- Author
- Seyed Hossein Chavoshi (UGent) , Bernard De Baets (UGent) , Tijs Neutens (UGent) , Guy De Tré (UGent) and Nico Van de Weghe (UGent)
- Organization
- 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.
- Keywords
- REPRESENTING MOVING-OBJECTS, QUALITATIVE TRAJECTORY CALCULUS, ACTIVITY PATTERNS, SIMILARITY, BLUETOOTH, SYSTEMS, TIME, GPS, INFORMATION, SOFTWARE
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-6979306
- MLA
- Chavoshi, Seyed Hossein, et al. “Exploring Dance Movement Data Using Sequence Alignment Methods.” PLOS ONE, vol. 10, no. 7, 2015, doi:10.1371/journal.pone.0132452.
- APA
- Chavoshi, S. H., De Baets, B., Neutens, T., De Tré, G., & Van de Weghe, N. (2015). Exploring dance movement data using sequence alignment methods. PLOS ONE, 10(7). https://doi.org/10.1371/journal.pone.0132452
- Chicago author-date
- Chavoshi, Seyed Hossein, Bernard De Baets, Tijs Neutens, Guy De Tré, and Nico Van de Weghe. 2015. “Exploring Dance Movement Data Using Sequence Alignment Methods.” PLOS ONE 10 (7). https://doi.org/10.1371/journal.pone.0132452.
- Chicago author-date (all authors)
- Chavoshi, Seyed Hossein, Bernard De Baets, Tijs Neutens, Guy De Tré, and Nico Van de Weghe. 2015. “Exploring Dance Movement Data Using Sequence Alignment Methods.” PLOS ONE 10 (7). doi:10.1371/journal.pone.0132452.
- Vancouver
- 1.Chavoshi SH, De Baets B, Neutens T, De Tré G, Van de Weghe N. Exploring dance movement data using sequence alignment methods. PLOS ONE. 2015;10(7).
- IEEE
- [1]S. H. Chavoshi, B. De Baets, T. Neutens, G. De Tré, and N. Van de Weghe, “Exploring dance movement data using sequence alignment methods,” PLOS ONE, vol. 10, no. 7, 2015.
@article{6979306,
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.}},
articleno = {{e0132452}},
author = {{Chavoshi, Seyed Hossein and De Baets, Bernard and Neutens, Tijs and De Tré, Guy and Van de Weghe, Nico}},
issn = {{1932-6203}},
journal = {{PLOS ONE}},
keywords = {{REPRESENTING MOVING-OBJECTS,QUALITATIVE TRAJECTORY CALCULUS,ACTIVITY PATTERNS,SIMILARITY,BLUETOOTH,SYSTEMS,TIME,GPS,INFORMATION,SOFTWARE}},
language = {{eng}},
number = {{7}},
pages = {{25}},
title = {{Exploring dance movement data using sequence alignment methods}},
url = {{http://doi.org/10.1371/journal.pone.0132452}},
volume = {{10}},
year = {{2015}},
}
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