Knowledge discovery in choreographic data using Relative Motion matrices and Dynamic Time Warping

Abstract Recent advances in location-aware technologies have led to the exploitation of geospatial methods to uncover valuable information from large movement data sets. The focus of this study is on choreographic data. In particular, the goal of the study is to visualize and analyze the motion patterns of samba dancers during their performance by means of two complementary methods. The first method performs map algebra with RElative MOtion (REMO) matrices to study the evolution of motion attributes, such as speed, motion azimuth, and vertical angle over time. The second method applies Dynamic Time Warping (DTW) to time series of motion attributes. The results demonstrate that both methods are useful in numerically comparing the performance of samba dancers and visually exploring the motion patterns of different body parts.

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