02131nas a2200409   4500000000100000000000100001008004100002260005900043653002500102653002700127653003100154653001600185653003400201653003300235653001300268653002500281653002400306653003300330653003400363653002100397653002000418653002000438653002700458653001800485653002000503100002100523700001900544700002200563700002400585700002900609700002600638245007400664856015400738300001100892520079300903020002501696       2019                            d  bInstitute of Electrical and Electronics Engineers Inc.10aAI for Serious Games10aBackground subtraction10aClassification performance10aConvolution10aConvolutional neural networks10aconvolutional neural network10aData set10aDeep neural networks10aGesture recognition10aIntangible cultural heritage10aIntangible cultural heritages10aLearning schemes10aMoving averages10aNeural networks10aPosture identification10aSerious games10aVirtual reality1 aNikolaos Bakalos1 aIoannis Rallis1 aNikolaos Doulamis1 aAnastasios Doulamis1 aEftychios Protopapadakis1 aAthanasios Voulodimos00aChoreographic Pose Identification using Convolutional Neural Networks  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85074251800&doi=10.1109%2fVS-Games.2019.8864522&partnerID=40&md5=8c2cdbe30c5a08fee23b3e43e5a70711  a95-1013 aIn this paper we present a deep learning scheme for classification of dance postures using Kinect II RGB data and Convolutional Neural Networks (CNN). This is achieved through the analysis of a data-set that includes three traditional Greek dances, where each dance was performed by 3 different dancers. The obtained data were processed and analyzed using a deep convolutional neural network, in order to identify the primitive postures that comprise the choreography. To enhance the classification performance, a background subtraction framework was utilized, while the CNN architecture was adapted to simulate a moving average behavior. The overall system can be used as an AI module for assessing the performance of users in a serious game for learning traditional dance choreographies  a9781538671238 (ISBN)