NARMAX representation and identification of ankle dynamics

Representation and identification of a parallel pathway description of ankle dynamics as a model of the nonlinear autoregressive, moving average exogenous (NARMAX) class is considered. A nonlinear difference equation describing this ankle model is derived theoretically and shown to be of the NARMAX...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2003-01, Vol.50 (1), p.70-81
Main Authors: Kukreja, S.L., Galiana, H.L., Kearney, R.E.
Format: Article
Language:English
Subjects:
Ankle - physiology
Ankle dynamics
Ankle Joint - physiology
Autoregressive processes
Biological and medical sciences
Biological system modeling
Biological systems
Biomedical engineering
Computer Simulation
Data models
Difference equations
Dynamic tests
Elasticity
Feedback
Human
Humans
Identification methods
Male
Mathematical modeling
Mathematical models
Medical sciences
Metalloporphyrins
Models, Biological
Movement - physiology
Muscle, Skeletal - physiology
NARMAX
NATURAL SCIENCES
NATURVETENSKAP
Nonlinear dynamical systems
Nonlinear Dynamics
Nonlinear systems
Nonlinearity
Parameter estimation
Representations
Reproducibility of Results
Rotation
Sensitivity and Specificity
Stochastic Processes
System identification
Torque
Viscosity
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Summary:Representation and identification of a parallel pathway description of ankle dynamics as a model of the nonlinear autoregressive, moving average exogenous (NARMAX) class is considered. A nonlinear difference equation describing this ankle model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to ankle dynamics and its properties investigated via continuous-time simulations of experimental conditions. Simulation results show that 1) the outputs of the NARMAX model match closely those generated using continuous-time methods and 2) NARMAX identification methods applied to ankle dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental human ankle data models with high cross-validation variance accounted for.
ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2002.803507