Characterization of motor skill based on musculoskeletal model.
In this paper, we propose two methods to quantitatively analyze the motor skill in sports. The first method is the dimensionality reduction using the principal component analysis (PCA). The motion data, e.g. the joint angles (143-dimensional vector) or the muscle tensions (989-dimensional vector), are projected to a lower dimensional space that well represents the characteristics of original data. The similarities and differences become clear by observing the data in the low-dimensional space. The second method utilizes the joint stiffness obtained from joint kinematics and a biological muscle model. Though muscle tension data contain richer information than joint angle data, the dimension is so high that simply applying PCA does not give useful insights. Here we calculate the joint stiffness using the muscle tension data and a biological muscle model. This information represents the muscle usage skill which can not be observed only from motion data, and reflects the redundancy of the muscle tensions. We demonstrate the two methods by analyzing skilled performers' motions.
Murai
Akihiko
A
Department of Mechano-Informatics, the University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan. murai@ynl.t.u-tokyo.ac.jp
Yamane
Katsu
K
Nakamura
Yoshihiko
Y
D000465:Algorithms; D001914:Boxing / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008954:Models, Biological; D009048:Motor Skills / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D013647:Task Performance and Analysis; D051598:Whole Body Imaging / Q000379:methods*