The University of Melbourne, Parkville, Vic. 3010, Australia.
IEEE Trans Biomed Eng. 2012 Jul;59(7):1892-901. doi: 10.1109/TBME.2012.2192437. Epub 2012 Apr 3.
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.
本文提出了一种计算模型，用于捕捉人类受试者在新动力学中适应其运动的学习能力。该模型使用迭代学习控制算法通过重复过程来表示人类学习。控制律使用从感兴趣个体的自然行为中捕获的实验数据设计的模型进行自适应。控制信号用于身体模型来产生运动，而无需逆运动学。所得到的运动行为与实验数据进行了验证。这项新技术具有对运动功能进行特定于主体的建模的能力，有可能解释物理康复中的个体行为。
