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Human performance data collected in a virtual environment.

Mashrura Musharraf1, Jennifer Smith1, Faisal Khan1

  • 1Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St John's, Newfoundland and Labrador, Canada A1B 3X5.

Data in Brief
|October 13, 2017
PubMed
Summary
This summary is machine-generated.

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This study presents human performance data from a virtual environment, analyzing how training levels and scenario complexity affect human reliability in safety-critical tasks. Findings inform human factors research.

Area of Science:

  • Human Factors and Ergonomics
  • Cognitive Psychology
  • Safety Engineering

Background:

  • Human reliability analysis (HRA) is crucial for safety in complex systems.
  • Existing HRA methods often overlook individual differences in human performance.
  • Virtual environments offer a controlled setting to study human behavior and reliability.

Purpose of the Study:

  • To provide a dataset on human performance in a virtual environment.
  • To investigate the impact of varying training levels and scenario complexities on human reliability.
  • To extend virtual experimental techniques for HRA by incorporating individual differences.

Main Methods:

  • Collected human performance data from 36 participants in a virtual environment.
  • Assigned participants to two training groups: high (G1) and low (G2).

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  • Tested participants across 4 virtual scenarios with varying visibility and complexity, recording key performance metrics.

    • Main Results:

    • Recorded metrics include time to muster, time spent running, interactions with safety equipment (doors), hazard encounters, and reporting accuracy.
    • Performance data varied based on training group and scenario characteristics.
    • The dataset allows for detailed analysis of individual differences in human performance.

    Conclusions:

    • The collected data serves as a valuable resource for HRA research.
    • Understanding individual differences is key to improving the accuracy of reliability predictions.
    • Virtual environments are effective tools for collecting nuanced human performance data.