2
The framework integrates cutting-edge self-escape technol-
ogies and immersive, scenario-based training modules to
prepare miners for the challenges of high-stress situations
in hazardous environments.
LITERATURE SEARCH
The mining industry remains one of the most hazard-
ous industries worldwide, with accidents continuing to
claim lives despite ongoing safety efforts. In 2023 alone,
40 fatalities were reported, 26 of which can be attributed
to machinery-related incidents (MSHA, 2024). It is esti-
mated, from the analysis of accident reports, that over
50% of equipment-related fatalities could be preventable
through the use of autonomy and advanced technologies.
As a result, there is no surprise that the industry is gradually
transitioning toward greater automation and the develop-
ment of technologies that aid miners during emergencies,
particularly for effective escape.
Like other high-risk industries, mining must comply
with stringent occupational health and safety standards.
The sector continuously strives to reduce fatalities through
training, which is fundamental for safety in hazardous
workplaces (U.S. Department of Labor, 2010). However,
mining largely relies on traditional approaches to health
and safety training, which are increasingly impractical due
to high costs, operational constraints, and limited effective-
ness (Gao et al., 2019).
Advances in computational and visualization technolo-
gies, combined with improved affordability, have driven
a shift across many industries toward technology-aided
training methods. Immersive environments, virtual real-
ity (VR), augmented reality (AR), and simulators are now
commonly used for health and safety training in high-risk
fields such as aviation, surgery, energy extraction, oil and
gas, construction, and the military. While simulation-based
training is gaining traction in the mining sector, it remains
underutilized compared to these other industries (Isleyen &
Duzgun, 2019 Tichon &Burgess-Limerick, 2011).
Immersive Training in Mining
Discussions around immersive training approaches in
mining have spanned more than two decades, yet adop-
tion remains slow compared to similarly complex and
high-risk industries (Helfrich, 2023 Hogan et al., 2023
McMlarnon &Denby, 1996 Squelch, 2001 Tavoularis &
Wiehagen, 1983). Early work, such as Kizil &Joy (2001),
highlighted the potential benefits of VR for safety train-
ing in the minerals sector and predicted its widespread use
in mining. Despite this early promise, immersive training
remained on the periphery of mining practices until more
recent years, where with the invent of modern technologies,
and automation made it more practical to utilize immer-
sive environments for training. Today, there exist a large
number of commercially available simulators focusing on
equipment operations and efficiency. However, the use of
simulators for health and safety is limited and is absent for
self-escape and evacuation training.
Tichon &Burgess-Limerick (2011) reviewed the appli-
cation of VR for safety-related training in mining, empha-
sizing its potential but also highlighting gaps in adoption.
Li et al. (2019) explored the use of advanced quantita-
tive methods, such as factor analysis and structural equa-
tion modeling, to inform training for health and safety in
mining. More recently, Bergamo et al. (2022) reviewed
simulation-based and technology-enhanced educational
approaches for operators in the mineral industry, identify-
ing promising directions for future adoption.
Specific examples of immersive tools include Isleyen &
Duzgun’s (2019) use of simulators to assess and mitigate roof
fall hazards in underground excavations and Grabowski’s
(2021) comparative study of firefighting training for mili-
tary cadets and firefighters using the Cave Automated
Virtual Environment (CAVE) and head-mounted display
(HMD) units. The latter demonstrated superior results for
immersive approaches. Xu et al. (2014) presented smoke
scenarios using simulators for emergency training, provid-
ing another example of practical VR applications.
While immersive training has seen greater adoption in
industries outside mining, such as oil and gas, substantial
opportunities remain to leverage VR and similar technolo-
gies for health and safety training within mining. These
approaches offer the potential to bridge critical gaps in pre-
paredness and performance.
Situational Awareness
Situational awareness (SA) refers to understanding, com-
prehension, predictions of a person about its surrounding
environment. Situational awareness (SA) is a critical skill
for effective decision-making in high-risk environments.
Endsley (1988) formally defined SA as a three-level process
encompassing (1) perception of environmental elements,
(2) comprehension of their meaning, and (3) projection
of their future status. Together these describe the effective
functioning of a person in the working environments where
the higher levels being more critical in complex environ-
ments (Endsley, 1988).
In simulation-based training environments, Raza
et al. (2019) measured SA among offshore drillers, find-
ing significant differences in performance linked to SA
levels. Similarly, Naqvi et al. (2018) identified notable
The framework integrates cutting-edge self-escape technol-
ogies and immersive, scenario-based training modules to
prepare miners for the challenges of high-stress situations
in hazardous environments.
