Evaluation of an Advanced Proximity Detection 

System for Continuous Mining Machines
 

1

Christopher Jobes, 2Jacob Carr 

1,2Research Engineer, 

and 3Joseph DuCarme 

3

Research Scientist 

The National Institute for Occupational Safety and Health 

626 Cochran’s Mill Road, Pittsburgh, PA 15236 

E-mail: chris.jobes@cdc.hhs.gov, jacob.carr@cdc.hhs.gov, 

joseph.ducarme@cdc.hhs.gov 

Abstract 

Researchers at the National Institute for Occupational Safety and Health 

(NIOSH) are advancing the emerging technology of electromagnetic 

proximity detection, which provides a promising means of protecting workers 

around any machinery that presents striking, pinning or entanglement hazards. 

This technology is particularly applicable to mobile underground mining 

equipment such as remote-control continuous mining machines, which offer 

perhaps the most difficult safety challenges in the mining industry. The 

operators of these machines must maintain constant vigilance to keep 

themselves and others near the machine safe. Tragically, striking and pinning 

accidents involving continuous mining machines occur every year causing 

severe injuries and claiming lives. Proximity detection technology has been 

effectively implemented for other types of equipment in underground and 

surface mining as well as in other industries. However, applying this 

technology to remote-control continuous mining machines presents uniquely 

difficult challenges. Due to visibility and space limitations, the machine 

operator must routinely work in very close proximity to the machine. In order 

to protect miners without preventing them from doing their jobs or causing 

nuisance alarms, NIOSH is now developing intelligent proximity detection 

technology. This technology accurately determines worker position relative to 

the machine and responds by intelligently issuing situation-specific alarms to 

warn the operator or disabling situation-specific machine functions to protect 

the operator from machine movements that could result in injury. In this paper, 

the authors review existing proximity warning technologies, describe ongoing 

NIOSH research on an intelligent proximity warning system, and summarize 
current test results. The NIOSH-developed intelligent system has the potential 

to have a significant impact on the mining industry by greatly advancing the 

state-of-the-art in proximity detection technology, leading to increased 

operator safety, and reducing the frequency of injuries and fatalities. 

Introduction 

Operating large mobile equipment such as a continuous mining machine (CMM), 

shown in Figure 1, is a hazardous job that workers perform in underground coal 

mining operations. 

Figure 1: Continuous Mining Machine 

 Some of the conditions which make this hazardous are the 

potential for roof falls, the close proximity of large moving machinery, decreased 

visibility due to low lighting and high dust levels, and high noise levels. In addition to 

the task of cutting coal from the face, continuous mining machine operators must 

focus attention on their own position, the location of other crewmembers, and the 

proximity of the machine to the crew. There are unsafe areas that the remote-control 

continuous mining machine operators and other workers must avoid. Some areas are 

clearly defined, such as beyond supported top which is defined by the last row of bolts 

supporting the roof. Recent NIOSH research by Bartels et al. (2009) identified safe 

and unsafe zones for the operator near the continuous mining machine. Since the 

mining environment is dynamic, creating physical barriers to keep operators out of the 

unsafe zones is not feasible. 
In the past, operation of continuous mining machines was performed from the 

machine cab in a seated position. In the 1980s, new technology enabled the transition 

to remote-control of the mining machinery. By removing the operator from the 

machine cab, several safety hazards associated with having the operator near the coal 

face were alleviated. With remote-control capability, the operators are now free to 

position themselves for better safety and better visibility of the workplace. Typically, 

the operator positions themselves behind and to one side of the machine during 

cutting operations. During tramming, the operator walks near the rear of the machine 

in high coal seams where the machine is less of an obstruction. In low coal seams, the 

operator trams the continuous mining machine while walking or crawling in front of 

it. This difference is because the operator cannot see over the machine from the rear. 

Unfortunately, operators have the tendency to step beside a moving continuous 

mining machine for a better view during forward, reverse and turning movements 

while cutting coal or tramming. Bauer et al. (1994) reported that the practice of 

extended-cut mining has increased the operators’ tendency to position themselves in 

hazardous locations. Additionally, Steiner et al. (1994) stated that an unforeseen 

consequence of remote-control operation is that an operator can position themselves 

in dangerous or hazardous locations that could result in a fatality or injury from 

possible roof falls, mine wall breakouts, pinch-points or other vehicle traffic. Adding 

to the hazards of operating a continuous mining machine is the restricted workspace 

with reduced visibility. The mine work environment, such as in low coal seams shown 

in Figure 2, puts continuous mining machine operators and helpers in awkward work 

postures for a job consisting of tasks that require fast reactions to avoid being struck 

by moving equipment. 

