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Exploring Dance Movement Data Using Sequence Alignment Methods.

Chavoshi SH, De Baets B, Neutens T, De Tré G, Van de Weghe N - PLoS ONE (2015)

Bottom Line: First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Geography, Ghent University, Ghent, Belgium.

ABSTRACT
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.

No MeSH data available.


Selected reflective markers (i.e., shoulders, hips, and feet) (a), an example of QTCB and QTCC movement sequences (i.e., ships) of the couple of professional tango dancers (b).
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pone.0132452.g007: Selected reflective markers (i.e., shoulders, hips, and feet) (a), an example of QTCB and QTCC movement sequences (i.e., ships) of the couple of professional tango dancers (b).

Mentions: The relative movements of the body parts of each tango dancer (i.e., shoulders, hips, and feet) are formalized by QTCB and QTCC relations. In Fig 7A, the selected reflective markers attached to the body parts of the tango dancer are illustrated. In Fig 7B, an example of important movement sequences of the hips of a couple of professional tango dancers is given. In this study, we only consider the relative movements of body parts of each dancer individually. However, it would also be of interest to examine the relative movement of one body part of a dancer with respect to that of the partner.


Exploring Dance Movement Data Using Sequence Alignment Methods.

Chavoshi SH, De Baets B, Neutens T, De Tré G, Van de Weghe N - PLoS ONE (2015)

Selected reflective markers (i.e., shoulders, hips, and feet) (a), an example of QTCB and QTCC movement sequences (i.e., ships) of the couple of professional tango dancers (b).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4504678&req=5

pone.0132452.g007: Selected reflective markers (i.e., shoulders, hips, and feet) (a), an example of QTCB and QTCC movement sequences (i.e., ships) of the couple of professional tango dancers (b).
Mentions: The relative movements of the body parts of each tango dancer (i.e., shoulders, hips, and feet) are formalized by QTCB and QTCC relations. In Fig 7A, the selected reflective markers attached to the body parts of the tango dancer are illustrated. In Fig 7B, an example of important movement sequences of the hips of a couple of professional tango dancers is given. In this study, we only consider the relative movements of body parts of each dancer individually. However, it would also be of interest to examine the relative movement of one body part of a dancer with respect to that of the partner.

Bottom Line: First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Geography, Ghent University, Ghent, Belgium.

ABSTRACT
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.

No MeSH data available.