In this paper, a new approach for estimating a nonlinear model of the electrically stimulated quadriceps muscle group under non-isometric conditions is investigated. In order to identify the muscle dynamics (stimulation pulse width-active knee moment relation) from discrete-time angle measurements only, a hybrid model structure is postulated for the shankquadriceps dynamics. The model consists of a relatively well known time-invariant passive component and an uncertain time-variant active component. Rigid body dynamics, described by the Equation of Motion (EoM). and passive joint properties form the time-invariant part. The actuator. i.e. the electrically stimulated muscle group, represents the uncertain time-varying section. A recursive algorithm is outlined for identifying online the stimulated quadriceps muscle group. The algorithm requires EoM and passive joint characteristics to be known a priori. The muscle dynamics represent the product of a continuous-time non linear activation dynamics and a nonlinear static contraction function described by a Normalised Radial Basis Function (NRBF) network which has knee-joint angle and angular velocity as input arguments. An Extended Kalman Filter (EKF) approach is chosen to estimate muscle dynamics parameters and to obtain full state estimates of the shank-quadriceps dynamics simultaneously.

(2003). Online identification of the electrically stimulated quadriceps muscle group . Retrieved from http://hdl.handle.net/10446/86574

Online identification of the electrically stimulated quadriceps muscle group

SCHAUER, THOMAS;PREVIDI, Fabio;
2003-01-01

Abstract

In this paper, a new approach for estimating a nonlinear model of the electrically stimulated quadriceps muscle group under non-isometric conditions is investigated. In order to identify the muscle dynamics (stimulation pulse width-active knee moment relation) from discrete-time angle measurements only, a hybrid model structure is postulated for the shankquadriceps dynamics. The model consists of a relatively well known time-invariant passive component and an uncertain time-variant active component. Rigid body dynamics, described by the Equation of Motion (EoM). and passive joint properties form the time-invariant part. The actuator. i.e. the electrically stimulated muscle group, represents the uncertain time-varying section. A recursive algorithm is outlined for identifying online the stimulated quadriceps muscle group. The algorithm requires EoM and passive joint characteristics to be known a priori. The muscle dynamics represent the product of a continuous-time non linear activation dynamics and a nonlinear static contraction function described by a Normalised Radial Basis Function (NRBF) network which has knee-joint angle and angular velocity as input arguments. An Extended Kalman Filter (EKF) approach is chosen to estimate muscle dynamics parameters and to obtain full state estimates of the shank-quadriceps dynamics simultaneously.
2003
Schauer, Thomas; Previdi, Fabio; Hunt, K. J.; Ferchland, E.; Negard, N. O.; Raisch, J.
File allegato/i alla scheda:
File Dimensione del file Formato  
2003 IFAC MCBS - Identification.pdf

Solo gestori di archivio

Versione: publisher's version - versione editoriale
Licenza: Licenza default Aisberg
Dimensione del file 421.41 kB
Formato Adobe PDF
421.41 kB Adobe PDF   Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/86574
Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 0
social impact