Exploring Dance Movement Data Using Sequence Alignment Methods

PLoS One. 2015 Jul 16;10(7):e0132452. doi: 10.1371/journal.pone.0132452. eCollection 2015.


Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method. The applicability of this approach is tested using movement data from samba and tango dancers.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Biomechanical Phenomena
  • Cluster Analysis
  • Dancing / statistics & numerical data*
  • Female
  • Humans
  • Male
  • Models, Statistical*
  • Movement
  • Sequence Alignment
  • Software*

Grant support

The authors would like to thank the Special Research Fund of Ghent University (BOF) for supporting the research.