02459nas a2200373   4500000000100000008004100001653002300042653001900065653002000084653001000104653001300114653001100127653002100138653001500159653002300174653002200197653004200219653003400261653002400295653002100319653001600340653002500356653002200381653000900403653002200412653002700434100001800461700001300479245009600492856015300588490000700741520131700748022002002065                                       d10aAcceleration rates10aClassification10aComputer Vision10adance10aEmotions10aArtist10aExpress emotions10aExpression10aFacial Expressions10aFacial expression10aField programmable gate arrays (FPGA)10aIntangible cultural heritages10aLearning algorithms10amachine learning10aPerformance10aPerformance analysis10aPerformance based10aSDVM10aStage performance10aTechnical difficulties1 aJingjing Wang1 aPei Wang00aSimulation of dance performance analysis with microprocessor and machine vision interaction  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097714039&doi=10.1016%2fj.micpro.2020.103625&partnerID=40&md5=e7fcc3571603a248855b0b5bd4f336a00 v803 aDance is the expression of artists  favourite art forms such as express emotions, their body language, and the combination of dance art and stage effects in dance performance. In almost all genres of the art form, the effectiveness of the dance performance is largely due to the quality of the individual dancer s and group dance performance. Performing arts, particularly dance, it is one of the most important of intangible cultural heritage. However, due to the preservation, documentation, analysis, and visualization understanding of dance mode, it is difficult because of technical difficulties relations. The Proposed Machine Learning Support Decision Vector Machine (SDVM) algorithm and Field Programmable Gate Array (FPGA) is a dance expert watching dance due to the recognition task, the task knowledge of professional forecasters, gestures, and facial expressions and face-to-face conditions led to better synchronization of timing. In the proposed Machine learning SDVM algorithm, the results show that positive and dancers in the audience increased negative emotions; acceleration rate and body movement also increased. SDVM is classified as dancer performance based on the artist s facial expressions, stage performance, emotions. The simulation results show good results compared to other methods.  a01419331 (ISSN)