Report and Recommendations for Strategic Research Areas 

from the

Metal Mining Automation and Advanced 

Technologies Workgroup 

under the

Mine Safety and Health Advisory Committee, NIOSH-CDC 

November 12, 2019 

Kray Luxbacher, Chair 

C.T. Holland Professor and Associate Head

Mining and Minerals Engineering, Virginia Tech

Blacksburg, Virginia

Stacy Kramer, Member 

Vice President, Global Health and Safety 

Freeport McMoRan 

Phoenix, Arizona 

Ronald Bowersox, Member 

International Representative 

United Mine Workers of America International 

Union 

Lucernemines, Pennsylvania 

Bruce Watzman, Member 

Senior Vice President (retired December 2018) 

National Mining Association 

Washington, DC 

Jefferey Burgess, Member 

Associate Dean for Research and Professor 

Mel and Enid Zuckerman College of Public 

Health 

University of Arizona 

Tucson, Arizona 

Michael Wright, Member 

Director of Health, Safety, and Environment 

United Steel Workers of America 

Pittsburgh, Pennsylvania 

Robin Burgess-Limerick, Ad Hoc Member 

Professorial Research Fellow 

Minerals Industry Safety and Health Centre 

University of Queensland 

Brisbane, Australia 

Kyle Zimmer, Member 

Director of Health and Safety/Instructor 

International Union of Operating Engineers 

Hamden, Connecticut 

Joel Haight, Ad Hoc Member 

Professor 

Industrial Engineering, University of Pittsburgh 

Pittsburgh, Pennsylvania 
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Executive Summary 

The following recommendations for work at NIOSH in the area of Metal Mining and Advanced 

Technologies are described in the report herein: 

• Track the degree of automation in various sectors of the industry, best practices, and

determine measurable impacts on health and safety.

• Assess and define appropriate limits for human operators interfacing with machines,

particularly in remote control – what are the maximum number of alarms/decisions the

operator can reasonably make, how is alertness maintained, what are appropriate

strategies to provide situational awareness, and distill data? What are strategies for

addressing the change from normal operating conditions to emergency conditions in a

control room?

• Determine the applications of virtual and augmented reality for training workers and

testing autonomous systems.

• Identify and study the gaps in sensing and situational awareness, developing solutions

that complement existing technology. This may include designs for providing situational

awareness to operators, new sensing devices, such as wearable sensors for mine

workers, continue to expand and build upon work in proximity detection, sensing of

operator alertness and controls for maintaining alertness and engagement.

• Research the use of unmanned vehicles for collection of environmental data that could

lead to improved health outcomes, collection of environmental data to provide

improved safety (prevention of explosive or toxic atmospheres, inspection of hazardous

areas), and collection of data during emergencies and catastrophic events.

• Identify measures of success for autonomous systems in terms of health and safety, and

disseminate standards and tools for such measures. In other words, what is the

equivalent to a lost time accident in an autonomous system? How is efficacy of such

systems measured in terms of health and safety outcomes?

• Conduct a complete review of other industries undergoing similar transformative

change with regard to autonomous systems, and identify partner offices in NIOSH, as

well as partner agencies around the world for transfer of knowledge and best use of

resources.

• Identify partner operations for holistic research that characterizes the best approach for

mine site design or retrofit with regard to autonomy, and develop plans for long term

projects.

• Design risk management approaches that are less linear than current approaches for use

with complex autonomous systems.

• Study how the behavior of workers in mines changes as they interact with autonomous

systems.

Introduction and Background 

During the May 22nd, 2018 meeting of the Mine Safety and Health Advisory Committee to NIOSH-CDC a 

workgroup was formed and charged to investigate the following:
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The Metal Mining Automation and Advanced Technologies (MMAAT) Workgroup reports to the Mine 

Safety and Health Research Advisory Committee (MSHRAC) regarding specific questions that impact 

potential health and safety issues related to the implementation of automation and other advanced 

technologies in U.S. metal mining. The workgroup is specifically tasked to draft a report for MSHRAC to 

review, deliberate and consider recommendations. Three questions have been developed to facilitate 

the workgroup’s activities: 

1. To what extent will automation and smart technologies be implemented in metal mining and in

what timeframe?

2. What are the related emerging health & safety concerns?

3. What gaps exist in occupational health & safety research related to automation and smart

technologies?

Research and data gathering included facilitating a mining stakeholder meeting, open to the public, to

discuss potential health and safety issues related to the implementation of automation and other 

advanced technologies in U.S. metal mining. 

The charge document may be found in Appendix A.

The workgroup held the first meeting on June 14, 2018. A public workshop was held on September 10-

11, 2018 in Aurora, Colorado at the University of Colorado Anschutz Medical Campus. There were 

approximately 40 workshop attendees, and stakeholders from the metals mining community included 

operators, vendors and technical, consultants, and academics, as well as several representative from 

NIOSH. The workshop agenda is available in Appendix B. Additionally, public materials from the 

workshop, including working documents are available at: https://sites.google.com/vt.edu/mshracmetals-tech/home.

