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Supervised Classification of Operator Functional State Based on Physiological Data: Application to Drones Swarm

Alexandre Kostenko1, Philippe Rauffet2,3, Gilles Coppin1

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This summary is machine-generated.

This study introduces Operator Functional State (OFS) assessment to enhance safety in demanding missions. Machine learning models predict operator performance levels using physiological and contextual data for adaptive human-machine cooperation.

Keywords:
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Area of Science:

  • Human-Computer Interaction
  • Cognitive Science
  • Operator Performance Monitoring

Background:

  • Dynamic adaptation of human-machine cooperation is crucial for safety and performance in high-risk missions.
  • Assessing an operator's ability to perform is key for reconfigurable cooperation systems.
  • Operator Functional State (OFS) is a critical concept for understanding operator capacity.

Purpose of the Study:

  • To operationalize the concept of Operator Functional State (OFS).
  • To develop a method for online assessment of operator functional state.
  • To enable adaptive countermeasures in demanding operational environments.

Main Methods:

  • Exploration of the Operator Functional State (OFS) concept.
  • Integration of contextual and physiological indicators for OFS assessment.
  • Application of supervised learning algorithms (SVM, k-NN, Random Forest) for classification.

Main Results:

  • Physiological and contextual data were classified into three distinct levels of Operator Functional State (OFS).
  • The proposed method demonstrated the feasibility of online OFS assessment.
  • Machine learning models successfully differentiated between operator states.

Conclusions:

  • The developed OFS classification system can support dynamic adaptation of human-machine cooperation.
  • This approach aids in implementing timely countermeasures to maintain operator performance.
  • The findings contribute to improving safety and efficiency in complex operational tasks like drone swarm monitoring.