title: Modeling individual human motor behavior through model reference iterative learning control.
abstract: A computational model is proposed in this paper to capture learning capacity of a human subject adapting his or her movements in novel dynamics. The model uses an iterative learning control algorithm to represent human learning through repetitive processes. The control law performs adaptation using a model designed using experimental data captured from the natural behavior of the individual of interest. The control signals are used by a model of the body to produced motion without the need of inverse kinematics. The resulting motion behavior is validated against experimental data. This new technique yields the capability of subject-specific modeling of the motor function, with the potential to explain individual behavior in physical rehabilitation.
authors.affiliation: The University of Melbourne, Parkville, Vic. 3010, Australia. s.zhou@pgrad.unimelb.edu.au
authors.forename: Shou-Han
authors.initials: SH
authors.lastname: Zhou
authors.forename: Denny
authors.initials: D
authors.lastname: Oetomo
authors.forename: Ying
authors.initials: Y
authors.lastname: Tan
authors.forename: Etienne
authors.initials: E
authors.lastname: Burdet
authors.forename: Iven
authors.initials: I
authors.lastname: Mareels
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008124:Locomotion / Q000502:physiology*; D008954:Models, Biological; D015203:Reproducibility of Results