The remaining sections of this report will address the workshop outcomes in terms of the questions 

posed in the charging document, and recommendations for strategic research areas for NIOSH based on 

stakeholder feedback.

Workshop Outcomes

The next three sections aim to collate the presentations and discussion from the workshop into 

guidance under the three charge questions referenced above.

Extent and timeframe for implementation of automation and smart 

technologies in metal mining 

The consensus of workshop attendees was that automation and smart technologies will be 

implemented and integrated into existing operations cautiously. In particular, stakeholders cited the 

fairly low tolerance of the public and by extension, miners, for failure of automated systems. For 

example, three fatal accidents involving autonomous vehicles in the US (Stewart, 2018) since Tesla 

rolled out Autopilot on its vehicles in October 2015 (Ramsey, 2015) have received significant attention, 

although the average number of fatalities due to vehicle crashes in 2017 alone was 37,133 (National 
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Center for Statistics and Analysis, NHSTA, 2018). The industry is very clear that automated systems must 

be robust and that miners must trust the systems in order to achieve success. 

Based on this feedback, it is difficult to establish a timeline, but it would be appropriate to assume that 

currents leading practice might be broadly implemented in large metals mines in the next 10-15 years. 

Leading practice includes:

• Fully automated haul truck fleets at surface operations

• Fully automated underground mining sections

• Remote control centers, and integrated automation of mining systems

• Implementation of ventilation-on-demand (VOD) in underground mines for efficient control of

air quality (temperature, gases, particulate) and quantity.

Related Emerging Health and Safety Concerns 

In particular, Robin Limerick-Burgess (2018) presented a suite of 18 questions that spurred discussion of 

emerging health and safety concerns, and nicely summarizes the discussion – they are:

1. How can we ensure decisions regarding automation implementation take human capabilities

and limitations into account?

2. What lessons can be learned from the experiences in other industries of Human-Systems

Integration during automation?

3. How can HSI methods be most effectively employed during the introduction of automation in

the mining industry?

4. What design and evaluation strategies should be employed to ensure alarms and other

interfaces are optimized?

5. How can isolation for maintenance be ensured and confirmed?

6. What strategies can be employed to introduce automated components into mining systems?

7. How can we create rewarding jobs for people to undertake within the increasingly automated

system?

8. How do we select line managers and workforce for their roles within the more automated

system?

9. How do we train (or retrain) line managers and workforce for their roles within the more

automated system?

10. How can the competency of people working within the system be assessed and demonstrated?

11. What are the utility and limitations for virtual simulation in design processes and within training

and competency assessment?

12. How can we unlock additional productivity gains through the optimal use of automation?

13. How do we ensure that unanticipated consequences of introducing automation and any

consequential risks are identified and managed?

14. How can the wider societal impacts of increasing automation in the resources industry be

managed most appropriately?

15. What verification processes can be developed to assure industry, regulators, unions, and the

public, of the safety of systems including automated components?

16. What are the appropriate risk management frameworks and tools to guide the implementation

of automation?
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17. What is the role of standards and guidelines in facilitating the implementation of safe and

effective automation?

18. What should the role of regulators be in facilitating the implementation of safe and effective

automation?

Gaps in occupational health & safety research related to automation 

and smart technologies 

1. Tracking the degree of automation in various sectors of the mining industry, along with best

practices

Tracking trends and best practices related to safety and health in mining has long been a key strength 

and role of NIOSH in the national dialogue around mine safety and health, and detailing and 

communicating emerging automation in the various sectors, regions, and commodities of mining will 

provide a great service to the industry and allow for early identification of gains in health and safety as 

well as unintended consequences of automation and remediation of associated hazards.

Several researchers have examined the roles philosophical and ethical frameworks, as well as ethical 

design considerations that are necessary for widespread acceptance of self-driving cars (e.g., Charness 

et al., 2018; Karnouskos, 2018; Borenstein, et al., 2019). Similar investigation of acceptance in the 

domains of occupational safety and health, and specifically, mine workers, may prove important in 

successful and safe implementation of automation in the industry. The major issues around acceptance 

of the technology identified by workshop attendees were training, regulatory approval (US), 

interoperability, and reassignment of displaced workers.

2. Human-computer interface design to include leading practice, issues related cognitive overload,

and feedback strategies

Sensors, boredom, reduction in actionable cues, loss of skill, loss of information, loss of situational 

awareness, new types of errors

There are many aspects that need to be studied about the increasing trend of expanded use of technology 

in mining and in any industry for that matter. To a large extent, the development of technology is being 

driven by the technology manufacturers. In many cases, technology is being designed and built without 

clear application, although the manufacturer will have a particular industry application in mind. This is a 

backwards design approach which can lead to a force-fit or a miss-fit. The mining industry must 

continually be in search of improvements in safety, efficiency, productivity, quality, and reliability. The 

answer often seems to be somewhere in the application of the next new technology; unfortunately, in 

some cases, the safety and health of the workers who will use this technology are not considered in the 

development or in the application. Or, if human use is considered, it is considered under the banner of 

“removing the operator from harm’s way”. This can result in the displacement of our human operators. 

