Comparing and Combining Time Series Trajectories Using Dynamic Time Warping

Neil Vaughan, Bogdan Gabrys

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    70 Downloads (Pure)


    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.
    Original languageEnglish
    Title of host publicationProcedia Computer Science
    Publication statusE-pub ahead of print - 4 Sept 2016


    Dive into the research topics of 'Comparing and Combining Time Series Trajectories Using Dynamic Time Warping'. Together they form a unique fingerprint.

    Cite this