Figure 2: Typical Mine Environment 

 Furthermore, Lewis (1986) reported that restricted visibility 

due to the nature of the mine environment and low lighting conditions further 

complicates the tasks involved in operating mining equipment. 
The Mine Safety and Health Administration (MSHA) recommends a set of “red 

zones” that define dangerous areas near the continuous mining machine, and operators 

are supposed to avoid these areas. These zones help operators to understand and avoid 

potentially dangerous areas within the turning radius of the machine. While this 

concept has been around since the mid 1990’s, fatalities and injuries continue to occur 

with moving machinery underground. A survey of the 2002–2008 accident data from 

MSHA reveals that an average of 252 accidents occurs per year involving remotecontrolled continuous mining machines. Since 1984, there have been 33 fatalities 

involving workers being struck or pinned by these machines. This indicates that 

violations of the red zone recommendation occur frequently. Research (Bartels, 2009) 

shows the red zone guidelines address potentially hazardous situations, but ignore 

what the operators need to see and sometimes conflict with where the machine 

operators would like to position themselves in order to perform their job. A 

technological control to prevent the continuous mining machine from making 

hazardous motions with workers nearby would reduce the frequency of these 

accidents. A promising technology for this purpose is electromagnetic proximity 

detection, which utilizes magnetic fields to determine the proximity of workers to the 

machine. In this paper, we present an advanced, intelligent system utilizing 

electromagnetic proximity detection hardware along with novel and efficient software 

for determining the 2- or 3-dimensional position of a worker and intelligently 

responding with alarms or disabling machine movement. The implementation of this 

intelligent system could greatly improve the safety of miners while also reducing the 

frequency of false alarms that are a problem for some currently available proximity 

detection systems. 

Background 

Remote-control operation has required the continuous mining machine operators to 

divide their attention and process more information simultaneously. Defining and 

prioritizing what cues and feedback the operator needs and determining what 

operators focus their attention on can then be used to develop safe, realistic operating 

procedures. The cues that operators use are primarily visual but will sometimes 

include auditory information to compensate when visual cues are blocked. 

Researchers can use this information when analyzing human-machine systems, it is 

important to examine the components of the mining machine operator and the 

machine within the work environment. The mining environment is a unique challenge 

due to its dynamic nature, many hazards, and operational information that must be 

continually monitored by the continuous mining machine operator. The ability to 

process and utilize feedback information, in particular the visual cues the operator 

uses, is an important component of the human-machine system. Safe and effective 

control of the system is dependent upon the worker properly sensing pertinent 

information and processing it to make the right decisions. Experienced miners have 

expanded their knowledge, skills, and abilities to perform safely and effectively. By 

identifying the specific cues used by these experienced operators, interventions and 

training methods can be designed to improve safety for all operators. 
Previous studies by Bartels et al. (2009) identified and defined visual attention 

locations (VALs) associated with remote operation of continuous mining machines. 

In this research, VALs are particular locations needed and visually used by the 

operator for machine control and operation. The operator needs to consider safe work 

positions, sounds, vibrations, and operator VALs such as machine orientation, 

operating characteristics and other visual cues within the work environment to 

perform their job effectively. These factors have to be accommodated concurrently 

when considering a safe operator location. The optimum work location for an 

operator may differ depending on the length of cut, visibility, roof condition, 

ventilation and avoidance of moving machinery. As part of this research project, the 

study gathered information on operator work positions and VALs needed when the 

operator trammed a continuous mining machine during the cutting phase or when 

moving to a new location. Analysis of the data defined the operators’ risk of injury 

relative to the operators’ task, equipment and workplace environment. The results 

showed that operators of continuous mining machines needed to maintain a 3-foot 

minimum distance for safety. In addition, the data indicated that a major contributing 

factor to continuous mining machine related injuries is operators positioning 

themselves in a hazardous position in order to see cues or VALs. 

Several types of proximity detection systems using various technologies have 

been developed (Ruff and Hession-Kunz 2001; Ruff and Holden 2003; Ruff 2004; 

Kloos, Guivant et al. 2006; Ruff 2006; Ruff 2007). Some of the technologies utilized 

in surface mining and in other industries include the Global Positioning System 

(GPS), and radar-, laser- or ultrasonic-based distance sensors. Unfortunately, these 

technologies are ineffective in underground mines, where GPS is unavailable, and the 

constant close proximity of mine walls makes the use of the other sensors extremely 

difficult. 

