5
equipment, while with higher cognitive abilities maybe able
to more effectively perform in design and analysis units.
This ranking of trainees can help the organizations achieve
higher efficiencies with reduced accidents. Ultimately, these
improvements are projected to increase safety outcomes and
foster a stronger culture of safety and preparedness within
the mining industry.
CONCLUSION AND DISCUSSION
This research proposes Adaptive Immersive Training
Framework (AITF) to improve self-escape readiness and
emergency preparedness for underground miners. The
AITF aims to bridge the gap between traditional train-
ing methods and the evolving demands of high-risk min-
ing environments by integrating immersive simulations,
eye-tracking, physiological monitoring, and personalized
adaptive learning. The proposed framework builds on best
practices from other high-risk industries, such as aviation,
medical surgery, and military training, which have success-
fully utilized simulation-based tools to enhance situational
awareness, decision-making, and overall performance
under stress.
Traditional training methods, while valuable, have lim-
itations in terms of cost, accessibility, and the ability to rep-
licate real-world conditions. In contrast, the AITF leverages
immersive technology to simulate complex mine environ-
ments, providing trainees with opportunities to experience
and react to emergencies in a controlled, risk-free setting.
Furthermore, the ability to measure and analyze cognitive
and behavioral responses during these simulations may
open new avenues for refining training techniques and
improving safety outcomes.
While simulation-based training has gained traction in
industries such as aviation and healthcare, its adoption in
mining remains limited. Practical considerations, includ-
ing the fidelity of simulations, the realism of scenarios, and
the design of feedback mechanisms, pose challenges that
the AITF seeks to address. While high-fidelity simulations
offer a closer replica of the physical, operational, and psy-
chological conditions of underground mines, their design,
costs, and training effectiveness remain a concern. Research
has shown where the low fidelity simulators offer similar or
superior training effectiveness in certain situations.
Another area for further exploration is the integration
of additional technologies, such as artificial intelligence (AI)
and machine learning (ML), into the training framework.
These technologies could enhance the adaptiveness of the
training system by providing real-time, data-driven insights
into the trainee’s learning progression, enabling even more
precise adjustments to training scenarios and interventions.
Additionally, expanding the scope of the training to include
a wider range of emergency scenarios—such as gas leaks,
cave-ins, or fires—could further enhance the realism and
effectiveness of the training experience.
In conclusion, the AITF can potentially offer a promis-
ing new direction for miner self-escape training, combining
advanced simulation technologies with personalized, adap-
tive learning strategies. By improving situational awareness,
decision-making, and stress management, this framework
has the potential to significantly enhance emergency pre-
paredness in the mining industry. With continued research
and development, the AITF could play a pivotal role in
reducing accidents and fatalities in underground mining,
ultimately contributing to a safer, more resilient workforce.
REFERENCES
[1] Bergamo, P. A. de S., Streng, E. S., de Carvalho,
M. A., Rosenkranz, J., &Ghorbani, Y. (2022).
Simulation-based training and learning: A review
on technology-enhanced education for the minerals
industry. Minerals Engineering, 175, 107272. DOI:
10.1016/j.mineng.2021.107272.
[2] Endsley, M. R. (1988). Design and evaluation for
situation awareness enhancement. Proceedings of
the Human Factors Society Annual Meeting, 32(2),
97–101.
[3] Gao, Y., Gonzalez, V. A., &Yiu, T. W. (2019). The
effectiveness of traditional tools and computer-aided
technologies for health and safety training in the
construction sector: A systematic review. Computers
&Education, 138, 101–115. DOI: 10.1016/j.
compedu.2019.05.003.
[4] Grabowski, A. (2021). Practical skills training in
enclosure fires: An experimental study with cadets
and firefighters using CAVE and HMD-based virtual
training simulators. Fire Safety Journal, 125, 103440.
DOI: 10.1016/j.firesaf.2021.103440.
[5] Helfrich, W. (2023). Usability of Collaborative “VR
Mine Rescue Training” Platform.
[6] Hogan, R. A., Conrad, P. W., Hart, J., &Rosenthal,
S. (2023). Improving Mine Health And Safety Training
Through The Development of Montana Technological
University’s Mine Health And Safety Training Program
-SME Annual Conference 2023.
[7] Isleyen, E., &Duzgun, H. S. (2019). Use of virtual
reality in underground roof fall hazard assessment
and risk mitigation. International Journal of Mining
Science and Technology, 29(4), 603–607. DOI:
10.1016/j.ijmst.2019.06.003.
equipment, while with higher cognitive abilities maybe able
to more effectively perform in design and analysis units.
