Musculoskeletal Model

Musculoskeletal models aim to computationally represent the complex interplay between muscles, bones, and joints to understand and predict human movement. Current research focuses on improving model accuracy and realism, particularly through the integration of physics-informed neural networks and reinforcement learning algorithms to predict joint torques and kinematics from electromyography (EMG) data or motion capture. These advancements are driving progress in areas such as prosthetic design, rehabilitation robotics, and the development of more accurate biomechanical analyses, bridging the gap between computer vision and biomechanics. Ultimately, refined musculoskeletal models promise to enhance our understanding of human motor control and facilitate the creation of more effective assistive technologies.