Nonlinear modeling of FES-supported standing-up in paraplegia for selection of feedback sensors

Kamnik, Roman; Shi, Jianghong; Murray-Smith, Roderick; Bajd, Tadej

doi:10.1109/tnsre.2004.841879

Cited by 30 publications

(14 citation statements)

References 29 publications

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“…The covariance function in Eq. (2) is adopted due to its reported good performance in the literature [17,15], and the conjugate gradient method is used to find the maximum likelihood estimation of the hyper-parameters. Following the common practice, the data is pre-processed to have zero mean and unit standard deviation at each variable before it is used for training a Gaussian process.…”

Section: Industrial Case Studymentioning

confidence: 99%

“…Gaussian processes can also be derived from the perspective of non-parametric Bayesian regression [14], by directly placing Gaussian prior distribution over the space of regression functions. As a result of its good performance in practice and desirable analytical properties, Gaussian process models have seen wide applications, such as rehabilitation engineering [15], machining optimization [16] and calibration of analytical sensors [17].…”

Section: Introductionmentioning

confidence: 99%

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Bagging for Gaussian process regression

2009

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This paper proposes the application of bagging to obtain more robust and accurate predictions using Gaussian process regression models. The training data is re-sampled using the bootstrap method to form several training sets, from which multiple Gaussian process models are developed and combined through weighting to provide predictions. A number of weighting methods for model combination are discussed, including the simple averaging rule and the weighted averaging rules. We propose to weight the models by the inverse of their predictive variance, and thus the prediction uncertainty of the models is automatically accounted for. The bagging method for Gaussian process regression is successfully applied to the inferential estimation of quality variables in an industrial chemical plant.

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Section: Industrial Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bagging for Gaussian process regression

2009

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“…The GP regression model can be derived from the perspectives of ANN and Bayesian non-parametric regression; see [29] for details. In recent studies, GP model has been shown to give superior prediction accuracy in process control [30], chemometric calibration [31], medical treatment design [32], and RSM [8,9]. Some theoretical analysis of the GP's generalization performance, which measures the prediction capability on unseen data, is given in [29,Chapter 7].…”

Section: The Gp Regression Modelmentioning

confidence: 99%

Interpretation of non-linear empirical data-based process models using global sensitivity analysis

Chen

Yang

2011

Chemometrics and Intelligent Laboratory Systems

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“…The analysis investigates the significance of arm, feet and seat reaction signals for the reconstruction of the human body centreof-mass (COM) trajectory. The motion kinematics, reaction forces and other quantities were measured for modelling; for more details see Kamnik et al (1999Kamnik et al ( , 2005). Here we model the vertical trajectory of the body COM as output, and select 8 input variables, such as the forces and torques under the patient's feet, under the arm support handle and under the seat while the body is in contact with it.…”

Section: Modelling Of Standing-up Manoeuvresmentioning

confidence: 99%

Curve prediction and clustering with mixtures of Gaussian process functional regression models

Shi

Wang

2008

Stat Comput

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Shi et al. (2006) proposed a Gaussian process functional regression (GPFR)model to model functional response curves with a set of functional covariates.Two main problems are addressed by this method: modelling nonlinear and nonparametric regression relationship and modelling covariance structure and mean structure simultaneously. The method gives very good results for curve fitting and prediction but side-steps the problem of heterogeneity. In this paper we present a new method for modelling functional data with 'spatially' indexed data, i.e., the heterogeneity is dependent on factors such as region and individual patient's information. For data collected from different sources, we assume that the data corresponding to each curve (or batch) follows a Gaussian process functional regression model as a lower-level model, and introduce an allocation model * Address for correspondence: School of Mathematics and Statistics, University of Newcastle, NE1 7RU, UK. Email: j.q.shi@ncl.ac.uk 1 for the latent indicator variables as a higher-level model. This higher-level model is dependent on the information related to each batch. This method takes advantage of both GPFR and mixture models and therefore improves the accuracy of predictions. The mixture model has also been used for curve clustering, but focusing on the problem of clustering functional relationships between response curve and covariates. The model is examined on simulated data and real data.

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Nonlinear modeling of FES-supported standing-up in paraplegia for selection of feedback sensors

Cited by 30 publications

References 29 publications

Bagging for Gaussian process regression

Bagging for Gaussian process regression

Interpretation of non-linear empirical data-based process models using global sensitivity analysis

Curve prediction and clustering with mixtures of Gaussian process functional regression models

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