LITERATURE SEARCH
The mining industry remains one of the most hazard-
ous industries worldwide, with accidents continuing to
claim lives despite ongoing safety efforts. In 2023 alone,
40 fatalities were reported, 26 of which can be attributed
to machinery-related incidents (MSHA, 2024). It is esti-
mated, from the analysis of accident reports, that over
50% of equipment-related fatalities could be preventable
through the use of autonomy and advanced technologies.
As a result, there is no surprise that the industry is gradually
transitioning toward greater automation and the develop-
ment of technologies that aid miners during emergencies,
particularly for effective escape.
Like other high-risk industries, mining must comply
with stringent occupational health and safety standards.
The sector continuously strives to reduce fatalities through
training, which is fundamental for safety in hazardous
workplaces (U.S. Department of Labor, 2010). However,
mining largely relies on traditional approaches to health
and safety training, which are increasingly impractical due
to high costs, operational constraints, and limited effective-
ness (Gao et al., 2019).
Advances in computational and visualization technolo-
gies, combined with improved affordability, have driven
a shift across many industries toward technology-aided
training methods. Immersive environments, virtual real-
ity (VR), augmented reality (AR), and simulators are now
commonly used for health and safety training in high-risk
fields such as aviation, surgery, energy extraction, oil and
gas, construction, and the military. While simulation-based
training is gaining traction in the mining sector, it remains
underutilized compared to these other industries (Isleyen &
Duzgun, 2019 Tichon &Burgess-Limerick, 2011).
Immersive Training in Mining
Discussions around immersive training approaches in
mining have spanned more than two decades, yet adop-
tion remains slow compared to similarly complex and
high-risk industries (Helfrich, 2023 Hogan et al., 2023
McMlarnon &Denby, 1996 Squelch, 2001 Tavoularis &
Wiehagen, 1983). Early work, such as Kizil &Joy (2001),
highlighted the potential benefits of VR for safety train-
ing in the minerals sector and predicted its widespread use
in mining. Despite this early promise, immersive training
remained on the periphery of mining practices until more
recent years, where with the invent of modern technologies,
and automation made it more practical to utilize immer-
sive environments for training. Today, there exist a large
number of commercially available simulators focusing on
equipment operations and efficiency. However, the use of
simulators for health and safety is limited and is absent for
self-escape and evacuation training.
Tichon &Burgess-Limerick (2011) reviewed the appli-
cation of VR for safety-related training in mining, empha-
sizing its potential but also highlighting gaps in adoption.
Li et al. (2019) explored the use of advanced quantita-
tive methods, such as factor analysis and structural equa-
tion modeling, to inform training for health and safety in
mining. More recently, Bergamo et al. (2022) reviewed
simulation-based and technology-enhanced educational
approaches for operators in the mineral industry, identify-
ing promising directions for future adoption.
Specific examples of immersive tools include Isleyen &
Duzgun’s (2019) use of simulators to assess and mitigate roof
fall hazards in underground excavations and Grabowski’s
(2021) comparative study of firefighting training for mili-
tary cadets and firefighters using the Cave Automated
Virtual Environment (CAVE) and head-mounted display
(HMD) units. The latter demonstrated superior results for
immersive approaches. Xu et al. (2014) presented smoke
scenarios using simulators for emergency training, provid-
ing another example of practical VR applications.
While immersive training has seen greater adoption in
industries outside mining, such as oil and gas, substantial
opportunities remain to leverage VR and similar technolo-
gies for health and safety training within mining. These
approaches offer the potential to bridge critical gaps in pre-
paredness and performance.
Situational Awareness
Situational awareness (SA) refers to understanding, com-
prehension, predictions of a person about its surrounding
environment. Situational awareness (SA) is a critical skill
for effective decision-making in high-risk environments.
Endsley (1988) formally defined SA as a three-level process
encompassing (1) perception of environmental elements,
(2) comprehension of their meaning, and (3) projection
of their future status. Together these describe the effective
functioning of a person in the working environments where
the higher levels being more critical in complex environ-
ments (Endsley, 1988).
In simulation-based training environments, Raza
et al. (2019) measured SA among offshore drillers, find-
ing significant differences in performance linked to SA
levels. Similarly, Naqvi et al. (2018) identified notable