@article{hanliang_lina_2022, 
  title={Investigation on the Use of Virtual Reality in the Flipped Teaching of Martial Arts Taijiquan Based on Deep Learning and Big Data Analytics}, 
  volume={2022}, 
  DOI={10.1155/2022/3921842}, 
  abstractNote={<jats:p>Flipping classroom teaching of martial arts "Taijiquan" is a contemporary teaching method that primarily uses information technology tools to accomplish the natural fusion of information technology and education and teaching. Digital teaching materials influence the conventional classroom. The conventional teaching approach of teacher instruction and student learning is no longer satisfactory to students. Virtual reality (VR) technology and physical education integration and development have currently emerged as a new trend, but research on its application to martial arts is still in its theoretical stages and lacks concrete application countermeasures and schemes, necessitating further investigation and study. This paper intends to investigate the use of virtual reality technology in martial arts education. The architecture of virtual reality based on the deep learning algorithm is suggested. The student dataset is collected, and they are split into two groups: control and study groups. Traditional teaching is provided to the control group, and deep learning-based VR-assisted teaching is provided to the study group using the Deep Binary Hashed Convolutional Neural Network (DBH-CNN). Statistical analysis is done using Student's <jats:inline-formula>
                     <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1">
                        <mi>t</mi>
                     </math>
                  </jats:inline-formula>-test, logistic regression analysis, and analysis of variance (ANOVA). According to the study findings, the majority of students see virtual reality technology-assisted martial arts education favorably, and their passion for studying martial arts has greatly increased.</jats:p>}, 
  publisher={Hindawi Limited}, 
  author={HanLiang, Zhang and LiNa, Zhang}, 
  editor={Bhattacharya, Sweta}, 
  year={2022}, 
  month={Oct}
  }