Displacement does not necessarily mean they lose their jobs, but it can mean that they are assigned to

another function for which they may not adequately trained or that may not have been fully developed. 

However, they are often still expected to remain engaged with the equipment and system that they once 

more actively operated. They are expected to apply their expertise to mitigate problems an automated 
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system may encounter. At that point, it is often too late for a now, bored, distracted or dis-interested 

operator focused on other things, to respond; an operator who has experienced skill decay (loss of 

operator expertise), loss of situational awareness, lack of information, lack of practice, unfamiliarity with 

the feedback, or a loss of memory of what to do when called upon. This can and likely will lead to new 

types of errors, and in some cases, a loss of the system to damage, or even worse, injuries to employees. 

These safety and health impacts of the transition to more a technologically driven or more automatic 

world are not yet well enough understood and this is a significant general gap in industry safety and health

research. 

Changing system dynamics (physical, cyber, and human system design – mine system design)

Even as operators are kept engaged in the system, one of the fallouts from automated control systems is 

the abundance of information with which operators are bombarded every minute. Because of this new 

found ease with which information can be provided (sensors can be placed almost anywhere and 

computers are capable of processing and sending data wherever it is needed), there is a profusion of 

actionable information that is nearly impossible for a human operator to synthesize and make informed 

decisions with. In one hospital study, Haight, Wetz, Daves, and Olumese (2018) stated that cardiac unit 

monitoring technicians were expected to process twice the information that a human should be able to 

effectively process every minute with over 800,000 alarms in a month. Several phenomena are known to 

occur in this environment and one of the more dangerous is that operators become overwhelmed, underrely on the system, and then respond errantly or not at all. Mine system design needs to incorporate a 

balanced function vs. human capability approach. This information overload and its impact on safety and 

health in the mining industry must be better understood through active research.

Communications and networking infrastructure

Engineers creating the technology focus on functionality; Engineers responsible for developing industrial, 

mining, defense, health care, and other systems and processes focus on design of the tasks and processes 

necessary to generate product output, and then the human factors engineers, psychologists and 

sociologists focus on the human operator, the community in which he or she lives, and society as a whole. 

They all have a job to do, but they all speak different languages. They do not always speak with each 

other or, in some cases, do not even understand each other to the extent that will ensure optimum system 

integration or the best and safest performance of the overall system. If the technology developer does 

his or her job, the computer, robot, autonomous vehicle, or other component will function reliably and 

effectively. But if the human operator’s behavior, motivation, interests, or capabilities are not considered, 

the operator could become overwhelmed, be less attentive, become bored or not know what to do if 

something goes wrong. Unfortunately, systems are often built without consideration of the human’s 

capabilities or limits and then, the now less-capable and less-attentive operator is left to save the system 

if an unanticipated scenario arises, and then it is often too late to save the system. The human operator 

cannot be an afterthought; he or she must be actively and meaningfully engaged through the entire 

operation to ensure proper operation of any system. This applies to the fully autonomous state as well. 

The engagement will be different, but no less important and critical to ensuring system performance. 

(Hancock, Billings, Oleson, Chen, de Visser and Parasuraman, 2011 and Miller and Parasuraman, 2007)

Research is needed to better understand what communication – content, media, and context - is 

necessary and how it needs to be delivered so all affected people have appropriate, adequate, accurate 

and thorough understanding and input to the functions, expectations, goals, and design of automated 

control systems and autonomous systems being developed in the mining world today. Multi-disciplinary 
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teams must be assembled to engage in this critical communication research and it impact on safety and 

health.

The invention of new technology versus the automation of “old technology” 

The question of developing new technology versus simply automating existing systems that were 

manually operated in the past presents some new and interesting problems. In actuality good design is 

less about implementing the use of new technology or redesigning old, it is most critical to understand 

an system and truly design the automation instead of just plugging in or force fitting a system without 

effective design in the automation domain or determination of the objective function or without 

considering human impact.

Critical controls, decision support, and decision making

There are many industries working to implement leading edge autonomous systems, as well as grappling 

with the questions posed here. Certainly, a thorough review of work in industries such as long-haul 

trucking, air traffic control, health care, and other process industries as if might apply to or transfer to 

mining is a critical first step. In addition, much of the focus on autonomous systems for mining 

emphasizes haulage, but all unit operations should be considered – rock fragmentation, for example.

