Application of Artificial Neural Networks for Air Traffic Controller Functional State Classification

Abstract

Determining operator cognitive or functional state is a critical component of adaptive aiding systems. To determine cognitive state, we must decide which measured features will assist in distinguishing different levels of mental activity. Psychophysiological signals were collected for two levels of cognitive workload from which 43 measures were derived. Three feature reduction methods were applied, and the results were used as inputs to an artificial neural network for training and classification. Average classification accuracies up to 89.7% were achieved and the number of input features required was reduced by up to 84 percent.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2001
Accession Number
ADA404631

Entities

People

  • Chris A. Russell
  • Glenn F. Wilson

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Air Force Research Laboratories
  • Air Traffic
  • Air Traffic Controllers
  • Algorithms
  • Classification
  • Cognitive Workload
  • Data Sets
  • Feature Selection
  • Machine Learning
  • Network Architecture
  • Neural Networks
  • Psychology
  • Signal Processing
  • Systems Engineering
  • Workload

Fields of Study

  • Agricultural and Food sciences

Readers

  • Instructional Design and Training Evaluation.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks