Big data analytics for resilience assessment and development in tactical training serious games

McGregor, C; Bonnis, B

Big data analytics for resilience assessment and development in tactical training serious games

McGregor, C

Bonnis, B

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2016, 2016-August, pp. 158-162
Issue Date:: 2016-08-16

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Field	Value	Language
dc.contributor.author	McGregor, C https://orcid.org/0000-0002-0491-4403
dc.contributor.author	Bonnis, B
dc.date	2016-06-20
dc.date.accessioned	2021-03-21T23:53:41Z
dc.date.available	2021-03-21T23:53:41Z
dc.date.issued	2016-08-16
dc.identifier.citation	Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2016, 2016-August, pp. 158-162
dc.identifier.isbn	9781467390361
dc.identifier.issn	1063-7125
dc.identifier.uri	http://hdl.handle.net/10453/147425
dc.description.abstract	© 2016 IEEE. Training activities utilising virtual realityenvironments are being used increasingly to create trainingscenarios to promote resilience for mental and physicalwellbeing and to enable repeatable scenarios to allow traineesto learn techniques for various stressors. However, assessmentof the trainees' response to these training activities has eitherbeen limited to various pre and post training assessmentmetrics or collected in parallel during experiments andanalysed retrospectively. We have created a Big Data analyticsplatform, Athena, that in real-time acquires data from a firstperson shooter game, ArmA 3, as well as the data ArmA 3sends to the muscle stimulation component of a multisensorygarment, ARAIG that provides on the body feedback to thewearer for communications, weapon fire and being hit andintegrates that data with physiological response data such asheart rate, breathing behaviour and blood oxygen saturation. This paper presents a method to create structured resiliencetraining scenarios that incorporate Big Data analytics forresilience analytics for new approaches for resilienceassessment and development in tactical training serious games.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - IEEE Symposium on Computer-Based Medical Systems
dc.relation.ispartof	29th IEEE International Symposium on Computer-Based Medical Systems (CBMS)
dc.relation.ispartofseries	IEEE International Symposium on Computer-Based Medical Systems
dc.relation.isbasedon	10.1109/CBMS.2016.64
dc.rights	© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Big data analytics for resilience assessment and development in tactical training serious games
dc.type	Conference Proceeding
utslib.citation.volume	2016-August
utslib.location.activity	NORTH IRELAND
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	closed_access	*
dc.date.updated	2021-03-21T23:53:39Z
pubs.finish-date	2016-06-23
pubs.publication-status	Published
pubs.start-date	2016-06-20
pubs.volume	2016-August

Abstract:

© 2016 IEEE. Training activities utilising virtual realityenvironments are being used increasingly to create trainingscenarios to promote resilience for mental and physicalwellbeing and to enable repeatable scenarios to allow traineesto learn techniques for various stressors. However, assessmentof the trainees' response to these training activities has eitherbeen limited to various pre and post training assessmentmetrics or collected in parallel during experiments andanalysed retrospectively. We have created a Big Data analyticsplatform, Athena, that in real-time acquires data from a firstperson shooter game, ArmA 3, as well as the data ArmA 3sends to the muscle stimulation component of a multisensorygarment, ARAIG that provides on the body feedback to thewearer for communications, weapon fire and being hit andintegrates that data with physiological response data such asheart rate, breathing behaviour and blood oxygen saturation. This paper presents a method to create structured resiliencetraining scenarios that incorporate Big Data analytics forresilience analytics for new approaches for resilienceassessment and development in tactical training serious games.

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http://hdl.handle.net/10453/147425