The purpose of this research project was to bridge the gap between motion analysists and athletes and coaches by establishing a platform for the communication amongst the three parties. The first part of this project depicted that: 1) differences amongst the external view (motion analysists), internal sight (athletes) and internal sight from external view (coaches) were caused by the inertial (environment-fixed) and the non-inertial (body-fixed) system; 2) joint rotations were not identical with the muscular moment, therefore, passive rotations can occur; 3) critical phases in a skill control, which can be revealed by using modeling simulation, should be emphasized during learning; and 4) dynamic modeling has the potential to link and to unify the three views and supply a more holistic picture of human motor control. Based on these results, a learning model was constructed in the second part of the project. The essence of the model is to supply learners with the control signal (muscle moments) obtained from individual anthropometrical data and should-be-learned kinematics. Such an individualized learning process consists of: 1) obtaining kinematic characteristics of a should-be-learned skill using motion capture, 2) substituting the model’s anthropometrical data with a learner’s data, and applying inverse dynamic analysis to the model for obtaining muscle moments – the individualized control signal, and 3) applying the control information in the skill learning. The model was validated in a motor learning study. The study unveiled that dynamic modeling is well suited for improving communication with athletes and coaches as well as for improving efficiency of learning.
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