In the process industries where explosions and fires are a real possibility with 24 hour/day operations 

and continuous processes that operate at high temperatures, high pressure and high flow rates the

extensive use of automation is required. These control systems are fed data through hundreds and even 

thousands of sensors throughout plant operations. This information is processed and provided to the 

human operators in many forms. This feedback is provided, but when the system is in control, the 

operators does not necessarily have to synthesize the information as plant actions are effectively 

controlled by the automated system. However, when something goes wrong, for example, a 

temperature spikes, a leak occurs, or a reaction rate increases beyond control limits, the number of 

alarms can easily be overwhelming (as many as 300 alarms or more within 5 minutes). To help in the 

rapid decision-making and quick response actions needed, there are alarm prioritization systems that 

are used to only present the critical alarms that are necessary to bring a unit under control before 

containment of flammable or toxic material is lost. Operators are able to manage the 10 or 12 most 

critical alarms in 5-10 minutes; they are not able to handle 300. Industries utilizing such approaches

include oil and gas, specialty chemicals, pharmaceutical, and paper manufacturing among others. The 

health care industry is also paying more attention to alarm overload in its telemetry covered units. They 

are evaluating patient conditions to determine when telemetry monitoring is necessary and they are 

evaluating rotation schedules to ensure no one monitoring technician stays at the monitoring screen for 

12 hours. Air traffic controllers also use critical information prioritization systems and job rotation to 

address the potential information overload situations. There should be no reason why mining 

operations cannot take advantage of these systems and administrative controls such as creative job 

rotations. 

Other research gap areas

Learning Curve Impact

A major area of concern for the mining industry is related to the learning curve impact that 

the industry will see as its employees and contractors are forced to take on new and different 
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roles. As their traditional roles and functions are taken over by the autonomous systems, 

experienced people will quite suddenly become inexperienced as they take on new jobs and 

are expected to perform new tasks. Often, these tasks will require higher order thinking and 

will require new and more advanced training and education. These mid- and long-service 

employees will become inexperienced in a new role and in integrating with the autonomous 

systems. This will undoubtedly have health and safety impacts. These impacts will need to 

be identified, located, quantified and better understood in the mining industry. (Sauer, 

Hockey and Wastell, 2000; Sauer and Chavaillaz, 2017)

System Performance Effectiveness and Risk Reduction Research

A widespread assumption is being made that when we get the fallible, unreliable human out 

of the machine and out of harm’s way, system performance and safety will improve. 

However, the intelligence and decision making capabilities and process speed of the human 

brain may be underestimated in the this assumption. As new technology is introduced and 

used, system performance effectiveness research will need to be conducted to quantify 

system performance improvements and actual risk reduction and to measure them against 

system performance during pre-technology use.

Human error research

The behavior of humans in partially- and fully autonomous environments has not been 

adequately explored. Will humans step in when they shouldn’t? Will the human operator 

fail to step in when he or she should? The fully autonomous world is well into the future and 

it is not yet established whether the mining industry or any industry, for that matter, can 

actually be made fully autonomous. No matter what the next generation mining industry 

looks like, the transition will involve humans making errors and an understanding of the types, 

the numbers, occurrence rates, and the locations should be well understood and predicted if 

possible. Much research will need to be conducted in a virtual environment (equipment, 

mine, etc.) to explore this phenomenon to the level that it is currently studied in the aviation 

industry (Haight and Kecojevic, 2005).

Autonomous systems as a preventive intervention

Since autonomous systems are often presented as a means of removing people from harm’s 

way, it makes sense to consider autonomous systems and their potential to be used as 

prevention interventions. When automated systems can be used to accomplish physically 

challenging work, work in adverse environments (close quarters or high traffic, wet, cold, 

slippery, etc.), work in dangerous environments (such as rescue in a flammable 

environment), risk of injury is reduced. This protective phenomenon is widely accepted 

anecdotally, but research is needed to quantify the risk reducing benefit such that these 

protective systems can actually be designed to achieve a specific risk reduction levels.

3. Unmanned vehicles for the improvement of health and safety

While unmanned vehicles play an extensive role in automated mining systems this primarily refers to 

the use of vehicles explicitly for improvement of health and safety. For examples vehicles that are used 

to inspect structures, enter hazardous areas for purposes of exploration and information gathering. Both 

ground vehicles and drones are included. Examples included drones that can be used to inspect large 

mills in mineral processing plants, so that personnel are not exposed to the risk of entrapment in 

enclosed spaces, hazardous atmospheres, potential failure of lock out/tag out systems. Another 
9

example was a small rubber-tired vehicle used to examine recently opened stopes, and abandoned 

areas about to undergo rehabilitation. Discussion during the workshop also included the notion that 

these vehicles may be unrecoverable and are actually designed to be sacrificed once information is 

gathered, either because recovery is too hazardous or too expensive. 

4. Characterization of the efficacy of automated systems in terms of health and safety

Characterization of the efficacy of automated systems centered around some standard guidance for 

identifying, logging and characterizing near-miss accidents in automated systems, so that systems may 

be comparably and objectively evaluated. Attendees at the workshop specifically indicated that there 

were needs for measurable performance metrics that are consistent and standardization of these 

metrics across the industry (nationally and globally). Also, they indicated that there must be standard 

recognizable units for these metrics. For example, the question was raised, how do we describe vehicle 

interaction and incidents - is the measure something like, incidents/mile? 

