Human motor learning dynamics in high-dimensional tasks.

ABSTRACT: Conventional approaches to enhance movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs). To effectively address coordination deficits in such complex motor systems, it becomes imperative to develop interventions grounded in a model of human motor learning; however, modeling such learning processes is challenging due to the large DoFs. In this paper, we present a computational motor learning model that leverages the concept of motor synergies to extract low-dimensional learning representations in the high-dimensional motor space and the internal model theory of motor control to capture both fast and slow motor learning processes. We establish the model's convergence properties and valid

SUBMITTER: Kamboj A 

PROVIDER: S-EPMC11501022 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature