Gait Trajectory Prediction using Gaussian Process Ensembles

Cornelius Glackin, Christoph Salge, Martin Greaves, D. Polani, Siniša Slavnić, Danijela Ristić-Durrant, Adrian Leu, Zlatko Matjačić

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

7 Citations (Scopus)


The 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 gait
Original languageEnglish
Title of host publicationHumanoids 2014
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)978-1-4799-7174-9
Publication statusPublished - 2014
Event2014 IEEE-RAS Int Conf on Humanoid Robots - Madrid, Spain
Duration: 18 Nov 201420 Nov 2014


Conference2014 IEEE-RAS Int Conf on Humanoid Robots


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