02433nas a2200433   4500000000100000000000100001008004100002653002800043653001600071653002300087653001800110653003300128653003400161653002100195653002700216653002100243653002100264653002800285653003700313653002000350653002200370653002100392653001700413653001800430653002300448653001000471100002100481700001900502700002200521700002400543700002600567700002400593245009100617856014900708300001000857490000700867520110500874022002001979                                       d10aBi-directional analysis10aConvolution10aCultural heritages10aDeep learning10aIntangible cultural heritage10aIntangible cultural heritages10aLearning systems10aLong short-term memory10aLow-cost sensors10amachine learning10aMonitoring capabilities10aMotion Primitives Classification10aMotion analysis10aMotion primitives10aProcessing layer10aSerious Game10aSerious games10aVisual information10adance1 aNikolaos Bakalos1 aIoannis Rallis1 aNikolaos Doulamis1 aAnastasios Doulamis1 aAthanasios Voulodimos1 aVassilios Vescoukis00aMotion Primitives Classification Using Deep Learning Models for Serious Game Platforms  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85084052185&doi=10.1109%2fMCG.2020.2985035&partnerID=40&md5=b5bfb5879e8524910a8ae112d1fe5daf  a26-380 v403 aSerious games are receiving increasing attention in the field of cultural heritage (CH) applications. A special field of CH and education is intangible cultural heritage and particularly dance. Machine learning (ML) tools are necessary elements for the success of a serious game platform since they introduce intelligence in processing and analysis of users  interactivity. ML provides intelligent scoring and monitoring capabilities of the user s progress in a serious game platform. In this article, we introduce a deep learning model for motion primitive classification. The model combines a convolutional processing layer with a bidirectional analysis module. This way, RGB information is efficiently handled by the hierarchies of convolutions, while the bidirectional properties of a long short term memory (LSTM) model are retained. The resulting convolutionally enhanced bidirectional LSTM (CEBi-LSTM) architecture is less sensitive to skeleton errors, occurring using low-cost sensors, such as Kinect, while simultaneously handling the high amount of detail when using RGB visual information.  a02721716 (ISSN)