A Neurophysiological Sensor Suite for Real-Time Prediction of Pilot Workload in Operational Settings
| Autor: | Lucas Hayne, Kevin Durkee, Lucca Eloy, Kaunil Dhruv, Trevor Grant, Leanne M. Hirshfield |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Measure (data warehouse)
Computer science business.industry Suite 05 social sciences Workload Real time prediction Neurophysiology Machine learning computer.software_genre Object (computer science) 03 medical and health sciences 0302 clinical medicine Data acquisition Empirical research 0501 psychology and cognitive sciences Artificial intelligence business computer 050107 human factors 030217 neurology & neurosurgery |
| Zdroj: | HCI International 2020 – Late Breaking Papers: Cognition, Learning and Games ISBN: 9783030601270 HCI (43) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|