Gymnastic movement recognition based on support vector machine classification model

[1] Ding, C., Hong, H., Zou, Y., et al. (2019). Continuous human motion recognition with a dynamic range-Doppler trajectory method based on FMCW radar. IEEE Transactions on Geoscience and Remote Sensing, 57(9), 6821-6831. Search in Google Scholar

[2] Chen, P., Wang, X., Wang, M., et al. (2021). Multi-view real-time human motion recognition based on ensemble learning. IEEE Sensors Journal, 21(18), 20335-20347. Search in Google Scholar

[3] Southwick, M., Mao, Z., & Niezrecki, C. (2021). Volumetric motion magnification: subtle motion extraction from 4d data. Measurement, 176(4), 109211. Search in Google Scholar

[4] Wang, P., Li, W., Ogunbona, P. O., et al. (2018). RGB-D-based human motion recognition with deep learning: A survey. Academic Press, 1. Search in Google Scholar

[5] Gurbuz, S. Z., Amin, M. G. (2019). Radar-based human-motion recognition with deep learning: Promising applications for indoor monitoring. IEEE Signal Processing Magazine, 36(4), 16-28. Search in Google Scholar

[6] Luo, J., Liu, C., Feng, Y., et al. (2020). A method of motion recognition based on electromyographic signals. Advanced Robotics, 34(15), 976-984. Search in Google Scholar

[7] Rajput, A. S. (2021). Scientific writing: an analysis of pune‐based climate scientists’ perceptions and training needs. Weather, (1). Search in Google Scholar

[8] Shi, L., Zhang, Y., Cheng, J., et al. (2019). Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks. IEEE Transactions on Circuits and Systems for Video Technology, 29(11), 3247-3257. Search in Google Scholar

[9] Yoon, Y., Yu, J., Jeon, M. (2021). Predictively encoded graph convolutional network for noise-robust skeleton-based action recognition. Applied Intelligence, 2021(8), 1-15. Search in Google Scholar

[10] Domingo, J. D., Gómez-García-Bermejo, J., Zalama, E. (2021). Visual recognition of gymnastic exercise sequences. Application to supervision and robot learning by demonstration. Robotics and Autonomous Systems. Search in Google Scholar

[11] Kok, L. Y., & Teh, L. H. (2013). Movement Pattern Recognition Ability Of Malaysian Rhythmic Gymnastics Judges. Mohe.gov.my, 2, 15-30. Search in Google Scholar

[12] Shi, T. (2020). Application of VR image recognition and digital twins in artistic gymnastics courses. Journal of Intelligent and Fuzzy Systems, 40(2), 1-12. Search in Google Scholar

[13] Wang, M., Zhang, Y. D., & Cui, G. (2019). Human motion recognition exploiting radar with stacked recurrent neural network. Digital Signal Processing, 87, 125-131. Search in Google Scholar

[14] Kim, K., & Cho, Y. K. (2020). Effective inertial sensor quantity and locations on a body for deep learning-based worker’s motion recognition. Automation in Construction, 113, 103126. Search in Google Scholar

[15] Fang, L., & Sun, M. (2021). Motion recognition technology of badminton players in sports video images. Future Generation Computer Systems, 124, 381-389. Search in Google Scholar

[16] Pang, X., Zhang, Y., & Xu, Y. (2022). A novel multi-task twin-hypersphere support vector machine for classification. Information Sciences, 598, 37-56. Search in Google Scholar