Sports information processing and physiological condition monitoring system based on multimedia computer

  • Xu Xu Department of Physical Education, Hebei Agricultural University, Baoding 071000, China
  • Qian Zhao Department of Basic Engineering, Shijiazhuang Engineering Vocational College, Shijiazhuang 050000, China
  • Tingting Yang P.E. Group, Ministry of Public Education, Baoding Preschool Teachers College, Baoding 072750, China
  • Xiaomei Liu Physical Education Group, Cangzhou No. 11 Middle School, Cangzhou 061000, China
Keywords: multimedia computer; sports information processing and monitoring system; sports information data analysis; physiological status monitoring; biomedical transducer
Article ID: 994

Abstract

Traditional sports information processing methods often rely on manual observation and recording. This method is not only inefficient, but also susceptible to subjective bias, which affects the accuracy and reliability of the data. In this paper, a sports information processing and physiological condition monitoring system based on multimedia computer is constructed, which deeply integrates multimedia technology, computer technology and biomedical sensing technology. Through the integration of advanced multimedia processors and a variety of biosensors, the system can collect, process and analyze the physiological data and sports trajectory information of athletes in sports events in real time, so as to achieve comprehensive and accurate monitoring of the status of athletes. Using data compression algorithms, each byte can store two bits of data, reducing the space occupied by system operation. In terms of functional implementation, this system not only provides a user management module to ensure the security authentication of user identity, but also is equipped with a sports information data analysis module, which can provide users with scientific training guidance and optimize training plans. Experiments show that the system constructed in this article is functionally tested and all functions meet the design expectations; within 500 m, the packet loss rate of the system is 0; when the number of users reaches 1200, the response time of this system is 3.62 s; under low-intensity exercise and high-intensity exercise, the average accuracy of monitoring and early warning of users in this system is 90.44% and 95.11% respectively. The sports information processing and monitoring system can not only accurately and quickly collect and process various sports information, but also monitor and analyze the physiological data of athletes with high precision.

References

1. Sun Y, Hu J, Li G, et al. Gear reducer optimal design based on computer multimedia simulation. The Journal of Supercomputing. 2020; 76: 4132-4148. doi: 10.1007/s11227-018-2255-3

2. Qiao Y, Liu J, Wang X. Novel Multimedia Feature Fusion Classification (Mffc) Model for Sports Game Design Enhancement. Journal of Electrical Systems. 2024; 20: 1681-1692. doi: 10.1109/JSEN.2024.3310658

3. Wu G. Human health characteristics of sports management model based on the biometric monitoring system. Network Modeling Analysis in Health Informatics and Bioinformatics. 2022; 11(1): 18. https://doi.org/10.1007/s13721-022-00356-4

4. Cong C, Fu D. An AI based research on optimization of university sports information service. Journal of Intelligent & Fuzzy Systems. 2021; 40.2: 3313-3324. doi: 10.1007/978-3-030-64058-3_52

5. Thornton HR, Delaney JA, Duthie GM, et al. Developing athlete monitoring systems in team sports: data analysis and visualization. International journal of sports physiology and performance. 2019; 14.6: 698-705. doi: 10.1123/ijspp.2018-0169.

6. Varmus M, Kubina M, Miciak M, et al. Integrated Sports Information Systems: Enhancing Data Processing and Information Provision for Sports in Slovakia. Systems. 2024; 12.6: 198. doi: 10.3390/systems12060198

7. Han Z. Using adaptive wireless transmission of wearable sensor device for target heart rate monitoring of sports information. IEEE Sensors Journal. 2020; 21.22: 25027-25034. doi: 10.1109/JSEN.2020.3034434

8. Xu D, Huang M, Tang Q, et al. Design of motion monitoring system based on Unity3D and MEMS inertial sensing. Journal of Medical Intelligence. 2024; 45.8: 89-95. doi: 10.3969/j.issn.1673-6036.2024.08.015

9. Yang M, Zhang S. Analysis of sports psychological obstacles based on mobile intelligent information system in the era of wireless communication. Wireless Networks. 2023; 29.8: 3599-3615. doi: 10.1007/s11276-023-03419-0

10. Blobel T, Martin R, Martin L. Sports information systems: a systematic review. International Journal of Computer Science in Sport 20.1 (2021): 1-22. doi: 10.2478ijcss-2021-0001

11. Wang D. Application of multimedia computer technology in radio and television engineering. Modern Engineering Project Management 2.8 (2023): 46-48. doi: 10.37155/2811-0625-0208-16

12. Su X. Application of Sports Action Multimedia Database on Harvesting Robot Actuator. Journal of Agricultural Mechanization Research 44.6 (2022): 236-239. doi: 10.13427/j.cnki.njyi.2022.06.042

13. Orines F. Multimedia-Based Instruction in Physical Education and Sports. Psychology and Education: A Multidisciplinary Journal. 2023; 558-567.doi: 10.5281/zenodo.8127233

14. Sari SI, Toni H. The Influence of Computer Based Management Information Systems on The Performance of Youth and Sports Offices in North Sumatra Province. International Journal of Economics (IJEC) 1.1 (2022): 44-50. doi: 10.55299/ijec.v1i1.71

15. Shao G. Sports Information Communication Model Based on Network Technology. Mobile Networks and Applications 27.5 (2022): 1987-1994. doi: 10.1007/s11036-022-01969-9

16. Kotiash I, Shevchuk I, Borysonok M, et al. Possibilities of Using Multimedia Technologies in Education. International Journal of Computer Science and Network Security 22.6 (2022): 727-732. doi: 10.22937/IJCSNS.2022.22.6.91

17. Vaganova OI, Bakharev NP, Kulagina JA, et al.Multimedia technologies in vocational education. Amazonia investiga 9.26 (2020): 391-398. doi: 10.34069/AI/2020.26.02.45