This ranking of trainees can help the organizations achieve
higher efficiencies with reduced accidents. Ultimately, these
improvements are projected to increase safety outcomes and
foster a stronger culture of safety and preparedness within
the mining industry.
CONCLUSION AND DISCUSSION
This research proposes Adaptive Immersive Training
Framework (AITF) to improve self-escape readiness and
emergency preparedness for underground miners. The
AITF aims to bridge the gap between traditional train-
ing methods and the evolving demands of high-risk min-
ing environments by integrating immersive simulations,
eye-tracking, physiological monitoring, and personalized
adaptive learning. The proposed framework builds on best
practices from other high-risk industries, such as aviation,
medical surgery, and military training, which have success-
fully utilized simulation-based tools to enhance situational
awareness, decision-making, and overall performance
under stress.
Traditional training methods, while valuable, have lim-
itations in terms of cost, accessibility, and the ability to rep-
licate real-world conditions. In contrast, the AITF leverages
immersive technology to simulate complex mine environ-
ments, providing trainees with opportunities to experience
and react to emergencies in a controlled, risk-free setting.
Furthermore, the ability to measure and analyze cognitive
and behavioral responses during these simulations may
open new avenues for refining training techniques and
improving safety outcomes.
While simulation-based training has gained traction in
industries such as aviation and healthcare, its adoption in
mining remains limited. Practical considerations, includ-
ing the fidelity of simulations, the realism of scenarios, and
the design of feedback mechanisms, pose challenges that
the AITF seeks to address. While high-fidelity simulations
offer a closer replica of the physical, operational, and psy-
chological conditions of underground mines, their design,
costs, and training effectiveness remain a concern. Research
has shown where the low fidelity simulators offer similar or
superior training effectiveness in certain situations.
Another area for further exploration is the integration
of additional technologies, such as artificial intelligence (AI)
and machine learning (ML), into the training framework.
These technologies could enhance the adaptiveness of the
training system by providing real-time, data-driven insights
into the trainee’s learning progression, enabling even more
precise adjustments to training scenarios and interventions.
Additionally, expanding the scope of the training to include
a wider range of emergency scenarios—such as gas leaks,
cave-ins, or fires—could further enhance the realism and
effectiveness of the training experience.
In conclusion, the AITF can potentially offer a promis-
ing new direction for miner self-escape training, combining
advanced simulation technologies with personalized, adap-
tive learning strategies. By improving situational awareness,
decision-making, and stress management, this framework
has the potential to significantly enhance emergency pre-
paredness in the mining industry. With continued research
and development, the AITF could play a pivotal role in
reducing accidents and fatalities in underground mining,
ultimately contributing to a safer, more resilient workforce.
REFERENCES
[1] Bergamo, P. A. de S., Streng, E. S., de Carvalho,
M. A., Rosenkranz, J., &Ghorbani, Y. (2022).
Simulation-based training and learning: A review
on technology-enhanced education for the minerals
industry. Minerals Engineering, 175, 107272. DOI:
10.1016/j.mineng.2021.107272.
[2] Endsley, M. R. (1988). Design and evaluation for
situation awareness enhancement. Proceedings of
the Human Factors Society Annual Meeting, 32(2),
97–101.
[3] Gao, Y., Gonzalez, V. A., &Yiu, T. W. (2019). The
effectiveness of traditional tools and computer-aided
technologies for health and safety training in the
construction sector: A systematic review. Computers
&Education, 138, 101–115. DOI: 10.1016/j.
compedu.2019.05.003.
[4] Grabowski, A. (2021). Practical skills training in
enclosure fires: An experimental study with cadets
and firefighters using CAVE and HMD-based virtual
training simulators. Fire Safety Journal, 125, 103440.
DOI: 10.1016/j.firesaf.2021.103440.
[5] Helfrich, W. (2023). Usability of Collaborative “VR
Mine Rescue Training” Platform.
[6] Hogan, R. A., Conrad, P. W., Hart, J., &Rosenthal,
S. (2023). Improving Mine Health And Safety Training
Through The Development of Montana Technological
University’s Mine Health And Safety Training Program
-SME Annual Conference 2023.
[7] Isleyen, E., &Duzgun, H. S. (2019). Use of virtual
reality in underground roof fall hazard assessment
and risk mitigation. International Journal of Mining
Science and Technology, 29(4), 603–607. DOI:
10.1016/j.ijmst.2019.06.003.