Another possible solution is the use of Radio Frequency Identification (RFID) 

technology. Many industries commonly use RFID for tracking the movement of 

personnel, supplies and equipment. It is also currently in use in the mining industry 

for tracking the movements of people, equipment and supplies through the mine. 

These systems are capable of providing information on whether a tag worn by a 

person or mounted on a machine is within a set range of the transmitter. These 

systems typically operate in the very-high (VHF) or ultra-high (UHF) radio 

frequencies, and interference from signal reflections and line-of-sight requirements 

make applying these systems to continuous mining machines difficult. 

Another emerging technology that may be applicable to this problem is intelligent 

video systems utilizing either single-camera or stereovision and complex algorithms 

to identify and locate people and machines in the visual scene. However, application 

of this technology in the underground mining industry is likely to be very challenging 

due to poor lighting, dust, and the extreme difficulty in keeping the cameras clean. 

In a survey of companies implementing proximity detection technology 

internationally, NIOSH found that mines in South Africa and Australia are using 

electromagnetic-based systems in several underground coal mining operations. 

Partnerships between coal operators and the companies marketing the system are a 

common mechanism for developing these systems. The systems are used on 
continuous mining machines as well as other underground equipment such as shuttle 

cars, roof bolting machines, and feeder/breakers. The systems provide warning and 

danger zones around equipment. An operator receives visual and audible warnings 

upon entry into the warning zone and further approach into the danger zone causes the 

equipment to shut down. 

Currently, in the United States, the available systems for underground mining use 

either magnetic fields or radio frequency technology to alert miners when a machine 

is close to another machine or a person. On continuous mining machines, these 

systems will disable all machine movement if an operator moves too close. Recent 

interviews with the mining community (Kingsley-Westerman, 2010) indicate this 

action results in frequent nuisance alarms and shut downs. NIOSH researchers are 

developing technology that adds a measure of intelligence to these systems. The 

NIOSH intelligent system accurately determines miners’ positions relative to the 

machine and responds by disabling only the specific machine functions that could 

cause injury. 

Intelligent Proximity Detection System 

If a proximity detection system is implemented that completely disables machine 

movement when a person is located near the machine, nuisance alarms are likely to 

occur frequently. From the standpoint of machine operators, it may seem that the 

proximity detection system is preventing them from performing their job effectively 

or standing where they need to stand. For miners to accept the use of proximity 

detection technology, the technology must provide the necessary protection while 

minimizing the occurrence of nuisance alarms. NIOSH researchers have developed a 

solution to this issue in an intelligent proximity detection system. This system 

accurately determines the position of miners around the continuous mining machine 

using magnetic fields generated by multiple pulsed electromagnetic field generators. 

The signal strength from each of these generators is measured by an operator-worn 

Personal Alarm Device (PAD). Distances are estimated from the signal strengths and 

are used to triangulate the PAD position. Based on this triangulated position, the 

onboard controller disables specific machine functions such that unsafe actions are 

prevented but safe actions are allowed. In this way, the operator maintains the 

freedom to stand in close proximity to the machine and continue to mine without 

being hampered by nuisance alarms. The implementation of this technology, along 

with proper training, is likely to greatly enhance the acceptance of proximity detection 

by mining machine operators. 

An intelligent system of this sort requires a method for accurate position 

calculation. Therefore, an accurate mathematical model of the magnetic field shape is 

needed. NIOSH researchers have developed such a model for magnetic field 

generators that use an antenna with a ferrite rod core typical of proximity detection 

systems (Carr, Jobes, Li 2010). The shape of the magnetic field is very complex and 

irregular. Equation (1) defines the shape of the magnetic “shell” in polar coordinates 

as all points having the same magnetic flux density.

(1) ܾ ൅ ሻߠ2ሺݏ݋ܿ ܽ· ൌ ߩ
In this equation, ρ is the radial coordinate measured from the center of the 

magnetic field generator and θ is the angular coordinate measured from the long axis 

of the magnetic field generator. The coefficients a and b are functions of the magnetic 

flux density as defined in (2) and (3). 

ିௗೌ ܽ ൌ ܿ௔ · ݉

ିௗ್ ܾ ൌ ܿ௕ · ݉

(2)

(3)

 In these equations, m is the magnetic flux density, which decreases with 

increasing distance from the magnetic field generator, and ca, da, cb and db are all 

positive constants dependent on the physical and electrical properties of the generator. 

