title: A nonlinear system model of isometric force.
abstract: The analysis of isometric force may provide early detection of certain types of neuropathology such as Parkinson's disease. Our long term goal is to determine if there are detectable differences between model parameters of healthy and unhealthy individuals. In this study we used system identification techniques to estimate the parameters of dynamic system models of the isometric force exerted by the index finger and focused on a single category of subjects, healthy young adults. The experiments involved subjects exerting isometric force over a range from 5% to 95% of maximal voluntary contraction. The coefficients of the differential equation models depended on the target force level. This finding suggests that a nonlinear dynamic system model provides the best fit for isometric force experiments.
authors.affiliation: Applied Research Laboratory, Pennsylvania State University, University Park, PA 16802, USA. JStitt@psu.edu
authors.forename: Joseph P
authors.initials: JP
authors.lastname: Stitt
authors.forename: Karl M
authors.initials: KM
authors.lastname: Newell
mesh_terms: D003198:Computer Simulation; D005246:Feedback / Q000502:physiology; D005385:Fingers / Q000502:physiology*; D006801:Humans; D007537:Isometric Contraction / Q000502:physiology*; D008954:Models, Biological; D018482:Muscle, Skeletal / Q000502:physiology*; D017711:Nonlinear Dynamics; D013314:Stress, Mechanical