The construction of sports training quality evaluation model based on sensor data
Abstract
Based on the visual quality of the training evaluation as a technology is closely related to People’s Daily life, has the very high research value, and in the near future will be in medical, health, security, etc. has a broad application prospect. In the human body based on visual training quality evaluation in the research of this field, human action recognition technology is one of the core technology, is also a research hotspot recently. The technology is mainly to solve the classification problem of human actions, but in practice, it is not enough to study classification problem, sometimes need to evaluate the quality of the completion of human action, namely human motion evaluation technology, provides the user with feedback, correct mistakes, thus improve the action degree of standardization. This paper analyzes and compares all kinds of action selection radio gymnastic sports training as a quality evaluation object, and using Microsoft’s depth camera device as acquisition equipment, data to obtain three-dimensional skeleton of the body joints. Human bone joint data from the device has the problem of “distortion”, this paper adopted the average filtering algorithm for data preprocessing, the experimental results show that average filtering algorithm can not only protect. The high sensitivity and good smoothing effect.
References
1. She, H. (2024). Application of big data analysis in model construction to prevent athlete injury in training. Applied Mathematics and Nonlinear Sciences, 9(1).
2. Li, P., Jie, X., & Li, X. (2024). A study on the evaluation of the effect of theme songs in sports events: The construction of index system. International Conference on Management Science and Engineering Management. Springer, Singapore.
3. Wang, D., Wang, S., Hou, J., & Yin, M. (2023). Construction of a sport-specific strength and conditioning evaluation index system for elite male wheelchair badminton athletes by the Delphi method.
4. Wang, C., Tang, M., Xiao, K., Wang, D., & Li, B. (2024). Optimization system for training efficiency and load balance based on the fusion of heart rate and inertial sensors. Preventive Medicine Reports, 41.
5. Zhan, X., Liu, H., & Tan, D. (2024). Exploration of the construction path of sports injury and first aid course systems for college students. Journal of Clinical and Nursing Research, 8(4), 105-109.
6. Wang, F., & Huang, Q. (2022). Construction and evaluation of sports rehabilitation training model under intelligent health monitoring. Wireless Communications & Mobile Computing.
7. Yang, J., & Chen, M. (2022). Construction of sports and health data resources and transformation of teachers’ orientation based on web database. Journal of Healthcare Engineering, 2022.
8. Ma, W., & Guo, B. (2024). Construction of neural network model for exercise load monitoring based on yoga training data and rehabilitation therapy. Heliyon, 10(12).
9. Chen, G., & Wu, H. (2024). Optimization simulation of sports stadium training based on ant colony algorithm and sensor network. Measurement: Sensors, 33.
10. Yang, Y., Yu, Y., Chen, X., & Li, J. (2024). Analysis of the motion postures in equestrian sports based on multi-sensor data fusion. International Journal of Pattern Recognition & Artificial Intelligence, 38(6).
11. Nikolis, L., Graff, C., Nikolis, A., & Tow, S. (2024). Wearable electronic devices in parasports: A focused review on para athlete classification. PM & R: Journal of Injury, Function & Rehabilitation, 16(4).
12. Blauberger, P., Fukushima, T., Russomanno, T. G., & Lames, M. (2024). A pilot study in sensor instrumented training (SIT)-Ground contact time for monitoring fatigue and curve running technique. International Journal of Computer Science in Sport, 23(1), 80-92.
13. Komaini, A., Illahi, F. D., Gusril, Sin, T. H., Handayani, S. G., & Yohandri, et al. (2022). Volleyball smash test instrument design with sensor technology. Journal of Physics: Conference Series, 2309(1).
14. Indrakasih, Sinulingga, A., Lumbaraja, F., & Pasaribu, A. (2022). Development of test forms of down passing techniques in sensor-based volleyball games. Journal Sport Area.
15. Teh, H.Y., Kempa-Liehr, A.W. & Wang, K.IK. Sensor data quality: a systematic review. J Big Data 7, 11 (2020). https://doi.org/10.1186/s40537-020-0285-1
16. Yang J and Lv W, Optimization of Sports Training Systems Based on Wireless Sensor Networks Algorithms, IEEE Sensors Journal, 2021,21(22), 25075-25082, doi: 10.1109/JSEN.2020.3046290.
17. Bin Y, M. M. K, Shaonan S, Application of Motion Sensor Based on Neural Network in Basketball Technology and Physical Fitness Evaluation System, Wireless Communications and Mobile Computing, 2021, 5562954, https://doi.org/10.1155/2021/5562954
Copyright (c) 2024 Wandong Pan, Jie Guo, Shu Zhang, Yuehua Fu
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.