An Anticipatory Sanet Environment for Training and Simulation of Laparoscopic Surgical Procedures

Chiu, CC; Chaczko, ZC

An Anticipatory Sanet Environment for Training and Simulation of Laparoscopic Surgical Procedures

Chiu, CC Chaczko, ZC

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Advanced Methods and Applications in Computational Intelligence, 2014, 1st Edition, pp. 367 - 385
Issue Date:: 2014-01

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Field	Value	Language
dc.contributor.author	Chiu, CC
dc.contributor.author	Chaczko, ZC
dc.contributor.editor	Klempous, R
dc.contributor.editor	Nikodem, J
dc.contributor.editor	Jacak, W
dc.contributor.editor	Chaczko, Z
dc.date.accessioned	2014-04-27T18:06:07Z
dc.date.accessioned	2015-03-02T14:12:24Z
dc.date.issued	2014-01
dc.identifier.citation	Advanced Methods and Applications in Computational Intelligence, 2014, 1st Edition, pp. 367 - 385
dc.identifier.isbn	978-3-319-01435-7
dc.identifier.other	B1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27480
dc.identifier.uri	http://hdl.handle.net/10453/33990
dc.description.abstract	Surgical simulation environments are required to train laparoscopic surgeons to become familiar with the technology, before they operate on patients. The chapter examines how a Sensor-Actor Network (Sanet) incorporated into the laparoscopic training facility can be augmented with the Belief-Desire-Intention (Bdi) agents for autonomous operation of the sensor facility along with the training environment. In addition to neural network processes, path trajectory estimation can be superimposed as a visual aid in a variety of training scenarios. The algorithms used in this experiment provides a balanced approach to monitor the training surgeons level of progress and feedback as they perform the simulated training procedures.
dc.publisher	Springer
dc.relation.ispartof	Verified OK	en_US
dc.relation.ispartof	Advanced Methods and Applications in Computational Intelligence
dc.relation.isbasedon	10.1007/978-3-319-01436-4_17
dc.relation.replaces	http://hdl.handle.net/10453/27480
dc.subject	Sanets, laparoscopic training, path trajectory estimation, simulation,	en_US
dc.subject.classification	Simulation and Modelling	en_US
dc.title	An Anticipatory Sanet Environment for Training and Simulation of Laparoscopic Surgical Procedures
dc.type	Chapter
utslib.location	Germany	en_US
utslib.citation.startpage	367	en_US
utslib.citation.endpage	385	en_US
utslib.identifier.org	FEIT.Faculty of Engineering & Information Technology	en_US
utslib.for	080603 Conceptual Modelling
utslib.for	080599 Distributed Computing Not Elsewhere Classified
utslib.for	080504 Ubiquitous Computing
utslib.for	0805 Distributed Computing
utslib.for	080309 Software Engineering
utslib.for	0803 Computer Software
utslib.for	080110 Simulation and Modelling
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	08 Information and Computing Sciences
utslib.percentage	25	en_US
utslib.citation.edition	1st Edition	en_US
pubs.embargo.period	Not known
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 Computing and Communications
pubs.organisational-group	/University of Technology Sydney/Strength - Realtime Information Networks
utslib.copyright.status	closed_access
pubs.declined	1970-01-01T00:00:00.0+1000
pubs.deleted	1970-01-01T00:00:00.0+1000

Abstract:

Surgical simulation environments are required to train laparoscopic surgeons to become familiar with the technology, before they operate on patients. The chapter examines how a Sensor-Actor Network (Sanet) incorporated into the laparoscopic training facility can be augmented with the Belief-Desire-Intention (Bdi) agents for autonomous operation of the sensor facility along with the training environment. In addition to neural network processes, path trajectory estimation can be superimposed as a visual aid in a variety of training scenarios. The algorithms used in this experiment provides a balanced approach to monitor the training surgeons level of progress and feedback as they perform the simulated training procedures.

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http://hdl.handle.net/10453/27480