Big data analytics for continuous assessment of astronaut health risk and its application to human-in-the-loop (HITL) related aerospace

McGregor A. M., C; Orlov, O; Baevsky, R; Chernikova, A; Rusanov, V

Big data analytics for continuous assessment of astronaut health risk and its application to human-in-the-loop (HITL) related aerospace

McGregor A. M., C

Orlov, O Baevsky, R Chernikova, A Rusanov, V

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Publication Type:: Conference Proceeding
Citation:: 19th AIAA Non-Deterministic Approaches Conference, 2017, 2017
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	McGregor A. M., C https://orcid.org/0000-0002-0491-4403	en_US
dc.contributor.author	Orlov, O	en_US
dc.contributor.author	Baevsky, R	en_US
dc.contributor.author	Chernikova, A	en_US
dc.contributor.author	Rusanov, V	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	19th AIAA Non-Deterministic Approaches Conference, 2017, 2017	en_US
dc.identifier.isbn	9781624104527	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136926
dc.description.abstract	© 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. The man-instrumentation-equipment-vehicle-environment ecosystem is complex in aerospace missions. Health status of the individual has important implications on decision making and performance that should be factored into assessments for probability of success/risk of failure both in offline and real-time models. To date probabilistic models have not considered the dynamic nature of health status. Big Data analytics is enabling new forms of analytics to assess health status in real-time. There is great potential to integrate dynamic health status information with platforms assessing risk and the probability of success for dynamic individualized real-time probabilistic predictive risk assessment. In this research we present an approach utilizing Big Data analytics to enable continuous assessment of astronaut health risk and show its implications for integration with HITL related aerospace mission.	en_US
dc.relation.ispartof	19th AIAA Non-Deterministic Approaches Conference, 2017	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Big data analytics for continuous assessment of astronaut health risk and its application to human-in-the-loop (HITL) related aerospace	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. The man-instrumentation-equipment-vehicle-environment ecosystem is complex in aerospace missions. Health status of the individual has important implications on decision making and performance that should be factored into assessments for probability of success/risk of failure both in offline and real-time models. To date probabilistic models have not considered the dynamic nature of health status. Big Data analytics is enabling new forms of analytics to assess health status in real-time. There is great potential to integrate dynamic health status information with platforms assessing risk and the probability of success for dynamic individualized real-time probabilistic predictive risk assessment. In this research we present an approach utilizing Big Data analytics to enable continuous assessment of astronaut health risk and show its implications for integration with HITL related aerospace mission.

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http://hdl.handle.net/10453/136926