These constants must be determined through a calibration process for each generator. 

Figure 3 shows the magnetic shell shape described by (1). 

Figure 3: General polar form of magnetic field model for a ferrite-cored generator. 

This shell represents all 

points at which a constant magnetic flux density is measured. Notice that the shell 

radius varies between (b + a) at θ = 0° and 180° and (b – a) at 90° and 270° (270° not 

shown in figure). The shell intersects a circle of radius b at 45°, 135°, 225° and 315° 

(225° and 315° not shown in figure). Inspection of Equation (1) shows that this will 

always be the case. It should be clear from examination of Figure 3, that if the value 

of a is large in relation to b then the shape of the magnetic shell will be more 

irregular. However, if a is very small in relation to b, the shape of the shell will 

become more regular, approximating a circle. Referring to (2) and (3), the constants 

behave in such a way that 0<ca<cb and that 0≤da≤db<1. This means that as the 

magnetic flux density, m, becomes smaller (i.e. the distance from the generator 

becomes larger), both a and b will increase, but the rate of increase for b will be much 

faster. This means that as the distance from the generator increases, the shapes of the 

magnetic shells becomes more regular. The three-dimensional model, shown in Figure 

4, is found by the fact that the magnetic field is rotationally symmetric around the θ = 

0° axis. 
Figure 4: Variation in size and shape of magnetic field in three dimensions 

The position of the PAD is determined by finding the intersection of two or more 

magnetic shells. Due to the irregular shapes of these shells, determining this 

intersection is not a trivial task, and an analytic solution cannot be determined. For 

two generators, 0 and 1, each will have its own calibration coefficients, and Equation 

(1) can be constructed for each generator with θ0 and θ1 corresponding to generators 0 

and 1, respectively. In the 2-D case, the task is to determine the values of θ0 and θ1 

that correspond to the intersection. This calculation can be done fairly simply with 

numeric techniques, such as a Newton-Raphson search. However, in the 3-D case, the 

problem is further complicated by the introduction of the angles of revolution around 

the θ = 0° axis, ψ0 and ψ1, which must also be determined and numerical solutions 

become computationally infeasible. Therefore, a novel geometric search method was 

developed which iteratively converges on the intersection of two fields in either two 

or three dimensions. The major benefit of this method is that it does not require 

significantly more computation time to find solutions in three dimensions. This makes 

it feasible for use with an onboard processor. 

This method converges to the intersection of the shells through an iterative series 

of spherica

ഥ

l approx

ഥ

imations. This method, described in three dimensions by Carr et al. 

(2010), can be most easily 

I

ࡼ

visualized in a two-dimensional simplification as shown in 

Figure 5. n

૙

 this fi

ࡼ

g

૚

ure, two generators, 0 and 1, are located in a plane at arbitrary 

positions, and , and arbitrary orientations. The solid lines around the generators 

indicate the magnetic shells defined by Equation (1) in which the angles θ0 and θ1 are 

defined relative to the long axis of generators 0 and 1, respectively. The shells can be 

approximated by two circles of radius r0 and r1. For the first iteration, an initial guess 

for the radii is made by assuming the average shell radius b as defined in Equation 

(3). These two circles will intersect at up to two points, and . From either of 

these intersections, the angles, θ0 and θ1, can be determined

തࡵ

 t

૙

hroug

തࡵ

h 

૚

geometry. These 

angles can then be used with Equation (1) to determine the model shell radii, ρ0 and 
ρ1. These radii are then used as the new radii, r0 and r1, for the circular shell 

approximations, and the process is repeated. It has been empirically determined that if 

this proc

തࡵ

ess is ite

כ

rated, and the intersection is used in every iteration, the solution 

will quickl

૚

y converge to the actual intersection, , of the two shells. Similarly, if 

is used, will be found. Iteration of the alg

തࡵ

orithm i

૙

૙തࡵ

כ

s halted when the radii, r0 and r

തࡵ

1

૚

, 

change by an amount less than a preset tolerance in a single iteration. 

Figure 5: Iterative spherical approximation method in two dimensions 

To extend this algorithm to three dimensions, the three-dimensional shells are 

approximated with spheres, and some additional constraining assumptions are made. 

It should be clear that an intersection between two three-dimensional shells would be 

either a point or, more likely, an infinite number of points located on an intersection 

curve. To limit the possible number of solutions, a further assumption is made that the 

PAD lies in a plane assumed parallel to and at a specified waist height from the 

ground surface. Based on the posture of the miner, a reasonably accurate assumption 

about the PAD elevation can be made. The inaccuracy introduced by having a poor 

PAD elevation assumption is analyzed in the test results later in this paper. 

