Sensor-Actor Networks utilising the Spring Tensor Model for Laparoscopic Surgical Training Simulations

Chiu, CC; Chaczko, ZC; Wazirali, R

Sensor-Actor Networks utilising the Spring Tensor Model for Laparoscopic Surgical Training Simulations

Chiu, CC

Chaczko, ZC

Wazirali, R

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Publisher:: IQProceedings
Publication Type:: Conference Proceeding
Citation:: 7th International Conference on Broadband and Biomedical Communications, 2012, pp. 152 - 157
Issue Date:: 2012-01

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Field	Value	Language
dc.contributor.author	Chiu, CC https://orcid.org/0000-0003-2045-5961	en_US
dc.contributor.author	Chaczko, ZC https://orcid.org/0000-0002-2816-7510	en_US
dc.contributor.author	Wazirali, R	en_US
dc.contributor.editor	Agbinya, DJ	en_US
dc.contributor.editor	Lal, DS	en_US
dc.date	2012-11-05	en_US
dc.date.issued	2012-01	en_US
dc.identifier.citation	7th International Conference on Broadband and Biomedical Communications, 2012, pp. 152 - 157	en_US
dc.identifier.isbn	978-0-9872129-1-7	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30650
dc.description.abstract	The use of Sensor-Actor Networks (SANETs) has been applied to surgical training contexts, to illustrate how the Spring Tensor Model (STEM) can be used for laparoscopic end-effector navigation through obstacles and high-risk areas. The modelling of agents as interactive components of a laparoscopic simulator seeks to emulate the physical environment as a virtualised representation in the integrated SANET infrastructure. Combining SANET middleware framework paradigms to a surgical knowledge-based construct demonstrates how SANETs can enhance medical practice. The architectural hybridisation of the training framework has enabled the adaptation of STEM modelling techniques for a simulated laparoscopic training methodology. The primary benefit of the architecture is that this integration strategy has resulted in a seamless transition of the heuristic framework to be applied to surgical training.	en_US
dc.publisher	IQProceedings	en_US
dc.relation.ispartof	7th International Conference on Broadband and Biomedical Communications	en_US
dc.relation.ispartof	International Conference on Broadband and Biomedical Communications	en_US
dc.title	Sensor-Actor Networks utilising the Spring Tensor Model for Laparoscopic Surgical Training Simulations	en_US
dc.type	Conference Proceeding
utslib.location	Australia	en_US
utslib.location.activity	Sydney, Australia	en_US
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Sydney, Australia
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Architecture
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.finish-date	2012-11-08	en_US
pubs.place-of-publication	Australia	en_US
pubs.start-date	2012-11-05	en_US

Abstract:

The use of Sensor-Actor Networks (SANETs) has been applied to surgical training contexts, to illustrate how the Spring Tensor Model (STEM) can be used for laparoscopic end-effector navigation through obstacles and high-risk areas. The modelling of agents as interactive components of a laparoscopic simulator seeks to emulate the physical environment as a virtualised representation in the integrated SANET infrastructure. Combining SANET middleware framework paradigms to a surgical knowledge-based construct demonstrates how SANETs can enhance medical practice. The architectural hybridisation of the training framework has enabled the adaptation of STEM modelling techniques for a simulated laparoscopic training methodology. The primary benefit of the architecture is that this integration strategy has resulted in a seamless transition of the heuristic framework to be applied to surgical training.

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http://hdl.handle.net/10453/30650