02678nas a2200409   4500000000100000000000100001000000100002008004100003260000800044653002300052653001000075653002400085653001800109653002500127653002400152653003200176653002900208653003300237653003400270653002100304653002700325653002400352653002900376653002900405653001800434100001900452700002100471700002200492700002400514700002600538245014100564856015100705300001000856490000700866520137500873022002002248       2021                            d  cjan10aBidirectional LSTM10aBrain10aDance summarization10aDeep learning10aEducational learning10aGesture recognition10aHierarchical representation10aIdentification procedure10aIntangible cultural heritage10aIntangible cultural heritages10aLearning systems10aLong short-term memory10aPose identification10aQuantitative assessments10aRepresentative selection10aSerious games1 aIoannis Rallis1 aNikolaos Bakalos1 aNikolaos Doulamis1 aAnastasios Doulamis1 aAthanasios Voulodimos00aBidirectional long short-term memory networks and sparse hierarchical modeling for scalable educational learning of dance choreographies  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85071584006&doi=10.1007%2fs00371-019-01741-3&partnerID=40&md5=3faead0ee88451ef6fe0c3d682443607  a47-620 v373 aRecently, 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.  a01782789 (ISSN)