Neuromechanical Model

Neuromechanical models aim to understand and replicate the complex interplay between the nervous system and musculoskeletal system in generating movement. Current research focuses on developing accurate models, often using musculoskeletal simulations coupled with reinforcement learning algorithms or Gaussian state-space models to control virtual or robotic systems, mimicking human actions like reaching or locomotion. These models are valuable for studying human motor control, designing assistive robots and prosthetics, and testing rehabilitation therapies, offering a powerful tool for both basic and applied research in biomechanics and neuroscience. The ultimate goal is to create more realistic and adaptable models that can accurately predict and control movement in diverse contexts.