The role of adaptive automation 

The adaptive automation concept, benefits and pitfalls have not been adequately explored. Adaptive 

automation is the descriptive name given to systems where the human operator shares functions with 

the automated system. Each maintains their own role as long as they are able to effectively manage. 

When either becomes overwhelmed or the system experiences an upset, the other takes over functions 

while the compromised component regains stability. It is an effective way that designers and users can 

be sure that the user is going to stay engaged with the autonomous operation of the system. This will 

become more and more important as the human function is transferred to the automated control 

system. Specific research into shared function, resource allocation is still needed, particularly in mining. 

(Endsley and Kaber, 1999) 

Ventilation-on-demand systems may be one of the best current examples of adaptive automation in the 

mining industry. These systems control quantity and quality of air based on environmental changes 

(humidity, pressure, temperature, gas inflow), location of equipment (associated changes in 

temperature, fumes, and expectation of human operators), and mining operations (currently active 

levels, real-time location of personnel). The systems may operate in near-autonomous mode under 

normal conditions with full operator oversight, but shift to partial or full operator control as unusual 

conditions develop. 

These systems may have measurable impacts on health and safety. They can allow for reduction of DPM 

and dust in locations where mine workers are active; they can also more closely monitor and control 

environments, particularly environments that are potentially explosive, and very high risk. Additionally, 

their ability to improve cost and efficiency by directing ventilation resources only where necessary make 

them attractive, easy to justify in terms of return on investment and are ripe for uptake in large 

underground mines. 

5. Technology transfer and leading practice from automated transportation and mining systems

especially for small operators

When the workgroup was charged there was some discussion as to why the metals mining industry was 

the focus, and the consensus was that: i) metals mining tends to move very large tonnages at a single 

operation and that, as a result, they would tend to be the first to approve capital resources toward large 
10

automation projects; ii) the metal/non-metal mining legislation in the US is less prescriptive, and in 

particular, does not have the permissibility requirements that can slow the introduction of new 

underground automation technologies, and; iii) leading practice in mining automation worldwide 

appears to be in the metals mining sector. 

In particular, as many of the automation practices discussed here come to fruition, it is critical to record 

and publish best practices, and identify practices that are easily scalable and transferable, so that 

smaller operators and other sectors can realize the health and safety benefits of mining automation. 

6. Mine design for safe automation (greenfield and brownfield)

Large metal mines tend to have long operating lives, and represent enormous investments. The 

opportunities to design a mine for automation in the next ten year are limited, but they do exist – one of 

the largest and most complex projects often cited during the workshop is the Resolution Copper Project 

near Superior, Arizona. This project represents an unparalleled case study in the design of a mine for 

automation, and, hopefully, NIOSH will be able to collect data and work closely with this operation. 

More often, however, the US industry will be faced with retrofitting equipment and sites for 

automation. Disseminating the lessons learned at these sites will represent a substantial contribution to 

the health and safety of mine workers and has the potential to improve and speed the development of 

safe autonomous mining technology. 

7. Risk Management

Risk management is the identification of hazards, the definition of potential consequences and the 

development and incorporation of controls to prevent or mitigate loss of control associated with a 

hazard. Much of the development of autonomous systems has been predicated on their use as 

preventative controls in the improvement of health and safety. However, these same controls may also 

introduce a new, and perhaps unpredicted hazard or even change the behavior of a worker. 

Existing risk management and assessment tools tend to be fairly linear, relying on a single loss of control 

not applicable to more complex automated. A critical review of current systems for risk management, 

along with gaps associated with complex systems is needed. Their most comprehensive use is likely in 

Australian mining operations – several of which also have a high degree of automation. 

8. Sensing and Situational Awareness

Sensing and situational awareness is one of the principle components of autonomous and semiautonomous systems. In the autonomous sense, there must be inputs to the systems describing the 

environment, location, presence of obstacles (human or otherwise), status of the machine, status of the 

entire system, etc. 

In the semi-autonomous system, where human interaction is a primary component of the system is to 

provide situational awareness operator, via multiple techniques, including visual, auditory, haptic, etc. 

IN addition to providing situational awareness, systems may also need to measure operator awareness 

or status. 

In a sense, situational awareness presents a paradox in terms of automation. Automation actually 

increases the need to situational awareness. They system needs inputs and the operator must be aware 
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of the environment, moving equipment, other people, but also the automation system itself – its 

operation and potential decisions.

Conclusions

Given the information presented above, and the particular strengths and position of the Office of Mine 

Safety and Health Research at NIOSH the work group suggests the following gaps may be priorities for 

NIOSH that could substantially impact the safety and health and U.S. mine workers. This list is not 

exhaustive and the work group intends that the information provided herein be used as a springboard 

toward advancing the broad aims of the organization in the area of automation.

• Track the degree of automation in various sectors of the industry, best practices, and determine

measurable impacts on health and safety.