Once a PAD position has been determined, the next step is to determine which 

machine functions to disable and which to allow. This is done by defining a number 

of zones around the machine. For the implementation and testing of the prototype 

system developed by NIOSH, a set of zones have been defined with associated 

shutdown logic. However, this is only one possible implementation and is not a 

recommendation for all mines. The conditions and standard operating procedures at a 

given mine should be considered when designing the logic that controls the proximity 

detection system. Figure 6 shows the zones defined for the prototype system and 

Table 1 shows the machine functions that are disabled for each of these zones. Note 

that the zones overlap each other and extend over the mining machine itself. 
Figure 6: Safety zones used in prototype system.
 

Table 1: Machine functions disabled by zone
 

X

X X X 

Zone 

123 4 5 6 7 8 9 10 

Tram both forward 

right only forward 

left only forward 

both reverse 

right only reverse 

left only reverse 

right forward/left reverseX X X X X X 

left forward/right reverse XX XX X X 

X X X X X 

X X X X X 

XXX 

X X X X X X 

XXX X X X 

 X 

Conveyor raise 

lower 

swing right 

swing left 

X X 

X 

 

Cutter head raise 

lower X X X 

Gathering panraise 

lower 

X X X 

X X X 

Cutter motor X X X 

Conveyor motor X X X 

High speed tram X X X X X X X X X X 

Each zone is defined as a convex polygon with an arbitrary number of sides, N. 

The vertices of the polygon, vi (i = 1...N), must lie in the horizontal plane and must be 

defined in the clockwise direction. The triangulated PAD position is P, and the PAD is 

assumed to be within a circle of radius, r, centered at P. The radius, r, corresponds to 

the uncertainty in the PAD position. Therefore, the task is to determine whether the 
ࡽ

࢏

polygon defined by vi (i = 1...N) intersects this circle and if, therefore, the PAD may 

be within the polygonal zone. Figure 7 shows a general illustration of this problem. 

r P

v1

v2

vi

vN‐1

vN

ui

Qi

vmod(i,N)+1

Figure 7: Zone identification algorithm 

The first step is to determine whether P is within the polygonal zone. This is done 

by evaluating Equation 4, in which z is a unit vector in the upward vertical direction, 

for i = 1...N. If this expression is true for all values of i, then P must be within the 

zone in question. 

0 ൐ ࢠ · ൯൧࢏ (4) ࢜ െ૚ ሻାࡺ,࢏ሺࢊ࢕࢓࢜൫ ൈ ࢏ሻ ࢜െࡼሺൣ

If this is not true, the next step is to determine whether the circle intersects any of 

the polygon edges. Each edge is checked individually. To do this, first a unit vector, 

u, is defined pointing from vi to vmod(i,N)+1 by Equation (5). 

࢏ൌ ࢛

࢜ି ࢜

࢜ฮ

࢏ ૚ሻశࡺ,࢏ሺࢊ࢕࢓

(5) ฮ࢏࢜ି૚ሻశࡺ,࢏ሺࢊ࢕࢓

This unit vector is used in Equation 6 to find a point, Qi

to P on the pol geygon ed . 

 which is the closest point 

࢏࢜ ൅ ࢏࢛ฮ൯࢏࢜ െ૚ ሻାࡺ,࢏ሺࢊ࢕࢓࢜ ൯ ࢏࢛ · ሻ࢏ (6) ฮ, ,0 ࢜െࡼ൫ሺݔ݉ܽ൫݉݅݊ ൌ ࢏

Finally, if Equation 7 is true for any value of i, the circle centered at P must 

intersect the polygonal zone. 

(7) ݎ ൑ ሻԡ ࡽെࡼԡሺ

If Equation 4 is true for all values of i or if Equation 7 is true for any value of i, 

the PAD is located within the zone. This is repeated for all zones. Because the zones 

can overlap, and because the PAD position is defined as a circle rather than as a single 

point, it is possible for the PAD to be located in more than one zone. In this case, only 

those machine functions that are allowed in all zones in which the PAD is located will 

be allowed. 
Test Procedures 

This system has been implemented and tested using a Joy 14CM continuous mining 

machine as a prototype platform. The installed proximity detection hardware is shown 

in Figure 8, and the host PC used for programming and testing is shown in Figure 9. 

Figure 8: Electromagnetic PAD and field generator used for prototype system. 

