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        Gait Trajectory Prediction using Gaussian Process Ensembles

        Author
        Glackin, Cornelius
        Salge, Christoph
        Greaves, Martin
        Polani, D.
        Slavnić, Siniša
        Ristić-Durrant, Danijela
        Leu, Adrian
        Matjačić, Zlatko
        Attention
        2299/15758
        Abstract
        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
        Publication date
        2014
        Published in
        Humanoids 2014
        Published version
        https://doi.org/10.1109/HUMANOIDS.2014.7041428
        Other links
        http://hdl.handle.net/2299/15758
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