Title
Comparison Of Multiple Physiological Sensors To Classify Operator State In Adaptive Automation Systems
Abstract
Automating tasks alleviates operator resources to be delegated to other demands, but the cost is often situation awareness. In contrast, complete manual control of a system opens the door for greater human error. Therefore, an ideal situation would require the development of an adaptive system in which automation can be triggered based on performance of a particular task, time spent on the task, or perhaps physiological response. The latter pertains to the goal for this particular study. Electroencephalogram (EEG), electrocardiogram (ECG), and eye tracking measures were recorded during six multi-tasking scenarios to assess if any one single measure is best suited for future implementation as an automation invocation. EEG showed the greatest potential for that purpose. Copyright 2010 by Human Factors and Ergonomics Society, Inc. All rights reserved.
Publication Date
12-1-2010
Publication Title
Proceedings of the Human Factors and Ergonomics Society
Volume
1
Number of Pages
195-199
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1518/107118110X12829369200396
Copyright Status
Unknown
Socpus ID
79952953252 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/79952953252
STARS Citation
Taylor, Grant; Reinerman-Jones, Lauren; Cosenzo, Keryl; and Nicholson, Denise, "Comparison Of Multiple Physiological Sensors To Classify Operator State In Adaptive Automation Systems" (2010). Scopus Export 2010-2014. 437.
https://stars.library.ucf.edu/scopus2010/437