Artificial physics engine for real-time inverse dynamics of arm and hand movement.

ABSTRACT: Simulating human body dynamics requires detailed and accurate mathematical models. When solved inversely, these models provide a comprehensive description of force generation that considers subject morphology and can be applied to control real-time assistive technology, for example, orthosis or muscle/nerve stimulation. Yet, model complexity hinders the speed of its computations and may require approximations as a mitigation strategy. Here, we use machine learning algorithms to provide a method for accurate physics simulations and subject-specific parameterization. Several types of artificial neural networks (ANNs) with varied architecture were tasked to generate the inverse dynamic transformation of realistic arm and hand movement (23 degrees of freedom). Using a physical model, we genera

SUBMITTER: Manukian M 

PROVIDER: S-EPMC10718432 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature