Skip to main content
SpringerLink
Log in

Evaluation of Physical Fitness of Pupils Based on Bayesian and Fuzzy Recognition Coupling Method

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The paper proposes the coupling method based on Bayesian formula and fuzzy recognition by improving the Bayesian classification algorithm. The combination weighting method and relative membership degree are introduced to clarify the weight of evaluation index, and the maximum likelihood classification criterion is used to determine the grade of individual primary school students' physical health evaluation index. This method is applied to physical-health evaluation of six students in Shuangfeng primary school in Jiujiang City. The results show that some student VI is worse in physical health evaluation, the other students are medium or above, which is consistent with the actual sampling results. The method achieves 67% comprehensive accuracy and has certain credibility, which realizes the correct judgment on physical condition of primary school students in probability, and take the initiative to warn the primary school students who have a higher probability of physical hidden-danger. In this case, group physical judgment and personalized effective intervention are carried out on students to promote their healthy development and the overall physical level of primary school students.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Availability of data and materials

All data generated or analyzed during this study are included in this published paper.

References

  1. Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques. (2nd ed.). San Francisco: Morgan Kaufmann Publishers.

    MATH  Google Scholar 

  2. Domingos, P., & Pazzani, M. (1996). Beyond independence: Conditions for the optimality of the simple Bayesian classifier. In Proceedings of the 13th international conference on machine learning, Bari (pp. 105–112).

  3. Zhang, Y., & Gong, L. (2004). Principles and techniques of data mining. . Beijing: Publishing House of Electronics Industry.

    Google Scholar 

  4. Kurfal, M. (2018). A Trie-structured Bayeian Model for unsupervised morphological segmentation. . Berlin: Springer. https://doi.org/10.1007/978-3-319-77113-7_7.

    Book  Google Scholar 

  5. Uchino, B. N. (2018). social support and physical health: models, mechanisms, and opportunities. . New York : Springer. https://doi.org/10.1007/978-0-387-93826-4_12.

    Book  Google Scholar 

  6. Zhongjian, C., & Jianguo, Y. (2009). The weight coefficient and correlation analysis of college students’ physique health index. Journal of Shanghai Physical Education Institute, 2, 74–78.

    Google Scholar 

  7. Guojun, W., Hui, W., & Yi, X. (2012). Establishment of membership function of physique health evaluation based on fuzzy evaluation—Concurrently discussing with two papers. Journal of Tianjin Institute of Physical Education, 6, 519–523.

    Google Scholar 

  8. Zongguo, Z. (2009). Analysis of subjective and objective factors influencing the test results of National Student Health Standards. Sports Science, 9, 86–91.

    Google Scholar 

  9. Leibenger, D., Millers, F., Petrlic, A., et al. (2016). Privacy challenges in the quantified self movement—An EU perspective. Proceedings on Privacy Enhancing Technologies, 4, 315–334.

    Article  Google Scholar 

  10. Weihan, W., Xuewen, T., & Yigui, D. (2012). Integrated traffic classification method based on Bayesian learning. Computer Engineering, 38(16), 164–166.

    Google Scholar 

  11. Wu, J., Cai, Z., & Zhu, X. (2013). Self-adaptive probability estimation for naive Bayes classification. In IEEE international joint conference on neural networks (pp. 1–8).

  12. Zhang, H., & Sheng, S. (2004). Learning weighted naive Bayes with accurate ranking. In The 4th IEEE international conference on data mining (pp. 567–570).

Download references

Funding

The 13th Five-Year Plan of Jiangxi Education Science, the subject of "Research on Compensation Mechanism of Physical Health Education in Rural Primary Schools of Ganjiang New District" in 2019 (No. 19YB232).

Author information

Authors and Affiliations

  1. Physical College, Jiujiang University, Jiujiang, 332005, China

    Peijun Gao & Kaiyan Han

  2. Jiujiang Maternal and Child Health Hospital, Jiujiang, 332005, China

    Dan Zhao

  3. Hunan First Normal University, Changsha, 410205, China

    Jinyun Yang

Authors
  1. Peijun Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinyun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kaiyan Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinyun Yang.

Ethics declarations

Conflict of interest

The authors declare there is no conflicts of interest regarding the publication of this paper.

Consent for publication

The authors of this paper has cooperated with Shuangfeng Primary school to carry out the research on students' physical health for six years, and followed up six students: Chen Ping, Xu Lifang, Liu Yan, Xiong Bi Han Xiaoyu, Li Jiao. This article involves their physical health data and portrait rights, which are allowed to be used in the scientific research and publication of this article. Without the consent of the primary school and six students themselves, they shall not be used for profits or other purposes.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, P., Zhao, D., Yang, J. et al. Evaluation of Physical Fitness of Pupils Based on Bayesian and Fuzzy Recognition Coupling Method. Wireless Pers Commun 119, 3037–3051 (2021). https://doi.org/10.1007/s11277-021-08385-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08385-4

Keywords

Search

Navigation