Motion Control is increasing its importance. Although the progress of system dynamics is remarkable, progress of human body motion control is very slow. Most of system dynamics deal with explicit knowledge, but human body motion control belongs to tacit knowledge.
Its difficulty is the number of degrees of freedom is tremendously large and human behaviors change very flexibly to cope with the changing contexts of environments and situations. Further, our body motions vary from person to person, because our bodies, muscles and joints are different. These problems make it very difficult to deal with human body motions.
Although there are many researches using motion capture, EMG, etc., they succeeded only in showing how final successful movements should be. They can show movements at each step toward this goal, but they cannot teach learners how they should coordinate their muscles or joints. Coordination or balancing plays an important role in body motion learning, But, there are very few, in any, researches which help learners learn how to coordinate or balance their muscles and joints to achieve the final successful movement.
In this paper, a solution to how we can help a learner learn to coordinate or balance in motion or motor learning is introduced. Its approach is pattern based and it uses Recognition Taguchi (RT) technique, one of the techniques of Mahalanobis Taguchi Systems. In this approach, Mahalanobis Distance (MD) is used to indicate quantitatively how a learner’s pattern of movement is close to the successful one. MD reduces multi-dimensional information to one-dimensional. RT indicates how a sample pattern matches the ideal pattern quantitatively using MD. In the regular RT approach, Unit Space (Ideal Pattern) is defined and each sample space is compared with Unit Space using MD.
But In this work, Unit Space is updated every time a learner succeeds, such as successfully riding a bicycle. And every trial movement is compared with this updated Unit Space. The primary benefits of RT are it can process large data in a very short time and it is based upon the difference between the ideal pattern and the current pattern. So, learners can understand which joints they should pay attention to in order to coordinate or balance to improve their movements. Thus, step by step, they can coordinate or balance their muscles and joints to get closer to the ideal movement.