Linear combinations of nonlinear
models for predicting human-machine interface FORCES
James L. Patton &
Ferdinando
A. Mussa-Ivaldi
Sensory Motor Performance
Program, Rehabilitation
Institute of Chicago
Physical Medicine and Rehabilitation, Northwestern
University
Medical
School
(2002) Biological
Cybernetics, 86 (1) 73-87
This study presents a
computational framework that capitalizes on known human neuromechanical
characteristics during limb movements in order to predict human-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 the 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. The mixture of nonlinear models accounted for 79±11% (mean
±SD) of the variance in measured force, and force errors were 0.73 ± 0.20% of
the maximum exerted force. Solutions were acquired in half the time with a
significantly better fit. However, both approaches suffered equally from the
simplifying assumptions, namely that the human neuromechanical system consisted
of a feedforward controller coupled with linear impedances and a moving state
equilibrium. Hence, predictability was best limited to the first half of the
movement. The mixture of nonlinear models may be useful in human-machine tasks
such as in telerobotics, fly-by-wire vehicles, robotic training, and
rehabilitation.
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