Modeling individual human motor behavior through model reference iterative learning control

Shou Han Zhou, Denny Oetomo, Ying Tan, Etienne Burdet, Iven Mareels

Research output: Contribution to journalArticleResearchpeer-review

15 Citations (Scopus)


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.

Original languageEnglish
Article number2192437
Pages (from-to)1892-1901
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Issue number7
Publication statusPublished - 29 Jun 2012
Externally publishedYes


  • Human motor computational model
  • impedance control
  • model reference iterative learning control (MRILC)

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