01519nas a2200325   4500000000100000000000100001008004100002260004000043653001500083653001600098653003300114653003400147653001800181653001900199653002400218653002100242653002700263653001900290100002900309700002600338700002400364700002900388700002000417245010000437856014800537300001200685490001700697520045400714020002501168       2017                            d  bAssociation for Computing Machinery10aFolk dance10aFolk dances10aIntangible cultural heritage10aIntangible cultural heritages10aKinect sensor10aKinect sensors10aPattern recognition10aPosture analysis10aPosture identification10aSoft computing1 aEftychios Protopapadakis1 aAthanasios Voulodimos1 aAnastasios Doulamis1 aStephanos Camarinopoulos1 aAssoc Machinery00aA Study on the Use of Kinect Sensor in Traditional Folk Dances Recognition via Posture Analysis  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85025118340&doi=10.1145%2f3056540.3076200&partnerID=40&md5=18dd0698978acf62a18ad43eab81494a  a305-3100 vPart F1285303 aIn this paper, we evaluate the performance of widely applied soft computing classifiers, in folk dance recognition problems, emphasizing on posture identification. In particular, the goal is to identify postures which are characteristic for the dance performed, based on exploiting simultaneously the information of 24 body joints, acquired by a Kinect II sensor. The data sets described 6 folk dances, and their variations, originating from Greece.  a9781450352277 (ISBN)