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When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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Experiment data: Human-in-the-loop decision support in process control rooms.

Chidera Winifred Amazu1,2, Joseph Mietkiewicz2,3, Ammar N Abbas2,4

  • 1Politecnico di Torino, Corso, Duca degli Abruzzi, 24, Turin 10129, Italy.

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|March 5, 2024
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Summary
This summary is machine-generated.

This study analyzed multi-modal data from 92 participants in a simulated formaldehyde plant, evaluating decision support tools to enhance operator performance and safety in control rooms.

Keywords:
BiometricsDecision supportDesign of experimentHuman–machine interactionProcess industrySafetySimulated studySurveys

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Engineering
  • Process Safety

Background:

  • Cognitive states like workload, situational awareness, stress, and fatigue are critical in industrial control room operations.
  • Existing multi-modal data collection methods provide insights into operator performance.
  • Human-in-the-loop systems require effective decision support tools for optimal operator functioning.

Purpose of the Study:

  • To collect and analyze multi-modal data assessing operator performance under varying decision support conditions.
  • To evaluate the impact of different decision support tools on cognitive states and task execution.
  • To provide a dataset for optimizing control room design and validating new safety solutions.

Main Methods:

  • Collected objective (eye-tracking, EEG, Health Monitoring Watch) and subjective (NASA-TLX, SART, surveys) data from 92 participants.
  • Participants engaged in a simulated formaldehyde production plant control task with alarm handling and process control.
  • Tested combinations of decision support tools: alarm prioritization, digital vs. paper procedures, and AI recommendations.

Main Results:

  • Statistical analysis performed to compare outcomes across four participant groups with different decision support tool configurations.
  • Identified impacts of decision support tools on operator performance and cognitive states.
  • Dataset enables comparison of current industry practices against proposed solutions.

Conclusions:

  • The collected dataset is valuable for understanding operator performance in complex industrial environments.
  • Findings can inform control room design, optimization, and the development of enhanced safety protocols.
  • Applicable for process safety, system, and human factors engineers, as well as researchers in related fields.