Abstract
Dance is a rich artistic expression that combines intricate human movements with music, emotion, and cultural elements. However, the analysis of complex dance movements poses significant challenges because of the lack of comprehensive motion capture data and efficient computational techniques for feature extraction. In the current study, we present a novel time-dependent principal component analysis approach for extracting beat-aligned motor synergies from large street dance datasets. Unlike existing methods, our technique accounts for the temporal variability induced by music beats, enabling an accurate representation of dance motion patterns. The extracted motor synergies, capturing both spatial and temporal patterns across motion segments and beat durations, were analyzed to gain insights into motor coordination, consistency, similarity, and variability across different dance genres. This analysis facilitates the understanding of complex dance movements by summarizing them in a low-dimensional subspace, elucidating the common elements and coordinated modalities among various dance sequences segmented based on the timing of music beats. Furthermore, we demonstrated that kinematic beat detection was improved by leveraging the first motor synergy activation, enabling more accurate beat alignment and synchronization with the music, a crucial factor in dance performance and analysis. The enhancement of beat estimation accuracy was verified through cross-validation comparisons of beat alignment scores. This work offers a novel computational approach to analyzing and extracting meaningful patterns from complex dance motions for a deeper understanding of the motor mechanisms inherent in dance genres, enabling new insights into the intricate dynamics of dance movements and their relationships with music influences.