@inproceedings{3834,
  keywords = {AI for Serious Games, Background subtraction, Classification performance, Convolution, Convolutional neural networks, convolutional neural network, Data set, Deep neural networks, Gesture recognition, Intangible cultural heritage, Intangible cultural heritages, Learning schemes, Moving averages, Neural networks, Posture identification, Serious games, Virtual reality},
  author = {Nikolaos Bakalos and Ioannis Rallis and Nikolaos Doulamis and Anastasios Doulamis and Eftychios Protopapadakis and Athanasios Voulodimos},
  title = {Choreographic Pose Identification using Convolutional Neural Networks},
  abstract = {In 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},
  year = {2019},
  booktitle = {Int. Conf. Virtual Worlds Games Serious Appl., VS-Games - Proc.},
  pages = {95-101},
  publisher = {Institute of Electrical and Electronics Engineers Inc.},
  school = {Institute of Electrical and Electronics Engineers Inc.},
  isbn = {9781538671238 (ISBN)},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074251800&doi=10.1109%2fVS-Games.2019.8864522&partnerID=40&md5=8c2cdbe30c5a08fee23b3e43e5a70711},
  doi = {10.1109/VS-Games.2019.8864522},
  note = {Journal Abbreviation: Int. Conf. Virtual Worlds Games Serious Appl., VS-Games - Proc.},
  language = {English},
}