@article{13169,
  keywords = {Acceleration rates, Classification, Computer Vision, dance, Emotions, Artist, Express emotions, Expression, Facial Expressions, Facial expression, Field programmable gate arrays (FPGA), Intangible cultural heritages, Learning algorithms, machine learning, Performance, Performance analysis, Performance based, SDVM, Stage performance, Technical difficulties},
  author = {Jingjing Wang and Pei Wang},
  title = {Simulation of dance performance analysis with microprocessor and machine vision interaction},
  abstract = {Dance 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.},
  volume = {80},
  issn = {01419331 (ISSN)},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097714039&doi=10.1016%2fj.micpro.2020.103625&partnerID=40&md5=e7fcc3571603a248855b0b5bd4f336a0},
  doi = {10.1016/j.micpro.2020.103625},
  note = {Publisher: Elsevier B.V.},
}