Are We Solving the Redundancy of Human Motor Control Properly? |Biomedgrid

One of the traditional questions in the biomechanics field is identifying human motor control laws. The human motor control system consists of complex biological neural networks and muscles. Yet, it is phenomenal to observe how our motor control can achieve efficient, stable, and robust motion control [1-4]. These intriguing observational results indicate that our central nervous system (CNS) manages to control multiple muscles, which have extremely non-linear dynamics, to drive a multiple linkage structure of the skeletal system to meet biomechanical requirements using limited computational power of CNS and imperfect neural feedback with time delay of peripheral information [5]. Therefore, understanding the principles in human motor control is valuable for not only scientific means but also poses a critical question for implementing affective assistive devices, such as neural prosthesis and exoskeletons, as well as robust autonomous robotics [6,7]. Although various attempts have been made to identify the human motor control law over the past several decades, human motor control is still far from being fully understood. Determining the level of muscle activity for a desired locomotion, also known as load sharing problem (LSP), requires inverse calculation of each muscle activity from given target joint torques or kinematics. Because the nature of the LSP's redundancy due to having greater number of muscles than that of the joints of interest, the analytical solution for the LSP is believed to be unattainable.