Task-Level Reconstruction and Analysis of Dynamic Motions in Human Musculoskeletal Systems

Understanding human motor control involves studying the principles used to optimize dynamic movement. This process requires accurate modeling and simulation of musculoskeletal kinematics, reconstruction of motion dynamics, and characterization of elite performance motor skills. Motivated by the previous robotics research applied to these challenges, the present research aims to leverage the task-level control strategies to reconstruct and analyze human dynamic motions, introducing the approaches of (i) modeling of human musculoskeletal system for task-level motion control, (ii) task-level reconstruction of human motion, (iii) robotics-based analysis of human dynamic skills. Our findings suggest that task-based control approach is an attractive tool to reconstruct and analyze the dynamic motions of human and other complex articulated body systems in a computationally efficient manner.