Design and data analysis of a wearable basketball training posture measurement system based on multifunctional conjugated polymer composite materials

  • Yunzhang Hu College of Sports and Health, Anhui University of Traditional Chinese Medicine, Hefei 230032, China
  • He Huang College of Sports and Health, Anhui University of Traditional Chinese Medicine, Hefei 230032, China
Keywords: conjugated materials; dynamic motion analysis system for basketball; computer vision algorithm; inertial measurement Units; MotionPro basketball analytics software; basketball player’s posture measurement
Article ID: 430

Abstract

Conjugated materials in basketball training are specific polymers included in sportswear to record and analyze player motions, helping to improve skills and prevent injuries by offering an in-depth analysis of the biomechanics and movements of athletes during training sessions. These materials provide basketball players with lightweight, long-lasting, and versatile qualities, offering comfortable gear that precisely monitors movements, complementing their training requirements for enhanced performance and technique improvement. This article describes creating and examining a novel wearable basketball conditioning posture assessment system called DMAS4B (Dynamic Motion Analysis System for Basketball). The technology includes sophisticated computer vision algorithms (CVA-Kalman Fusion Algorithm), Inertial Measurement Units (IMUs), and versatile, conjugated polymer composite materials. These materials, strategically positioned within specially developed sportswear, enable real-time tracking and evaluation of basketball player locations during training sessions. DMAS4B includes gathering detailed body movement data and focusing on essential basketball skills like shooting technique, dribbling stance, and defensive alignments. The collected data is delivered wirelessly to the MotionPro+ Basketball Analytics Software, a specialized platform for thorough analysis and visualization. The ability of IMUs, multifunctional conjugated polymer composites, and computer vision algorithms to work together to record and analyze basketball player movements precisely is demonstrated in this study. The system’s implementation seeks to connect traditional training methods with advanced technology, providing athletes and coaches instant and thorough feedback on posture accuracy, balance, and mastery of techniques. The comprehensive examination of data collected from DMAS4B offers a novel method to improve basketball training programs, enhance player performance, and reduce the likelihood of injuries. In addition, the flexible character of this technology provides a foundation for possible use in various sports, transforming customised training methods worldwide.

References

1. Li, S., Zhang, B., Fei, P., Shakeel, P. M., & Samuel, R. D. J. (2020). Computational efficient wearable sensor network health monitoring system for sports athletics using IoT. Aggression and Violent Behavior, 101541.

2. Cui, C., Fu, Q., Meng, L., Hao, S., Dai, R., & Yang, J. (2020). Recent progress in natural biopolymers conductive hydrogels for flexible wearable sensors and energy devices: materials, structures, and performance. ACS Applied Biomaterials, 4(1), 85-121.

3. Hou, Z., Liu, X., Tian, M., Zhang, X., Qu, L., Fan, T., & Miao, J. (2023). Smart fibers and textiles for emerging clothe-based wearable electronics: materials, fabrications and applications. Journal of Materials Chemistry A, 11(33), 17336-17372.

4. Zosimadis, I., & Stamelos, I. (2023). A Novel Internet of Things-Based System for Ten-Pin Bowling. IoT, 4(4), 514-533.

5. Pan, D., Hu, J., Wang, B., Xia, X., Cheng, Y., Wang, C. H., & Lu, Y. (2023). Biomimetic Wearable Sensors: Emerging Combination of Intelligence and Electronics. Advanced Science, 2303264.

6. Nag, A., Alahi, M. E. E., Mukhopadhyay, S. C., & Liu, Z. (2021). Multi-walled carbon nanotubes-based sensors for strain sensing applications. Sensors, 21(4), 1261.

7. Seesaard, T., & Wongchoosuk, C. (2023). Flexible and stretchable pressure sensors: from basic principles to state-of-the-art applications. Micromachines, 14(8), 1638.

8. Saucier, D. (2020). Application of soft robotic sensors to predict foot and ankle kinematic measurements. Mississippi State University.

