Location Dir deg 

Relative 

risk 

2 4.77 squatting right Drum 90 0.0179 

2 4.77 squatting left Drum 90 0.0195 

Lowest 

1 4.77 kneeling right Tail 45 0.021 

1 4.77 kneeling right Tail 135 0.021 

1 

3 

4.77 

19.1 

squatting 

squatting 

right 

front 

Drum 

Tail 

90 

180 

0.0212 

292.87 

3 9.75 squatting front Tail 180 291.18 

Highest 

3 19.1 squatting left Tail 180 267.07 

3 9.75 squatting left Tail 180 265.53 

3 19.1 squatting right Tail 180 245.81 

The coefficients in this model helped to evaluate the 

relative risk of experiencing a struck-by event at different 

points during the machine movement and showed the degree 

of influence for each variable in the model while controlling 

the effects of all other covariates. 

DISCUSSION 

Machine speed was the most influential variable in terms of 

explaining the struck-by event occurring. Increases in machine 

speed resulted in an increased chance of being struck-by and 

the increased risk associated with higher speeds was constant 

throughout the times investigated in the study. In general, 

compared to the 4.77 deg/s condition (referent condition), the 

9.75 deg/s speed increased risk threefold and the 19.1 deg/s 

condition increased risk by 8 times the referent value. The 

distance from the machine at the start of the test (0.1 sec), also 

had a significant influence. The relative risk of being struckby the machine while working within 1-ft of the machine was 

the greatest at the beginning of the simulation. 

Throughout the simulation the operator in a squatting work 

posture has a high potential threat of being struck-by. In 

general, this posture was associated with an increase in 

relative risk that was 2.5 times the standing (referent) 

condition. Indicating that a mid and low seam height may 

pose a greater probability of struck by accidents depending on 

posture. It seems clear that the difficulty and slow speed 

associated with this posture 

increased the time it took to escape. On the other hand, the 

kneeling posture was found to be the lowest in terms of 

relative risk of all the postures. This posture may have 

positioned the body in a manner relative to the machine to 

avoid being struck by the machine, and still allowed for a 

relatively quick escape via crawling. At the beginning of the 

simulation, front-facing orientation revealed a higher relative 

risk, which appeared to be due to the propensity of subjects to 

turn around before moving away from the machine. The extra 

time associated with this maneuver may result in subjects 

being unable to successfully avoid contact with the continuous 

miner. At later phases of the simulation, right-facing 

orientation becomes a lower relative risk. 

The tail location is the highest relative risk compared to the 

drum location throughout the simulations. This would be 

explained in part by the tail being further from the axis of 

rotation than the drum, meaning it has a higher linear velocity. 
Due to the nature of the simulation, data was not collected 

with an operator positioned at the center point of the machine. 

An operator standing in that position would have no struck by 

incidents in the model. Unfortunately, the rotation of the 

machine around it’s center point makes many think it would 

not lead to a risk of struck by accidents when the operator was 

so positioned. However, tramming while positioned within 

the turning radius of the continuous miner can be fatal 

according to MSHA data (Dransite J and Huntley C., 2005). 

The center of the machine is no way a safe location for the 

operator to be positioned, because of distance from the 

machine and pinch points (MSHA 2006) such as if the 

machine slips toward the operator while tramming. The data 

presented here suggest that greater risk is present when the 

operator is positioned by the tailpiece compared to the drum 

(in terms of struck by accidents). However, positioning near 

the drum may have other consequences associated with it that 

may need to be considered. Relative risk of being struck-by 

the machine using the escape path direction 180° (path number 

7) was the greatest throughout the simulation. It is clear that 

successful escape is less likely when moving parallel to the 

machine than when the escape vector has a component of 

motion that is away from the machine. 

In summary, the data obtained in this study revealed a 

complex interaction of factors that affect the risk of struck by 

accidents when tramming continuous miners in an 

underground mining environment. However, the increased 

understanding of these relationships should ultimately result in 

recommendations that reduce this risk of these potentially fatal 

accidents in continuous miner operators. The results can also 

be used to set parameters for new proximity warning systems 

being developed, Ruff (2007), for mining equipment. 

Acknowledgments 

The authors gratefully acknowledge the following colleagues 

at the NIOSH Pittsburgh Research Laboratory: Joseph P. 

DuCarme, Mary Ellen Nelson, Albert H. Cook, and 

E. William Rossi who provided technical support in operating 

the motion tracking system and data collection of test subjects 

and to August Kwitowski for automating portions of the postprocessing steps to achieve the resulting simulation database. 

REFERENCES 

Badler, N.I., Erignac, C.A., and Liu, Y., 2002, Virtual humans 

for validating maintenance procedures, Communications 

of the ACM, Vol. 45, Issue 7, July 2002, ISSN:0001­

0782, pp. 56-63 

Brown, A.S., 1999, Role Models: Virtual People Take On the 

Job of TestingComplex Designs. Mechanical 

Engineering, 44 pp. 

Chaffin, D.B., 2002, On simulating human research motions 

for ergonomics analyses, Human Factors and Ergonomics 

in Manufacturing, Vol. 12, Issue 3, pp. 235-247. 

Clark G, Warnock B, Wease D, Dransite J [1998]. Remotely 

Controlled Mining Machinery Study, MSHA Approval 

and Certification Center, August 3, 1998, p. 45. 

Colley W, Hill J, Holubeck R, Malin B, Warnock B [2006]. 

Remote Control Continuous Mining Machine Crushing 

Accident Data Study, MSHA Approval and Certification 

Center, May 11, 2006, p. 67. 

Colombo, G. and Cugini, U., 2005, Virtual humans and 

protypes to evaluate ergonomics and safety, Journal of 

Engineering Design, Vol. 16, Number 2, April 2005, pp. 

195-203. 

Dransite J and Huntley C [2005] Remote Control Fatal 

Accident Analysis Report of Victims Physical Location 

with Respect to the Mining Machine, Dept. of Labor, 

Mine Safety and Health Administration, Technical 

Support Approval & Certification Center, Dec 15, 2005, 

p. 11 

Drowatzky JN [1981]. Motor Learning, Principles and 

Practices, Second Edition, Burgess Publishing Company, 

Minneapolis, MN, p 235. 

Ferguson, S.A., and Marras, W.S., 2005, Workplace design 

guidelines for asymptomatic vs. low-back-injured 

workers, Applied Ergonomics 36 (2005), pp. 85-95. 

MSHA [2006] Remote Control Hazard Awareness, Dept. of 

Labor, Mine Safety and Health Administration, web site 

address: http://mshawebapps.msha.gov/webcasts/ 

coal2005/ mshawebcast20060109_ files/frame.htm# 

slide0066.htm 

Ruff, T., 2007, Recommendations for Evaluating & 

Implementing Proximity Warning Systems on Surface 

Mining Equipment, Department of Health and Human 

Services, CDC RI 9672 pp 1-65. 

Zhang, X and Chaffin, D.B., 1996, Task effects on threedimensional dynamic postures during seated reaching 

movements: An analysis method and illustration, 

Proceedings of the 1996 40th Annual Meeting of Human 

Factors and Ergonomics Society. Philadelphia, PA, Part 1, 

Vol. 1, pp. 594-598.