Home News About Us Contact Contributors Disclaimer Privacy Policy Help FAQ

Home
Search
Quick Search
Advanced
Fulltext
Browse
Collections
Persons
My eDoc
Session History
Login
Name:
Password:
Documentation
Help
Support Wiki
Direct access to
document ID:


          Display Documents



  history
ID: 124496.0, MPI für Dynamik komplexer technischer Systeme / Systems and Control Theory
Online identification of the electrically stimulated quadriceps muscle group
Authors:Schauer, T.; Previdi, F.; Hunt, K. J.; Negard, N.-O.; Ferchland, E.; Raisch, J.
Language:English
Date of Publication (YYYY-MM-DD):2003
Title of Proceedings:Proc. 5th IFAC Symposium on Modelling and Control in Biomedical Systems
Start Page:467
End Page:472
Name of Conference/Meeting:5th IFAC Symposium on Modelling and Control in Biomedical Systems
Place of Conference/Meeting:Melbourne, Australia
(Start) Date of Conference/Meeting
 (YYYY-MM-DD):
2003-08-21
End Date of Conference/Meeting 
 (YYYY-MM-DD):
2003-08-23
Review Status:Peer-review
Audience:Experts Only
Intended Educational Use:No
Abstract / Description: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 with-active knee moment relation) from discrete-time angle measurements only, a hybrid model structure is postulated for the shank-quadriceps 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 nonlinear activation dynamics and a nonlinear static contraction function described bya 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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Jörg Raisch
Affiliations:MPI für Dynamik komplexer technischer Systeme/Systems and Control Theory
Identifiers:LOCALID:828
The scope and number of records on eDoc is subject to the collection policies defined by each institute - see "info" button in the collection browse view.