• Assess and define appropriate limits for human operators interfacing with machines, particularly

in remote control – what are the maximum number of alarms/decisions the operator can

reasonably make, how is alertness maintained, what are appropriate strategies to provide

situational awareness, and distill data? What are strategies for addressing the change from

normal operating conditions to emergency conditions in a control room?

• Identify and study the gaps in sensing and situational awareness, developing solutions that

complement existing technology. This may include designs for providing situational awareness

to operators, new sensing devices, such as wearable sensors for mine workers, continue to

expand and build upon work in proximity detection, sensing of operator alertness and controls

for maintaining alertness and engagement.

• Research the use of unmanned vehicles for collection of environmental data that could lead to

improved health outcomes, collection of environmental data to provide improved safety

(prevention of explosive or toxic atmospheres, inspection of hazardous areas), and collection of

data during emergencies and catastrophic events.

• Identify measures of success for autonomous systems in terms of health and safety, and

disseminate standards and tools for such measures. In other words, what is the equivalent to a

lost time accident in an autonomous system? How is efficacy of such systems measured in

terms of health and safety outcomes?

• Conduct a complete review of other industries undergoing similar transformative change with

regard to autonomous systems, and identify partner offices in NIOSH, as well as partner

agencies around the world for transfer of knowledge and best use of resources.

• Identify partner operations for holistic research that characterizes the best approach for mine

site design or retrofit with regard to autonomy, and develop plans for long term projects.

• Design risk management approaches that are less linear than current approaches for use with

complex autonomous systems.

• Study how the behavior of workers in mines changes as they interact with autonomous systems.

Finally, NIOSH OMSHR has a history and reputation for developing productive relationships in all sectors 

of the industry – it is critical that this continues. The Office should consider which equipment 

manufacturers, operators and researchers are key to providing sites for work, providing case studies, 

sharing facilities and data, and work to maintain these relationships. The importance of support for the 

NIOSH OMSHR research personnel to also stay abreast of the leading edge internationally should also 

not be underestimated.
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References 

Borenstein, J., Herkert, J.R. & Miller, K.W. (2019). Self-driving cars and engineering ethics: The need for a 

system level analysis. Science and Engineering Ethics, 25: 383. https://doi.org/10.1007/s11948-

017-0006-0

Burgess-Limerick, Robin (2018.) 14 [sic] Mining Automation Research Questions in Managing Autonomy 

Risks. A presentation in the NIOSH-MSHRAC Emerging Technologies in Metals Mining Workshop. 

September 10-11, 2018, University of Colorado Anschutz Medical Campus, Aurora, CO.

Charness, N., Yoon, J. S., Souders, D., Stothart, C., & Yehnert, C. (2018). Predictors of Attitudes Toward 

Autonomous Vehicles: The Roles of Age, Gender, Prior Knowledge, and Personality. Frontiers in 

psychology, 9, 2589. doi:10.3389/fpsyg.2018.02589 

Endsley, M.R., Kaber, D., 1999. Level of automation effects on performance, situation awareness and 

workload in a dynamic control task. Ergonomics 42, 462e492.

Endsley, M.R., Kiris, E.O., 1995. The out of the loop performance and level of control in automation. J. 

Hum. Factors Ergonomics Soc. 37, 381e394.

Haight, J. M. and Kecojevic, V. (2005), Automation vs. Human intervention: What is the best fit for the 

best performance?. Proc. Safety Prog., 24: 45–51. doi:10.1002/prs.10050

Haight, J.M., Do Automated Control Systems Reduce Human Error and Incidents? Professional Safety -

The Journal of the American Society of Safety Engineers, Vol. 52, No. 5, May 2007, pp. 20 -27

Haight, J.M. and Caringi, R.G., Automation vs. Human Intervention What is the best mix for optimum 

system performance? A Case Study, International Journal of Risk Assessment and Management, 

Vol. 7, No. 5, May 2007, pp 708-721

Haight, J.M., Cole, D.G., Sejdic, E., The Optimized Design and Use of Automated Control Systems – State 

of the Literature and Proposed Research, Proceedings of the 9th International Topical Meeting 

on Nuclear Plant Instrumentation, Control, and Human Machine Interface Technologies, 

American Nuclear Society, 25 February, 2015

Hancock, P. A., Billings, D. R., Oleson, K. E., Chen, J. Y. C., de Visser, E., & Parasuraman, R. (2011). A metaanalysis of factors impacting trust in human-robot interaction. Human Factors, 53, 517–527.

Kaber, D.B., Wright, M.C. Prinzel, L.J. III, Clamann, M.P., Adaptive Automation of Human-Machine 

System Information-Processing Functions, International Journal of Cognitive Ergonomics, 2005, 

5(1), 37-57

Karnouskos, S. (2018). Self-driving car acceptance and the role of ethics. IEEE Transactions on 

Engineering Management. 1-14. doi:10.1109/TEM.2018.2877307. 

