@article{3511,
  keywords = {Bidirectional LSTM, Brain, Dance summarization, Deep learning, Educational learning, Gesture recognition, Hierarchical representation, Identification procedure, Intangible cultural heritage, Intangible cultural heritages, Learning systems, Long short-term memory, Pose identification, Quantitative assessments, Representative selection, Serious games},
  author = {Ioannis Rallis and Nikolaos Bakalos and Nikolaos Doulamis and Anastasios Doulamis and Athanasios Voulodimos},
  title = {Bidirectional long short-term memory networks and sparse hierarchical modeling for scalable educational learning of dance choreographies},
  abstract = {Recently, several educational game platforms have been proposed in the literature for choreographic training. However, their main limitation is that they fail to provide a quantitative assessment framework of a performing choreography against a groundtruth one. In this paper, we address this issue by proposing a machine learning framework exploiting deep learning paradigms. In particular, we introduce a long short-term memory network with the main capability of analyzing 3D captured skeleton feature joints of a dancer into predefined choreographic postures. This pose identification procedure is capable of providing a detailed (fine) evaluation score of a performing dance. In addition, the paper proposes a choreographic summarization architecture based on sparse modeling representative selection (SMRS) in order to abstractly represent the performing choreography through a set of key choreographic primitives. We have modified the SMRS algorithm in a way to extract hierarchies of key representatives. Choreographic summarization provides an efficient tool for a coarse quantitative evaluation of a dance. Moreover, hierarchical representation scheme allows for a scalable assessment of a choreography. The serious game platform supports advanced visualization toolkits using Labanotation in order to deliver the performing sequence in a formal documentation.},
  year = {2021},
  journal = {Visual Computer},
  volume = {37},
  number = {1},
  pages = {47-62},
  month = {jan},
  issn = {01782789 (ISSN)},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071584006&doi=10.1007%2fs00371-019-01741-3&partnerID=40&md5=3faead0ee88451ef6fe0c3d682443607},
  doi = {10.1007/s00371-019-01741-3},
  note = {Publisher: Springer Science and Business Media Deutschland GmbH},
  language = {English},
}