Comparing and Combining Time Series Trajectories Using Dynamic Time Warping

Vaughan, N; Gabrys, B

Comparing and Combining Time Series Trajectories Using Dynamic Time Warping

Vaughan, N Gabrys, B

Permalink

Publisher:: ELSEVIER SCIENCE BV
Publication Type:: Journal Article
Citation:: Procedia Computer Science, 2016, 96, pp. 465-474
Issue Date:: 2016-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (1.13 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Vaughan, N
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.contributor.editor	Howlett, RJ
dc.contributor.editor	Jain, LC
dc.contributor.editor	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.contributor.editor	Toro, C
dc.contributor.editor	Lim, CP
dc.date.accessioned	2023-02-23T06:38:05Z
dc.date.available	2023-02-23T06:38:05Z
dc.date.issued	2016-01-01
dc.identifier.citation	Procedia Computer Science, 2016, 96, pp. 465-474
dc.identifier.issn	1877-0509
dc.identifier.issn	1877-0509
dc.identifier.uri	http://hdl.handle.net/10453/166397
dc.description.abstract	This research proposes the application of dynamic time warping (DTW) algorithm to analyse multivariate data from virtual reality training simulators, to assess the skill level of trainees. We present results of DTW algorithm applied to trajectory data from a virtual reality haptic training simulator for epidural needle insertion. The proposed application of DTW algorithm serves two purposes, to enable (i) two trajectories to be compared as a similarity measure and also enables (ii) two or more trajectories to be combined together to produce a typical or representative average trajectory using a novel hierarchical DTW process. Our experiments included 100 expert and 100 novice simulator recordings. The data consists of multivariate time series data-streams including multi-dimensional trajectories combined with force and pressure measurements. Our results show that our proposed application of DTW provides a useful time-independent method for (i) comparing two trajectories by providing a similarity measure and (ii) combining two or more trajectories into one, showing higher performance compared to conventional methods such as linear mean. These results demonstrate that DTW can be useful within virtual reality training simulators to provide a component in an automated scoring and assessment feedback system.
dc.language	English
dc.publisher	ELSEVIER SCIENCE BV
dc.relation.ispartof	Procedia Computer Science
dc.relation.isbasedon	10.1016/j.procs.2016.08.106
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 10 Technology
dc.title	Comparing and Combining Time Series Trajectories Using Dynamic Time Warping
dc.type	Journal Article
utslib.citation.volume	96
utslib.location.activity	York, ENGLAND
utslib.for	08 Information and Computing Sciences
utslib.for	10 Technology
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/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2023-02-23T06:38:04Z
pubs.publication-status	Published
pubs.volume	96

Abstract:

This research proposes the application of dynamic time warping (DTW) algorithm to analyse multivariate data from virtual reality training simulators, to assess the skill level of trainees. We present results of DTW algorithm applied to trajectory data from a virtual reality haptic training simulator for epidural needle insertion. The proposed application of DTW algorithm serves two purposes, to enable (i) two trajectories to be compared as a similarity measure and also enables (ii) two or more trajectories to be combined together to produce a typical or representative average trajectory using a novel hierarchical DTW process. Our experiments included 100 expert and 100 novice simulator recordings. The data consists of multivariate time series data-streams including multi-dimensional trajectories combined with force and pressure measurements. Our results show that our proposed application of DTW provides a useful time-independent method for (i) comparing two trajectories by providing a similarity measure and (ii) combining two or more trajectories into one, showing higher performance compared to conventional methods such as linear mean. These results demonstrate that DTW can be useful within virtual reality training simulators to provide a component in an automated scoring and assessment feedback system.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/166397