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.

Wrist redundancy management during pointing tasks remains stable over time and in presence of a visuomotor perturbation

“…Such evidences are in line with the most popular theories of human motor control, in which the motor output is the result of a cooperative and iterative process for optimizing all factors involved, i.e. the accuracy in performing the task and the management of redundancy 16 – 18 .…”

Event-Based Data Acquisition and Reconstruction—Mathematical Background

“…An iterative learning control explanation of these results is given by Zhou, Oetomo, Tan, Burdet, and Mareels (2012).…”

“…The system similar to that described in Section IV, case 3 of the paper by Zhou et al (2012) was used. The lateral motion of the arm in the force field was described the transfer function of Equation ( Figure 44 shows the evolution of the estimated parameters with iteration number.…”

Intermittent Control in Man and Machine

“…Repetitive reaching and pointing has been examined by a number of authors including Shadmehr and Mussa-Ivaldi (1994) (see also Shadmehr and Mussa-Ivaldi (2012)), Burdet, Tee, Mareels, Milner, Chew, Franklin, Osu, and Kawato (2006) and Tee, Franklin, Kawato, Milner, and Burdet (2010). An iterative learning control explanation of these results is given by Zhou, Oetomo, Tan, Burdet, and Mareels (2012).…”

“…The system similar to that described in Section IV, case 3 of the paper by Zhou et al (2012) was used. The lateral motion of the arm in the force field was described the transfer function of Equation ( Figure 42 shows the system output (transverse position) y and control input u for five of the iterations; the sample instants are denoted by the • symbol and the ideal trajectory by the grey line.…”