Bernstein, a Russian physiologist in 20th century, aka, the father of biomechanics, reported that skilled blacksmith correctly hit a target place by the hammer, however, variance was observed in hand trajectory every trial.
From this finding he discussed that (1) our nervous system pays attention to some specific points to perform a given task and (2) the ability to solve a given task by multiple ways, in other words, by utilizing the synergy among the abundant DOFs, is a typical characteristics of the intelligence that the living bodies show.
This episode indicates that variance of the movement of proficient performers involves a key to find tacit knowledge. We are analyzing such tacit knowledge involved in human movements by using uncontrolled manifold (UCM) analysis.
Legged animals change their gait pattern according to their locomotion speed, e.g., walking, trotting, and galloping. Our analysis by computer simulations and theoretical investigation explain that many characteristics of locomotor parameters observed in many legged animals, e.g., step length, stride period, and gait transition, are the results of the optimization based on energetic efficiency.
In order to reach our arm to a given target, there are infinite number of choices in the selection of arm joint trajectory. Many theoretical studies proposed criterion that explain the characteristics of the arm reaching trajectory, e.g., minimum jerk model, minimum torque change model, and minimum endpoint variance model.
We have shown that many characteristics of the arm reaching trajectory can also be explained by the minimum energetic cost model, the same criterion that explains the characteristics of locomotor patterns.
Basic locomotor patterns are generated by the mutual entrainment between the physical system, i.e., the body, and the CPG (central pattern generator) which is composed of neural oscillators. We have proposed a simple associative learning models for coupled neural oscillators which enable the learning of instructed phase pattern and the adaptive control of periodic locomotor pattern.
In the production of basic locomotor patterns, not only the CPG but also higher centers are involved to adjust the motor pattern according to sensory signals. We have proposed a simple hierarchical learning control model composed of the CPG and higher centers and shown that the control model realizes robust control against perturbations.
We are currently planning to begin some new works...
We are analyzing structure of musical pieces from the view point of dynamics system in order to reveal the underlying the spatio-temporal structure that can move our minds.