Show simple item record

dc.contributor.authorGlackin, Cornelius
dc.contributor.authorSalge, Christoph
dc.contributor.authorGreaves, Martin
dc.contributor.authorPolani, D.
dc.contributor.authorSlavnić, Siniša
dc.contributor.authorRistić-Durrant, Danijela
dc.contributor.authorLeu, Adrian
dc.contributor.authorMatjačić, Zlatko
dc.identifier.citationGlackin , C , Salge , C , Greaves , M , Polani , D , Slavnić , S , Ristić-Durrant , D , Leu , A & Matjačić , Z 2014 , Gait Trajectory Prediction using Gaussian Process Ensembles . in Humanoids 2014 . IEEE , pp. 628-633 , 2014 IEEE-RAS Int Conf on Humanoid Robots , Madrid , Spain , 18/11/14 .
dc.identifier.otherPURE: 8202038
dc.identifier.otherPURE UUID: f09c1674-008a-4c3b-b624-5dff5b95d6d7
dc.identifier.otherBibtex: urn:6e8e21d159e36afe750dfaf44a07d8d0
dc.identifier.otherScopus: 84945188722
dc.description.abstractThe development of robotic devices for the rehabilitation of gait is a growing area of interest in the engineering rehabilitation community. The problem with modelling gait dynamics is that everybody walks differently. The approach advocated in this paper addresses this issue by modelling the gait dynamics of individual patients. Specifically, we present a model learner which performs automated system identification of patient gait. The model learner consists of an ensemble of multiple-input-single-output Gaussian Processes which feature automatic relevance determination kernels for automated tuning of parameters. First, the paper presents results for the application of the Gaussian Process ensemble to the learning of a particular patient's gait using a typical prediction configuration. Generalisation of gait prediction is tested with multiple patients and cross-validation. Finally, initial results are presented in which the Gaussian Process ensemble is shown to be capable of learning the mapping between the patient's gait and the therapist-assisted gaiten
dc.relation.ispartofHumanoids 2014
dc.titleGait Trajectory Prediction using Gaussian Process Ensemblesen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionAdaptive Systems
dc.relation.schoolSchool of Computer Science

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record