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

Lookup NU author(s): Dr Jian Shi

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Abstract

This paper presents analysis of the standing-up manoeuvre in paraplegia considering the body supportive forces as a potential feedback source in functional electrical stimulation (FES)-assisted standing-up. The analysis investigates the significance of arm, feet, and seat reaction signals to the human body center-of-mass (COM) trajectory reconstruction. The standing-up behavior of eight paraplegic subjects was analyzed, measuring the motion kinematics and reaction forces to provide the data for modeling. Two nonlinear empirical modeling methods are implemented-Gaussian process (GP) priors and multilayer perceptron artificial neural networks (ANN)-and their performance in vertical and horizontal COM component reconstruction is compared. As the input, ten sensory configurations that incorporated different number of sensors were evaluated trading off the modeling performance for variables chosen and ease-of-use in everyday application. For the purpose of evaluation, the root-mean-square difference was calculated between the model output and the kinematics-based COM trajectory. Results show that the force feedback in COM assessment in FES assisted standing-up is comparable alternative to the kinematics measurement systems. It was demonstrated that the GP provided better modeling performance, at higher computational cost. Moreover, on the basis of averaged results, the use of a sensory system incorporating a six-dimensional handle force sensor and an instrumented foot insole is recommended. The configuration is practical for realization and with the GP model achieves an average accuracy of COM estimation 16 ± 1.8 mm in horizontal and 39 ± 3.7 mm in vertical direction. Some other configurations analyzed in the study exhibit better modeling accuracy, but are less practical for everyday usage. © 2005 IEEE.


Publication metadata

Author(s): Kamnik R, Shi JQ, Murray-Smith R, Bajd T

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Neural Systems and Rehabilitation Engineering

Year: 2005

Volume: 13

Issue: 1

Pages: 40-52

Print publication date: 01/03/2005

ISSN (print): 1534-4320

ISSN (electronic): 1558-0210

Publisher: IEEE

URL: http://dx.doi.org/10.1109/TNSRE.2004.841879

DOI: 10.1109/TNSRE.2004.841879

PubMed id: 15813405


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