@article{4584,
  author = {Binbin Gong},
  title = {Exercise Movement Recognition Using 3D Convolutional Neural Networks for Intelligent Physical Education},
  journal = {Journal of Multimedia Processing and Technologies},
  year = {2025},
  volume = {16},
  number = {4},
  doi = {https://doi.org/10.6025/jmpt/2025/16/4/175-183},
  url = {https://www.dline.info/jmpt/fulltext/v16n4/jmptv16n4_1.pdf},
  abstract = {This paper presents an exercise movement recognition system based on 3D Convolutional Neural Networks (3D CNNs) to enhance intelligent exercise teaching. The author, Binbin Gong, addresses limitations of traditional instruction such as lack of real-time, personalized feedback by leveraging 3D CNNs to extract spatiotemporal features from exercise videos for accurate motion classification. The model architecture includes four convolutional layers, two max pooling layers, two fully connected layers, and a Softmax classifier. Evaluated on the MSRAction3D dataset, the system achieved an average recognition accuracy of 91%, with individual action accuracies ranging from 81% to 98%. The study reviews current intelligent technologies in exercise education, including pose estimation, VR, and deep learning methods like RNNs and CNNs, both internationally and domestically. Results demonstrate the model's strong potential to support instructors with real-time movement analysis and personalized student feedback, thereby improving teaching effectiveness and learner experience in physical education.},
}