No abstract

Lateral quickness is a crucial component of many sports. However, biomechanical factors that contribute to quickness in lateral movements have not been understood well. Thus, the purpose of this study was to quantify 3-dimensional kinetics of hip, knee, and ankle joints in side steps to understand the function of lower extremity muscle groups. Side steps at nine different distances were performed by nine male subjects. Kinematic and ground reaction force data were recorded, and net joint torque and work were calculated by a standard inverse-dynamics method. Extension torques and work done at hip, knee, and ankle joints contributed substantially to the changes in side step distances. On the other hand, hip abduction work was not as sensitive to the changes in the side step distances. The main roles of hip abduction torque and work were to accelerate the center of mass laterally in the earlier phase of the movement and to keep the trunk upright, but not to generate large power for propulsion.

No abstract

Modeling the complex collective behavior is a challenging issue in several material and life sciences. The collective motion has been usually modeled by simple interaction rules and explained by global statistics. However, it remains difficult to bridge the gap between the dynamic properties of the complex interaction and the emerging group-level functions. Here we introduce decomposition methods to directly extract and classify the latent global dynamics of nonlinear dynamical systems in an equation-free manner, even including complex interaction in few data dimensions. We first verified that the basic decomposition method can extract and discriminate the dynamics of a well-known rule-based fish-schooling (or bird-flocking) model. The method extracted different temporal frequency modes with spatial interaction coherence among three distinct emergent motions, whereas these wave properties in multiple spatiotemporal scales showed similar dispersion relations. Second, we extended the basic method to map high-dimensional feature space for application to actual small-dimensional systems complexly changing the interaction rules. Using group sports human data, we classified the dynamics and predicted the group objective achievement. Our methods have a potential for classifying collective motions in various domains which obey in non-trivial dominance law known as active matters.