This paper proposes a method to obtain a low-dimensional feature vector appropriately representing the characteristics of a given motion-capture data stream. The feature vector is derived based on the concept of phase plane analysis. A set of phase plane trajectories are obtained from the temporal variation of the state variables representing the body-segment arrangement. The information on six motion-characteristic properties is extracted from the shapes of the trajectories, and used as the components of a six-dimensional feature vector. The experimental results showed the effectiveness and limitation of the proposed method.
This study proposes a method to systematically visualize the motion-characteristic distribution of Japanese folk dances passed down in a certain area. This is accomplished by adopting an approach that involves analyzing motion-capture data collected from the dances. The visualization process in the proposed method consists of three stages. The first stage is the modeling of the relationship among motion-capture data, folk dances, and the settlements in which folk dances have been passed down. This relationship is modeled as a hierarchical-structure model. The second stage is the extraction of motion characteristics from motion-capture data streams. The motion characteristics of each data stream are summarized as a fourteen-dimensional feature vector. The third stage is the visualization of the motion-characteristic distribution of the dances investigated. Each of the dances is mapped on a two-dimensional scatter plot in accordance with the feature quantities obtained in the second stage. Information on the hierarchicalstructure model constructed in the first stage is also displayed. The analysis results for the distribution of Bon Odori dances showed that the proposed method could have almost completely visualized the motion-characteristic distribution of sample folk dances, while also demonstrating consistency with the knowledge of the dances acquired in the previous studies.
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