9. Li, B. M. (2021). Compliant Design Approaches for E-Textile Systems. North Carolina State University.

10. Zhang, L., Sun, Y., Wang, M., & Pu, Y. (2021). Wearable product design for intelligent monitoring of basketball training posture based on image processing. Journal of Sensors, 2021, 1-15.

11. Zhang, S., Zhang, J., & Zhou, X. (2022). Design and Development of Smart Wearable Products for Basketball Dribble Teaching Training Posture Monitoring. Wireless Communications and Mobile Computing, 2022.

12. Hou, X., & Qi, B. (2022). Basketball Training Posture Monitoring Based on Intelligent Wearable Device. Mobile Information Systems, 2022.

13. Jiang, L., & Zhang, D. (2023). Deep Learning Algorithm based Wearable Device for Basketball Stance Recognition in Basketball. International Journal of Advanced Computer Science and Applications, 14(3).

14. Ren, H., & Wang, X. (2021). Application of wearable inertial sensor in optimization of basketball player’s human motion tracking method. Journal of Ambient Intelligence and Humanized Computing, 1-15.

15. Hou, S., Lian, B., Li, W., & Tang, H. (2022). A Basketball Training Posture Monitoring Algorithm Based on Machine Learning and Artificial Intelligence. Mobile Information Systems, 2022.

16. Tang, B., & Guan, W. (2022). CNN Multi-Position Wearable Sensor Human Activity Recognition Used in Basketball Training. Computational Intelligence and Neuroscience, 2022.

17. Worsey, M. T. O. (2021). Understanding and interpretation of wearable inertial sensor output in human monitoring.

18. Zhao, Y., & You, Y. (2021). Design and data analysis of wearable sports posture measurement system based on Internet of Things. Alexandria Engineering Journal, 60(1), 691-701.

19. Sharma, S., Sudhakara, P., Omran, A. A. B., Singh, J., & Ilyas, R. A. (2021). Recent trends and developments in conducting polymer nanocomposites for multifunctional applications. Polymers, 13(17), 2898.

20. https://www.kaggle.com/datasets/matthewjohnson14/nba-player-shooting-motions

21. Gao, Y., Wang, J., Li, Z., & Peng, Z. (2023). The Social Media Big Data Analysis for Demand Forecasting in the Context of Globalization: Development and Case Implementation of Innovative Frameworks. Journal of Organizational and End User Computing, 35(3), 1-15.

22. Meng, F., Jiang, S., Moses, K., & Wei, J. (2023). Propaganda Information of Internet Celebrity Influence: Young Adult Purchase Intention by Big Data Analysis. Journal of Organizational and End User Computing, 35(1), 1-18.

23. Cunbao Deng, Xiaobo Wang, Yafei Shan, Zhiqiang Song. Study on the effect of low molecular hydrocarbon compounds on coal spontaneous combustion. Fuel,2022, 318,123193, https://doi.org/10.1016/j.fuel.2022.123193.

24. Zhao, Y., Bai, C., Xu, C., & Foong L. K. (2021).Efficient metaheuristic-retrofitted techniques for concrete slump simulation. Smart Structures and Systems,27 (5), 745-759.

25. Mengzhen Lv, Xiyue Cao, Meichen Tian, Rong Jiang, Chengjin Gao, Jianfei Xia, Zonghua Wang. A novel electrochemical biosensor based on MIL-101-NH2 (Cr) combining target-responsive releasing and self-catalysis strategy for p53 detection,Biosensors and Bioelectronics.Biosensors and Bioelectronics 2022, 214, 114518.

26. Xiyue Cao, Jianfei Xia, Xuan Meng, Jiaoyan Xu,Qingyun Liu, Zonghua Wang. Stimuli-Responsive DNA-Gated Nanoscale Porous Carbon Derived from ZIF-8. Adv. Funct. Mater. 2019, 29, 1902237.

Published
2024-11-20
How to Cite
Hu, Y., & Huang, H. (2024). Design and data analysis of a wearable basketball training posture measurement system based on multifunctional conjugated polymer composite materials. Molecular & Cellular Biomechanics, 21(3), 430. https://doi.org/10.62617/mcb430
Section
Article