Improving anomaly detection through identification of physiological signatures of unconscious awareness

Alyssa M. Piasecki; Mary E. Fendley; Rik Warren

doi:10.1007/978-3-319-41956-5_23

Improving anomaly detection through identification of physiological signatures of unconscious awareness

Alyssa M. Piasecki
, Mary E. Fendley
, Rik Warren

Wright State University
Sensors Directorate Air Force Research Laboratory Wright Patterson AFB

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	Advances in Human Factors and System Interactions - Proceedings of the AHFE International Conference on Human Factors and System Interactions, 2016
Editors	Isabel L. Nunes
Publisher	Springer Verlag
Pages	259-269
Number of pages	11
ISBN (Print)	9783319419558
DOIs	https://doi.org/10.1007/978-3-319-41956-5_23
State	Published - 2017
Externally published	Yes
Event	International Conference on Human Factors and System Interactions, 2016 - Walt Disney World, United States Duration: Jul 27 2016 → Jul 31 2016

Publication series

Series	Advances in Intelligent Systems and Computing
Volume	497
ISSN	2194-5357

Conference

Conference	International Conference on Human Factors and System Interactions, 2016
Country/Territory	United States
City	Walt Disney World
Period	7/27/16 → 7/31/16

ASJC Scopus Subject Areas

Control and Systems Engineering
General Computer Science

Keywords

Anomaly response
Eye-tracking
Unconscious detection
Visual search task

Access to Document

10.1007/978-3-319-41956-5_23

Cite this

Piasecki, A. M., Fendley, M. E., & Warren, R. (2017). Improving anomaly detection through identification of physiological signatures of unconscious awareness. In I. L. Nunes (Ed.), Advances in Human Factors and System Interactions - Proceedings of the AHFE International Conference on Human Factors and System Interactions, 2016 (pp. 259-269). Springer Verlag. https://doi.org/10.1007/978-3-319-41956-5_23

Improving anomaly detection through identification of physiological signatures of unconscious awareness. / Piasecki, Alyssa M.; Fendley, Mary E.; Warren, Rik.
Advances in Human Factors and System Interactions - Proceedings of the AHFE International Conference on Human Factors and System Interactions, 2016. ed. / Isabel L. Nunes. Springer Verlag, 2017. p. 259-269 (Advances in Intelligent Systems and Computing, Vol. 497).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Piasecki, AM, Fendley, ME & Warren, R 2017, Improving anomaly detection through identification of physiological signatures of unconscious awareness. in IL Nunes (ed.), Advances in Human Factors and System Interactions - Proceedings of the AHFE International Conference on Human Factors and System Interactions, 2016. Springer Verlag, Advances in Intelligent Systems and Computing, vol. 497, pp. 259-269, International Conference on Human Factors and System Interactions, 2016, Walt Disney World, United States, 7/27/16. https://doi.org/10.1007/978-3-319-41956-5_23

Piasecki AM, Fendley ME, Warren R. Improving anomaly detection through identification of physiological signatures of unconscious awareness. In Nunes IL, editor, Advances in Human Factors and System Interactions - Proceedings of the AHFE International Conference on Human Factors and System Interactions, 2016. Springer Verlag. 2017. p. 259-269. (Advances in Intelligent Systems and Computing, Vol. 497). doi: 10.1007/978-3-319-41956-5_23

Piasecki, Alyssa M. ; Fendley, Mary E. ; Warren, Rik. / Improving anomaly detection through identification of physiological signatures of unconscious awareness. Advances in Human Factors and System Interactions - Proceedings of the AHFE International Conference on Human Factors and System Interactions, 2016. editor / Isabel L. Nunes. Springer Verlag, 2017. pp. 259-269 (Advances in Intelligent Systems and Computing, Vol. 497).

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Improving anomaly detection through identification of physiological signatures of unconscious awareness

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this