Linear combinations of nonlinear models for predicting human-machine interface forces

ACT This study presents a computational framework that capitalizes on known human neuromechanical characteristics during limb movements in order to predict man-machine interactions. A parallel-distributed approach, the mixture of nonlinear models, fits the relationship between the measured kinematics and kinetics at the handle of a robot. Each element of the mixture represented the arm and its controller as a feedforward nonlinear model of inverse dynamics plus a linear approximation of musculotendonous impedance. We evaluated this approach with data from experiments where subjects held a handle of a planar manipulandum robot and attempted to make point-to-point reaching movements. We compared the performance to the more conventional approach of a constrained, nonlinear optimization of the parameters. On average, the mixture of nonlinear models accounted for 0.79

• Type: Journal
• Year: 2002
• Where: BC
• Authors: James L. Patton, Ferdinando A. Mussa-Ivaldi
• Keywords: BC 2002, Feedforward Nonlinear Model, Human Neuromechanical Characteristics, Nonlinear Models