Designing effective rehabilitation strategies for upper extremities, particularly hands and fingers, warrants the need for a computational model of human motor learning. The presence of large degrees of freedom (DoFs) available in these systems makes it difficult to balance the trade-off between learning the full dexterity and accomplishing manipulation goals. The motor learning literature argues that humans use motor synergies to reduce the dimension of control space. Using the low-dimensional space spanned by these synergies, we develop a computational model based on the internal model theory of motor control. We analyze the proposed model in terms of its convergence properties and fit it to the data collected from human experiments. We compare the performance of the fitted model to the experimental data and show that it captures human motor learning behavior well.
@article{arxiv.2202.02863,
title = {Towards Modeling Human Motor Learning Dynamics in High-Dimensional Spaces},
author = {Ankur Kamboj and Rajiv Ranganathan and Xiaobo Tan and Vaibhav Srivastava},
journal= {arXiv preprint arXiv:2202.02863},
year = {2022}
}