18. Muxtarova LA. Use of multimedia technologies in the educational process. ACADEMICIA: An International Multidisciplinary Research Journal 11.4 (2021): 1781-1785. doi:10.5958/2278-4853.2021.00298.6

19. Jayasankar U, Vengattaraman T, Dhavachelvan P. A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications. Journal of King Saud University-Computer and Information Sciences 33.2 (2021): 119-140. doi: 10.1016/j.jksuci.2018.05.006

20. Zhang M, Zhang H, Fang Y, er al. Learning-based data transmissions for future 6G enabled industrial IoT: A data compression perspective. IEEE Network 36.5 (2022): 180-187. doi: 10.1109/MNET.109.2100384

21. Xu M, Jia Z, Wang J, et al. Statistical data compression and differential coding for digital radio-over-fiber-based mobile fronthaul. Journal of Optical Communications and Networking 11.1 (2019): A60-A71. doi: 10.1364/JOCN.11.000A60

22. Dai HN, Wong RCW, Wang H, et al. Big data analytics for large-scale wireless networks: Challenges and opportunities. ACM Computing Surveys (CSUR) 52.5 (2019): 1-36. doi: 10.1145/3337065

23. Nguyen DC,Cheng P, Ding M, et al. Enabling AI in future wireless networks: A data life cycle perspective. IEEE Communications Surveys & Tutorials 23.1 (2020): 553-595. doi: 10.1109/COMST.2020.3024783

24. Shen S, Xiao X, Chen J. Wearable triboelectric nanogenerators for heart rate monitoring. Chemical Communications 57.48 (2021): 5871-5879. doi: 10.1039/D1CC02091A

25. Bent B, Goldstein BA, Kibbe WA, Dunn JP. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ digital medicine 3.1 (2020): 18. doi: 10.1038/s41746-020-0226-6

26. Irawan Y, Yunior F, Refni W. Detecting Heart Rate Using Pulse Sensor as Alternative Knowing Heart Condition. Journal of Applied Engineering and Technological Science (JAETS) 1.1 (2019): 30-42. doi: 10.37385/jaets.v1i1.16

27. Teng L, Pan K, Nemitz MP, et al. Soft radio-frequency identification sensors: Wireless long-range strain sensors using radio-frequency identification. Soft robotics 6.1 (2019): 82-94. doi: 10.1089/soro.2018.0026

28. Yang C, Wang X, Mao S. RFID-pose: Vision-aided three-dimensional human pose estimation with radio-frequency identification. IEEE transactions on reliability 70.3 (2020): 1218-1231. doi: 10.1109/TR.2020.3030952

29. Shen G, Zhang J, Marshall A, et al. Radio frequency fingerprint identification for LoRa using deep learning. IEEE Journal on Selected Areas in Communications 39.8 (2021): 2604-2616. doi: 10.1109/JSAC.2021.3087250

30. Sun Y, Zhang Y, Guo D, et al. Intelligent distributed temperature and humidity control mechanism for uniformity and precision in the indoor environment. IEEE Internet of Things Journal 9.19 (2022): 19101-19115. doi: 10.1109/JIOT.2022.3163772

31. Guo T, Ge J, Jiao Y, Teng Y, et al. Intelligent matter endows reconfigurable temperature and humidity sensations for in-sensor computing. Materials Horizons 10.3 (2023): 1030-1041. doi: 10.1039/D2MH01491B

32. Cao C, Yang Y, Lu Y, et al. Performance evaluation of a smart mobile air temperature and humidity sensor for characterizing intracity thermal environment. Journal of Atmospheric and Oceanic Technology 37.10 (2020): 1891-1905. doi: 10.1175/JTECH-D-20-0012.1

33. Grobbelaar M, Phadikar S, Ghaderpour E, et al. A survey on denoising techniques of electroencephalogram signals using wavelet transform. Signals 3.3 (2022): 577-586. https://doi.org/10.3390/signals3030035.

34. Arts Lukas PA, van den Broek EL. The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time–frequency analysis. Nature Computational Science 2.1 (2022): 47-58. doi: 10.1038/s43588-021-00183-z.

35. Praniffa AC, Syahri A, Sandes F, et al. Pengujian Sistem Informasi Parkir Berbasis Web Pada UIN SUSKA RIAU Menggunakan White Box dan Black Box Testing. Jurnal Testing dan Implementasi Sistem Informasi 1.1 (2023): 1-16.

36. Maspupah A. Literature Review: Advantages and Disadvantages Of Black Box And White Box Testing Methods. Jurnal Techno Nusa Mandiri 21.2 (2024): 151-162. doi: 10.33480/techno.v21i2.5776

37. Komargodski I, Moni N, Eylon Y. White-box vs. black-box complexity of search problems: Ramsey and graph property testing. Journal of the ACM (JACM) 66.5 (2019): 1-28. doi: 10.1145/3341106

38. Wu H, Liu Y, Ni S, et al. Lossdetection: Real-time packet loss monitoring system for sampled traffic data. IEEE Transactions on Network and Service Management 20.1 (2022): 30-45. doi: 10.1109/TNSM.2022.3203389

39. Zhang B, Dou C, Yue D, et al. A packet loss-dependent event-triggered cyber-physical cooperative control strategy for islanded microgrid. IEEE Transactions on Cybernetics 51.1 (2019): 267-282. doi: 10.1109/TCYB.2019.2954181

Published
2025-02-13
How to Cite
Xu, X., Zhao, Q., Yang, T., & Liu, X. (2025). Sports information processing and physiological condition monitoring system based on multimedia computer. Molecular & Cellular Biomechanics, 22(3), 994. https://doi.org/10.62617/mcb994
Section
Article