Abstract
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].
Highlights
One of the traditional questions in the biomechanics field is identifying human motor control laws
It is phenomenal to observe how our motor control can achieve efficient, stable, and robust motion control [1,2,3,4]. 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]
The advanced state estimation methodologies have been developed for the cases of imperfect system modeling, non-negligible noise, and unknown inputs [15,16,17]. This implies that if the muscle activity is considered as system states and the joint torque or kinematics is treated as system outputs, the analytical solution can be driven exclusively based on the neuromuscular dynamics while accounting for the unmodeled neural dynamics as system noise
Summary
One of the traditional questions in the biomechanics field is identifying human motor control laws. It is phenomenal to observe how our motor control can achieve efficient, stable, and robust motion control [1,2,3,4]. 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]. 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
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