A Neurophysiological Sensor Suite for Real-Time Prediction of Pilot Workload in Operational Settings

Trevor Grant, Kaunil Dhruv, Lucca Eloy, Lucas Hayne, Kevin Durkee, Leanne Hirshfield

Research output: Chapter in Book/Entry/PoemConference contribution


In recent years, research involving the use of neurophysiological sensor streams to quantitatively measure and predict the level of mental workload experienced by an individual user has gained momentum as the complexity of the tasks operators have experienced in heavily computerized contexts has continued to expand. Despite the promising results from many empirical studies reporting successful classification of workload using neurophysiological sensor data, accurate classification of workload in real-time remains a largely unsolved problem. This research aims to both introduce and examine the efficacy of a new research tool: Tools for Object Measurement and Evaluation (TOME). The TOME system is a toolset for collating and examining neurophysiological data in real time. Following a presentation of the system, and the problems the system may help to solve, a validation study using the TOME system is presented.

Original languageEnglish (US)
Title of host publicationHCI International 2020 – Late Breaking Papers
Subtitle of host publicationCognition, Learning and Games - 22nd HCI International Conference, HCII 2020, Proceedings
EditorsConstantine Stephanidis, Don Harris, Wen-Chin Li, Dylan D. Schmorrow, Cali M. Fidopiastis, Panayiotis Zaphiris, Andri Ioannou, Andri Ioannou, Xiaowen Fang, Robert A. Sottilare, Jessica Schwarz
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages18
ISBN (Print)9783030601270
StatePublished - 2020
Event22nd International Conference on Human-Computer Interaction,HCII 2020 - Copenhagen, Denmark
Duration: Jul 19 2020Jul 24 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12425 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Human-Computer Interaction,HCII 2020


  • Data acquisition
  • Mental workload
  • Physiological sensors

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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