Automatic Multimodal Cognitive Load Measurement (AMCLM)

Abstract

This report summarizes the research activities, results of the user studies, and research accomplishments out of the AMCLM project in the past year. We investigated the validity of using speech formants and their fusion to measure cognitive load automatically. For the research on eye-activity based cognitive load measurement, we had examined various features, including blink latency, fixation time, saccade speed and pupil size. We further investigated the use of pupil size for automatic classification of cognitive load in different luminance conditions and under various emotional stimuli. All together, we had carried out four sets of user experiments to validate the research outcomes in a range of task scenarios, including Stroop test, computer-based basketball training, and mental arithmetic (summation) tasks.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2011
Accession Number
ADA547654

Entities

People

  • Fang Chen

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Bayesian Networks
  • Cameras
  • Cognition
  • Cognitive Systems Engineering
  • Cognitive Workload
  • Computers
  • Data Fusion
  • Human-Computer Interaction
  • Human-Machine Interaction
  • Information Processing
  • Information Science
  • Machine Learning
  • Measurement
  • Nervous System
  • Neural Networks
  • Psychology
  • Training

Readers

  • Psychometric Testing or Psychological Assessment.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.