Figure 9: Host PC used for programming and testing prototype system. 

Figure 10 illustrates, conceptually, the interface between the intelligent proximity 

detection system and the mining machine control hardware. Normally, the remote 

control sends operator input to the demultiplexer (or demux) which sends control 

signals to the individual actuators. In the prototype system, the actuator signals travel 

through a set of relays that the onboard controller selectively opens or closes. In this 

manner, individual controls are selectively disabled, but the software does not send 

active control signals to the actuators. This implementation was effective for the 

prototype system, but is only one of several types of hardware that could achieve 

similar behavior. Figure 11 shows the major components installed on the Joy 14CM. 
Figure 10: Relay-based hardware implementation 

Figure 11: Intelligent control hardware as installed on Joy 14CM prototype testbed 
The generator placement in the prototype system was designed to provide 

excellent triangulation accuracy near the mining machine. With the available 

hardware it was impossible to provide coverage around the entire machine. The 

generators were concentrated near the rear of the mining machine since this is where 

the operator usually stands. The generator placement is shown in Figure 12. Planned 

future work will expand the prototype system to extend coverage over the entire 

machine. 

Figure 12: Generator placement in prototype system; generators concentrated near 

rear of the machine due to range limitations 

The model given in Equations 1-3 was calibrated using measurements taken with 

a PAD at various locations around each of the generators. From these measurements, 

a least-squares fit was used to determine the model coefficients for each generator. 

For this prototype installation, these coefficients are summarized in Table 2. 

Table 2: Model calibration coefficients used for prototype tests. 

Generator 1 Generator 2 Generator 3 Generator 4 

ca 104.6 45.6 32.1 159.6 

da -0.210 0 0 -0.322 

cb 1287.5 977.0 1883.4 1696.7 

db -0.264 -0.211 -0.340 -0.322 

These 

coefficients provide the radius, ρ, based on the magnetic field strength reading 

received from the proximity hardware, which is proportional to the magnetic flux 

density. 
Using this calibration, the accuracy and reliability of the prototype intelligent 

proximity detection system has been tested. The accuracy is measured as the distance 

between the actual PAD position and the triangulated position. Since the accuracy is 

critical within three feet of the machine, the prototype system has been designed to 

have excellent accuracy in this zone. Therefore, the accuracy has been analyzed in 

two populations: within three feet of the machine, and outside three feet of the 

machine. 

While the accuracy of the triangulation gives an indication of the system 

performance, a more important measure is the reliability with which the system 

correctly disables machine movement or issues alarms. Tests were performed to 

measure this reliability in terms of missed alarms and false alarms. The current 

implementation of the prototype system does not include visual or audible alarms, so 

the missed alarm and false alarm rates will be discussed in terms of the machine 

functions that are disabled by the system. If the PAD is located within a zone as in 

Figure 6, the machine functions associated with that zone, shown in Table 1 should be 

disabled. A missed alarm is defined as when a function should have been disabled, but 

was not. A false alarm is defined as when a function should not have been disabled, 

but was. 

This is slightly complicated when either the actual position or the triangulated 

position of the PAD is near the boundary of a zone because the position of the PAD is 

not defined as a single point, but rather as a circle of a particular uncertainty radius. 

Figure 13 demonstrates several possible scenarios and how they are interpreted. In 

this figure, the actual and triangulated PAD positions are shown along with the circle 

associated with the uncertainty. If the actual PAD position is outside but within the 

uncertainty radius from the zone boundary, it is not clear whether the machine 

functions associated with that zone should be disabled. In this case, either disabling or 

not disabling those functions would be considered correct system behavior as this 

uncertainty is part of the logic design. If the PAD is outside the zone by a distance of 

more than the uncertainty radius, than a false alarm can occur if the triangulated 

position is either within or near the zone boundary. A missed alarm only occurs when 

the actual PAD position is inside the zone and the triangulated PAD position is 

outside the zone boundary by more than the uncertainty radius. 

 

   

     

     

     

   

               

     

   

     

   

     

Triangulated Triangulated

(a)	 PAD position 2: (b) PAD position 2:

Triangulated Triangulated identified as identified as PAD position 1:	 PAD position 1: out of Zone out of Zone Actual PAD

identified asin	 identified as in (Correct) (Correct) position

Zone

Actual PAD

position

Zone

(Correct)

Zone

Zone

(FALSE ALARM)
   

     

     

  

     

  

     

Triangulated

(c) PAD position 2: Triangulated identified as PAD position 1:

out of Zone identified asin (MISSED ALARM) Zone

(Correct)

Actual PAD

position

Zone

Figure 13: Missed alarm and false alarm logic for cases in which (a) PAD is outside 

but near the zone, (b) PAD is outside the zone and (c) PAD is inside the zone 

Since the PAD can be identified as being within multiple zones, and since some of 

the zones result in the disabling of the same machine functions, the missed alarm and 

false alarm rates must be analyzed in terms of the machine functions that are disabled. 

Consider the case that the PAD is actually located only in zone A, but is identified as 

being located only in zone B. If a given machine function is disabled for both zones A 

and B, a missed alarm will not have occurred for that function. 

The PAD was mounted on a nonmetallic stand and positioned at all points on a 1­

meter grid around the Joy 14CM (Figure 14 and Figure 15). The actual position of the 

PAD was determined with surveying equipment. Two reference points on the mining 

machine were located to establish the position and orientation of the machine. This 

allowed each surveyed location of the PAD to be expressed relative to the mining 

machine and in the same coordinate system that the proximity detection system uses. 

The PAD was positioned at two belt heights: 16 inches, corresponding to a 5th 

percentile female kneeling, and 46 inches, corresponding to a 50th percentile male 

standing. In the discussion that follows, these PAD heights are referred to as “low” 

and “high,” respectively. In addition to the actual PAD elevation, the PAD elevation 

that was assumed by the triangulation software was also varied between the low and 

high elevations. The tail of the mining machine, which can be moved through a range 

of 90° in swing and 6° in elevation, was also varied. The swing positions used were 

left (45° left of center), center (aligned with the machine’s centerline) and right (45° 

right of center). The tail elevation was varied between down (3° above horizontal) and 

down (9° above horizontal). 
Figure 14: Triangulation accuracy test setup for prototype system 

Figure 15: Data collection points. 

Test Results 

Figure 16 shows the accuracy of the triangulated position for the tests in which the 

prototype system was installed on the Joy 14CM mining machine. In this figure, the 

actual position of the PAD is shown with a blue dot and the triangulated position of 
the PAD is shown with a green circle. The radius of this circle indicates the 50 cm 

uncertainty radius used in determining the zone in which the PAD is located. The 

points for which the system has a missed alarm are highlighted in this figure in red. 

Excellent accuracy is achieved close to the machine and the PAD position is 

generally known to within 20 to 50cm. The errors increase with distance from the 

generators, tend to be high in specific areas due to generator pair selection for 

triangulation, and tend to be in a tangential direction due to the shape of the fields. 

Further from the mining machine, the accuracy is worse, even exceeding 3 meters. 

However, it can be argued that accuracy further from the machine is not as critical 

since the miner operator will be in a safer location and only identifying his general 

location is of importance. 

There is a very low occurrence of missed alarms. In the example shown, there is 

only one missed alarm highlighted in red. In this case, the PAD is actually located 

very close to the border of one zone, but is determined to be in the adjacent zone. The 

very large error at this point is because the PAD is located very far from the 

generators which were concentrated near the rear of the mining machine. 

Figure 16: Example triangulation accuracy results with prototype system; dots 

represent the actual PAD position, circles represent the triangulated PAD position 

with uncertainty radius, larger solid circle indicates a missed alarm 

Figure 17 and Figure 18 summarize the triangulation accuracy achieved in tests 

with the prototype system. Due to the sampling locations and low sampling density in 

the prototype system test, the area outside three feet from the machine is somewhat 

under-represented in these results. The accuracy depends strongly on accurate 
knowledge of the PAD elevation. The PAD elevation is needed as a constraint in the 

triangulation algorithm, and can be assumed based on the posture of the miner. Cases 

b and c in Figure 17 show how the accuracy can be greatly reduced when a poor 

estimate of the PAD elevation is used. In general, the accuracy of the triangulation is 

generally known to within 50 cm for the normal operating case in which the PAD is 

located at a high elevation and assumed at a high elevation. 

Figure 17: Results of testing with prototype system: PAD position triangulation 

accuracy for (a) PAD elevation high and assumed high, (b) PAD elevation low and 

assumed high, (c) PAD elevation high and assumed low, and (d) PAD elevation low 

and assumed low 

PAD within 3 feet of machine 

Assumed 

PAD 

Elevation 

Actual 

PAD 

Elevation 

No. of 

Samples 

Mean 

Error 

(cm) 

St. Dev. 

