Muscle Force

Muscle force estimation and control are crucial for understanding and replicating human and robotic movement, with current research focusing on accurately predicting muscle forces from various data sources like surface electromyography (sEMG) and motion capture. Researchers employ diverse methods, including physics-informed deep learning models (e.g., integrating musculoskeletal models into neural networks) and Kalman filtering techniques enhanced by recurrent neural networks, to improve prediction accuracy and robustness. These advancements have significant implications for applications ranging from improving prosthetic limb control and human motion analysis to enabling safe and effective physical interaction for aerial robots.