Miller C.A., and Parasuraman, R., Designing for Flexible Interactions Between Humans and Automation: 

Delegation Interfaces for Supervisory Control (2007) Human Factors, 49(1), 57-73.
13

Miller, C.A., and Parasuraman, R., Designing for Flexible Interaction Between Humans and Automation: 

Delegation Interfaces for Supervisory Control, Human Factors: The Journal of the Human Factors 

and Ergonomics Society, 2007, 49(1), 57-75

Miller, C.A., Funk, H., Wu, P., Goldman, R., Meisner, J., Chapman, M., The PlaybookTM Approach to 

Adaptive Automation, MFWGMC-HFES 2005

National Center for Statistics and Analysis. (2018, October). 2017 fatal motor vehicle crashes: Overview. 

(Traffic Safety Facts Research Note. Report No. DOT HS 812 603). Washington, DC: National 

Highway Traffic Safety Administration.

Ramsey, M. (2015, October 25) Driver videos push Tesla’s ‘Autopilot’ to its limits. Wall Street Journal. 

https://www.wsj.com/articles/driver-videos-push-teslas-autopilot-to-its-limits-1445765404

Sauer, J., Chavaillaz, A., The use of adaptable automation: Effects of extended skill lay-off and changes in 

system reliability, In Applied Ergonomics, Volume 58, 2017, Pages 471-481, ISSN 0003-6870

Sauer, J., Hockey, G.R.J., Wastell, D., Effects of training on short- and long-term skill retention in a 

complex multiple-task environment Ergon., 43 (12) (2000), pp. 2043-2064, 

10.1080/00140130010000893

Stewart, J. (2018, March 30) Tesla’s autopilot was involved in another deadly car crash. Wired. 

https://www.wired.com/story/tesla-autopilot-self-driving-crash-california/
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Appendix A

Charge Document

Metal Mining Automation and Advanced Technologies Workgroup of 

the Mine Safety and Health Research Advisory Committee 
15

Metal Mining Automation and Advanced Technologies Workgroup of the 

 Mine Safety and Health Research Advisory Committee 

PURPOSE 

This document defines the activities, membership, and administrative requirements associated 

with the establishment of the Metal Mining Automation and Advanced Technologies (MMAAT) 

Workgroup under the Mine Safety and Health Research Advisory Committee (MSHRAC), 

National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and 

Prevention (CDC). The workgroup is being established to research and discuss potential health 

and safety issues related to the implementation of automation and other advanced 

technologies in the United States (U.S.) metal mining industry. 

BACKGROUND 

The mining industry is undergoing significant changes as mining companies are looking to gain a 

competitive advantage by adopting smart technologies to decrease costs, increase efficiency, 

and improve safety. Smart technologies include technologies associated with automation and 

robotics, wireless communications, smart sensors, wearable platforms, augmentation of reality, 

interconnectivity of devices, and data analytics. In the U.S., the adoption of smart technologies 

is now growing, particularly in metal mining. The introduction of these technologies into 

mining will potentially affect worker health & safety.

DESCRIPTION OF ACTIVITIES

The Metal Mining Automation and Advanced Technologies (MMAAT) Workgroup will report to 

the MSHRAC regarding specific questions that impact potential health and safety issues related 

to the implementation of automation and other advanced technologies in U.S. metal mining. 

The workgroup is specifically tasked to draft a report for MSHRAC to review, deliberate and 

consider recommendations. Three questions have been developed to facilitate the 

workgroup’s activities: 

1. To what extent will automation and smart technologies be implemented in metal mining 

and in what timeframe?

2. What are the related emerging health & safety concerns?

3. What gaps exist in occupational health & safety research related to automation and 

smart technologies?

Research and data gathering will include facilitating a mining stakeholder meeting, open to the 

public, to discuss potential health and safety issues related to the implementation of 

automation and other advanced technologies in U.S. metal mining. 
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WORKGROUP MEMBERSHIP

The Workgroup will consist of a Designated Federal Officer (DFO), either the MSHRAC DFO or 

another CDC employee, and at a minimum, two Special Government Employee (SGE) volunteers 

from the MSHRAC. One of the MSHRAC SGEs will serve as Workgroup Chair. 

The workgroup may also consist of 1-2 ad hoc Consultants or other subject-matter experts with 

specific knowledge and experience related to automation and smart technologies. Ad hoc 

consultants only participate in the workgroup activities on an intermittent basis and are not 

permanent members. Candidates would most likely be from academia. In addition, Federal 

officials representing the Mine Safety and Health Administration; National Science Foundation; 

or National Institutes of Health as ex officio members of MSHRAC may also be members of the 

workgroup. 

DELIVERABLE

The output of the Workgroup will be a summary report based on research activities and 

information gathered during the open stakeholder meeting. The report should specifically 

address the three questions above. The report will be presented at a future MSHRAC meeting 

for discussion and potential recommendations to NIOSH to proactively address worker health 

and safety issues related to the implementation of automation and smart technologies in metal 

mining. The draft report may include draft action steps/strategies for MSHRAC to consider. 

