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Related Experiment Video

Updated: Jul 20, 2026

Virtual Reality Experiments with Physiological Measures

Virtual Reality Experiments with Physiological Measures

Published on: August 29, 2018

On accident causation models, safety training and virtual reality.

Krantiraditya Dhalmahapatra¹, Souvik Das¹, J Maiti¹

¹Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, India.

International Journal of Occupational Safety and Ergonomics : JOSE

|May 7, 2020

Summary

This study introduces a virtual reality-based accident causation model (VR-ACM) to improve real-time hazard identification and safety training. The VR-ACM enhances accident analysis and decision-making for crane operators.

Keywords:

accident causation hazard identification safety training virtual prototype virtual reality

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Last Updated: Jul 20, 2026

Virtual Reality Experiments with Physiological Measures

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Area of Science:

Occupational Safety and Health
Human-Computer Interaction
Virtual Reality Applications

Background:

Traditional accident causation models (ACMs) lack real-time visualization and user interaction capabilities.
Existing safety training practices often do not adequately address dynamic hazard identification and prevention.
Inefficiencies in current models hinder effective accident analysis and real-time decision-making.

Purpose of the Study:

To develop a dynamic, virtual reality-based accident causation model (VR-ACM).
To address the limitations of traditional ACMs in real-time hazard identification and interactive safety training.
To propose a novel VR-ACM for enhanced accident analysis and operator training.

Main Methods:

Conducted a comprehensive literature review of existing ACMs and safety training practices.
Identified limitations in current models and training methodologies.
Proposed a VR-ACM framework with three integrated modules: VR modelling/simulation, accident causation, and safety training.

Main Results:

An experimental study with 22 crane operators demonstrated the applicability of the proposed VR-ACM.
The VR-ACM facilitates three-dimensional visual analysis of potential accident causes.
The model enables real-time user interaction, judgment, and decision-making.

Conclusions:

The proposed VR-ACM effectively overcomes the limitations of traditional models.
VR-ACM offers a dynamic platform for analyzing accident causation and improving safety training.
The model supports real-time hazard identification, accident prevention, and interactive learning environments.