Error 

(cm) 

90% 

Confidence 

(cm)* 

95% 

Confidence 

(cm)* 

Plot 

symbol 

High High 

Low 

209 27 31 48 

172 53 36 93 

81 

107 

Low High 

Low 

194 34 22 57 

168 41 36 76 

72 

84 

PAD outside 3 feet of machine 
 

Assumed Actual No. of Mean St. Dev. 90% 95% Plot 

PAD PAD Samples Error Error Confidence Confidence symbol 

Elevation Elevation (cm) (cm) (cm)* (cm)* 

High High 131 56 53 127 163 

Low 127 64 51 143 174 

Low High 130 64 52 137 170 

Low 123 61 50 136 155 

Figure 18: Triangulation accuracy for tests with prototype system represented with 

cumulative frequency plot 

Table 3 summarizes the missed alarm and false alarm rate for the prototype 

system tests. The “Desired Disable Events” refers to the total number of times any 

machine function should have been disabled and was determined based on the actual 

PAD position. The table shows that, in general, the system responds correctly nearly 

all of the time by disabling the correct machine functions to prevent collisions and 

allowing those functions that will not cause a collision. In general, the missed alarm 

rate is low. The missed alarm rate is higher, however, when the PAD is low and the 

tail is raised. An increase in error is expected in this case because the distance 

between the generators and the PAD is increased. 

In general, however, the results of these tests are very encouraging, showing that 

under most operating conditions, the system will respond appropriately by disabling 

the correct machine movements to prevent a collision. 
 

Table 3: System reliability in terms of missed and false alarms as tested with the 

prototype system 

Assumed 

PAD 

Elevation 

Actual 

PAD 

Elevation 

Tail 

Elevation 

Tail 

Swing 

Desired 

Disable 

Events 

Correctly 

Disabled 

Missed 

Alarms 

False 

Alarms 

High High Down Left 

Center 

Right 

194 

233 

215 

98.5% 

98.7% 

98.6% 

1.5% 

1.3% 

1.4% 

21.1% 

13.7% 

33.0% 

Up Left 

Center 

Right 

190 

233 

202 

95.3% 

100.0% 

100.0% 

4.7% 

0.0% 

0.0% 

10.5% 

11.6% 

23.8% 

Low Down Left 

Center 

Right 

178 

199 

187 

100.0% 

100.0% 

100.0% 

0.0% 

0.0% 

0.0% 

10.7% 

6.5% 

16.0% 

Up Left 

Center 

Right 

174 

201 

174 

89.7% 

98.5% 

82.8% 

10.3% 

1.5% 

8.6% 

8.0% 

17.2% 4.6%

Low High Down Left 

Center 

Right 

186 

225 

199 

100.0% 

100.0% 

100.0% 

0.0% 

0.0% 

0.0% 

40.9% 

28.0% 

25.1% 

Up Left 

Center 

Right 

164 

229 

189 

100.0% 

100.0% 

100.0% 

0.0% 

0.0% 

0.0% 

37.8% 

33.6% 

26.5% 

Low Down Left 

Center 

Right 

174 

190 

187 

100.0% 

100.0% 

95.2% 

0.0% 

0.0% 

29.9% 

25.8% 

4.8% 28.3% 

Up Left 

Center 

Right 

170 

201 

165 

89.4% 

100.0% 

87.3% 

10.6% 

0.0% 

23.5% 

15.9% 

12.7% 21.2%

Discussion and Future Work 

While this prototype system represents a significant advance in the technology of 

proximity detection and is expected to greatly improve the safety of underground coal 

miners, more can be done to improve the system’s range, reliability, and accuracy. 

The next step in this research is to expand the number of generators to 6 and enhance 

their individual range as well so that the entire machine and much of its immediate 

surroundings can be covered (see Figure 19) Future tests on the resulting system will 

include quantifying the triangulation accuracy and the system reliability. Simulated 

mining tasks will be performed to collect data on the human machine interaction 

aspects of implementing this advanced proximity detection system. NIOSH 

researchers are also considering using operator worn MEMS inertial sensors to 

identify the operator posture and thus fine tune the assumed PAD height to improve 
triangulation results. Posture information can also be used to make changes on the fly 

to how the onboard controller reacts to potentially risky control requests by the 

operator. It is hoped that this new system and/or the technological advances 

represented in it will be adopted by the industry, and offer improved safety for 

underground coal miners. 

Figure 19: Future 6 generator system allowing coverage of the entire CMM 

Disclaimer 

The findings and conclusions in this report are those of the authors and do not 

necessarily represent the views of the National Institute for Occupational Safety and 

Health. 

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