MEETINGS, ADMINISTRATION, and TIMELINES

1. Administrative Oversight: The workgroup DFO will work with the workgroup chair, and 

the MSHRAC DFO to arrange meetings, document meeting proceedings, and report to 

the parent advisory committees on workgroup findings.

2. Meeting frequency: The workgroup will meet as often as needed to prepare for and 

organize the stakeholder meeting, and then after the stakeholder meeting as many 

times as necessary to address specified issues and to draft the summary report.

3. Meeting structure. Two MSHRAC SGEs and the workgroup DFO must be present at each 

workgroup meeting for a quorum. Meetings will occur in person or as teleconferences. 

An agenda and “read-ahead” material will be circulated 1-2 weeks prior to each 

meeting.

4. Conflicts of Interests: Non-SGE workgroup members will complete the form Conflict of 

Interest and Confidentiality Information for Workgroup Members (CDC Form 0.1473) to 

disclose interests (e.g., employment, special interests, grants, or contracts) that a 

reasonable person could view as conflicts or potential conflicts of interest with their 

committee workgroup participation. Members will also disclose any potential conflicts 

of interest before any meeting. If a workgroup member indicates a potential or actual 

conflict of interest, the DFO will advise the member to recuse from participating in 

workgroup discussions that implicate such a conflict of interest concern.

5. Timelines: The workgroup will be established at the May 22-23, 2018 MSHRAC meeting 

and should hold its first meeting within 30 days. The workgroup should plan to hold the 
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stakeholder meeting as soon as reasonable after its first meeting. The workgroup will 

provide its summary report to MSHRAC no later than December 28, 2018. 

6. Subject content: Findings and opinions of the workgroup members will be discussed at 

workgroup meetings. A summary of the workgroup’s findings will be presented to

MSHRAC for consideration for action (discussion, deliberation and decision).

7. Workgroup Meeting Summaries: Summary documents will be created for all workgroup 

meetings. Workgroup documents provided to MSHRAC for consideration and 

deliberation in a public meeting will become part of MSHRAC’s official record.

8. CDC Staff involvement: The workgroup may seek input from NIOSH subject-matter 

experts for consultation or informational presentations that contribute to the 

workgroup’s tasks. Participation by and contributions of NIOSH staff must be 

transparent and evident, to avoid the risk of, or the appearance of, undue influence that 

would compromise independence. The parent committee and workgroup DFOs will 

ensure that the workgroup work products are appropriate and not influenced by NIOSH, 

CDC or by any special interest. 

RECORDKEEPING and REPORTING

1. The workgroup chair will present meeting summaries to MSHRAC for consideration and

for determining recommendations. Approved recommendations will be included in the

MSHRAC annual report.
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Appendix B

Workshop Agenda

Emerging Technologies in Metals Mining: Health and Safety 

Implications
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Emerging Technologies in Metals Mining: Health and Safety Implications

Krugman Conference Space, University of Colorado Anschutz medical Campus

Aurora, Colorado

Workshop Agenda

Monday September 10, 2018

8:00 am – 8:15 am Welcome and Charge

Kray Luxbacher, Virginia Tech

Josh Scott, Colorado School of Public Health

8:15 am – 9:00 am Case Study I – State of Art Autonomous Haulage in Australia 

Robin Burgess-Limerick, University of Queensland

9:00 am – 9:45 am Case Study II – Autonomy and Sensing Underground

Jacob Rukavina and Mark Harris, Rio Tinto

9:45 am – 10:00 am BREAK

10:00 am – 10:45 am Case Study III – Autonomous and Semi-Autonomous Systems for 

Surface Mining

Ron Crawford, Komatsu

Bill Nassauer, Modular Mining Systems

10:45 am – 11:30 am Case Study Panel Discussion

11:30 am – 12:30 pm LUNCH

12:30 pm – 1:45 pm Human Factors Session

Joel Haight, University of Pittsburgh

Discussion

1:45 pm – 2:45 pm Sensor Technology Session

Rob Bush, Howden Simsmart

Moe Momayez, University of Arizona

Discussion

2:45 pm – 3:00 pm BREAK

4:00 pm – 5:00 pm Health and Safety Issues and Gaps Discussion

Moderated by Jeff Burgess, University of Arizona

Tuesday, September 11, 2018

8:00 am – 9:20 am Risk Management and Assessment

Rob McLain, Freeport McMoRan

Robin Burgess-Limerick, University of Queensland

Discussion

9:15 am to 10:15 am Data Analytics

Pratt Rogers, University of Utah

Craig Ross, Hexagon Mining 

Discussion

10:15 am – 10:30 am BREAK

10:30 am – 11:30 am Health and Safety Issues and Gaps Discussion

Moderated by Jeff Burgess, University of Arizona

Kyle Zimmer, International Union of Operating Engineers

11:30 am – 11:45 am Closing Remarks and Adjourn

Kray Luxbacher, Virginia Tech
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