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3D Body Sensor-Based Martial Arts Demonstration and Teaching System

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Published:04 April 2023Publication History

ABSTRACT

The existing human action recognition methods have imperfect models, resulting in low recognition accuracy, lack of famous teacher resources, shortage of judges, and strong subjectivity in competition and test scoring. Based on Kinect 2.0, through Unity3D program development and 3D character model development, this system designs innovative continuous action algorithms ( mainly including segmented depth search method and weighted matching method), and realizes Chinese martial arts action demonstration, standard action entry, martial arts action scoring and other functions

References

  1. Li, Y. and Wang, L. Human Activity Recognition Based on Residual Network and BiLSTM. Sensors, 22, 2 (2022), 635.Google ScholarGoogle ScholarCross RefCross Ref
  2. Basak, H., Kundu, R., Singh, P. K., Ijaz, M. F., Woźniak, M. and Sarkar, R. A union of deep learning and swarm-based optimization for 3D human action recognition. Scientific Reports, 12, 1 (2022), 1-17.Google ScholarGoogle ScholarCross RefCross Ref
  3. Chen, G. and Wei, S. Fusion sampling networks for skeleton-based human action recognition. Journal of Electronic Imaging, 31, 5 (2022), 053015.Google ScholarGoogle ScholarCross RefCross Ref
  4. Lu, Y., Yu, H., Ni, W. and Song, L. 3D real-time human reconstruction with a single RGBD camera. Applied Intelligence (2022), 1-11.Google ScholarGoogle Scholar
  5. An, J., Cheng, X., Wang, Q., Chen, H., Li, J. and Li, S. Human action recognition based on Kinect. IOP Publishing, City, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  6. Wang, L., Huynh, D. Q. and Koniusz, P. A comparative review of recent kinect-based action recognition algorithms. IEEE Transactions on Image Processing, 29 (2019), 15-28.Google ScholarGoogle ScholarCross RefCross Ref
  7. Cao, C., Shan, B. and Zhang, H. Pattern Recognition of Wushu Routine Action Decomposition Process Based on Kinect. Mathematical Problems in Engineering, 2022 (2022).Google ScholarGoogle Scholar
  8. Li, B., Han, C. and Bai, B. Hybrid approach for human posture recognition using anthropometry and BP neural network based on Kinect V2. EURASIP Journal on Image and Video Processing, 2019, 1 (2019), 1-15.Google ScholarGoogle Scholar
  9. Wu, Q., Xu, G., Chen, L., Luo, A. and Zhang, S. Human action recognition based on kinematic similarity in real time. PloS one, 12, 10 (2017), e0185719.Google ScholarGoogle Scholar
  10. Islam, M. S., Bakhat, K., Khan, R., Naqvi, N., Islam, M. M. and Ye, Z. Applied Human Action Recognition Network Based on SNSP Features. Neural Processing Letters (2022), 1-14.Google ScholarGoogle Scholar
  11. Li, A., Zhang, R. and Tao, L. Recognition Method of Wushu Human Complex Movement Based on Bone Point Feature. Computational and Mathematical Methods in Medicine, 2022 (2022).Google ScholarGoogle Scholar
  12. Jain, V., Gupta, G., Gupta, M., Sharma, D. K. and Ghosh, U. Ambient intelligence-based multimodal human action recognition for autonomous systems. ISA Transactions (2022).Google ScholarGoogle Scholar
  13. Rashmi, M. and Guddeti, R. M. R. Human identification system using 3D skeleton-based gait features and LSTM model. Journal of Visual Communication and Image Representation, 82 (2022), 103416.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Jin, Z., Li, Z., Gan, T., Fu, Z., Zhang, C., He, Z., Zhang, H., Wang, P., Liu, J. and Ye, X. A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition. Sensors, 22, 9 (2022), 3524.Google ScholarGoogle Scholar
  15. Cha, J., Saqlain, M., Kim, D., Lee, S., Lee, S. and Baek, S. Learning 3d skeletal representation from transformer for action recognition. IEEE Access, 10 (2022), 67541-67550.Google ScholarGoogle ScholarCross RefCross Ref
  16. Chen, Y., Li, Y., Zhang, C., Zhou, H., Luo, Y. and Hu, C. Informed Patch Enhanced HyperGCN for skeleton-based action recognition. Information Processing & Management, 59, 4 (2022), 102950.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Tang, C., Tong, A., Zheng, A., Peng, H. and Li, W. Using a selective ensemble support vector machine to fuse multimodal features for human action recognition. Computational Intelligence and Neuroscience, 2022 (2022).Google ScholarGoogle Scholar
  18. Guanqi, M. The Design and Implementation of the Motion Capture and Comparison System Based on Kinects. MA, Shandong University, 2017.Google ScholarGoogle Scholar
  19. Dawei, L. and Hao, L. Design and implementation of science and technology museum exhibits based on Kinect augmented reality technology. Journal of Natural Science Museum Research, 2, S2 (2017), 124-127.Google ScholarGoogle Scholar
  20. Chen, L., Xing, Z., Zhuocheng, Z., Haiming, L. and Dongwei, C. Design and implementation of virtual dressing room system based on cloud platform. Information Technology, 01 (2017), 39-43.Google ScholarGoogle Scholar
  21. Kaiyang, L., Huamao, Y., Xiaoguang, L., Yushu, B., Zhengmei, X., Hongxing, S., Zhiqing, Z. and Zaiping, J. Application and prospect of Kinect body-feeling technology in teaching of animal surgical operations. China Medical Education Technology, 26, 02 (2012), 171-173.Google ScholarGoogle Scholar
  22. Zhixuan, W. Construction of athlete gait analysis model based on computer Kinect. PC Fan, 07 (2017), 165.Google ScholarGoogle Scholar

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          ICNCC '22: Proceedings of the 2022 11th International Conference on Networks, Communication and Computing
          December 2022
          365 pages
          ISBN:9781450398039
          DOI:10.1145/3579895

          Copyright © 2022 ACM

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          Publication History

          • Published: 4 April 2023

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