Author:
Zhang Jun,Ren Yuanshi,Lin Liyue,Xing Yu,Ren Jie
Abstract
AbstractAction recognition has been applied in fields such as smart homes, gaming, traffic management, and security monitoring. Motion recognition is helpful for biomechanical analysis, auxiliary training systems, table tennis robots, motion-sensing games, virtual reality and other fields. In our study, we collected data on table tennis skill motion, created the TTMD6 dataset, and analyzed the characteristics of table tennis paddle trajectories. We propose a motion recognition algorithm to recognize paddle trajectories. Other research has used multijoint data to identify actions, while we use only the paddle trajectory to recognize table tennis skill motions, accelerating the speed of motion recognition. Therefore, it is feasible to use paddle trajectories to recognize table tennis skill motions.
Publisher
Springer Science and Business Media LLC
Reference32 articles.
1. Junxiao, B., Cuilin, B., Xiang, Z. & Jinli, W. Deep learning algorithm in biomedical engineering in intelligent automatic processing and analysis of sports images. Wirel. Commun. Mob. Comput. 2022, 1–10 (2022).
2. Bao, J., Tuo, M., Hou, T. M., Li, Y. X. & Wang, Q. Research on intelligent medical engineering analysis and decision based on deep learning. Int. J. Web Serv. Res. (IJWSR) 19, 1–9 (2022).
3. Zhenyu, N. Voice detection and deep learning algorithms application in remote english translation classroom monitoring. Mob. Inf. Syst. 2022, 1–10 (2022).
4. Landa, V. & Reuveni, Y. Low-dimensional convolutional neural network for solar flares GOES time-series classification. Astrophys. J. Suppl. Ser. 258, 12 (2022).
5. Sablok, S., Gururaj, G., Shaikh, N., Shiksha, I. & Choudhary, A. R. in 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS).