Skip to main content
SpringerLink
Log in

Establishing a cognitive evaluation model for injury risk assessment in athletes using RBF neural networks

  • Neural Networks
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In recent years, there have been several sports-related injuries and even unexpected fatalities due to the rapid growth of China’s sports industry and increasing fitness issues. The prevalence of sports injuries is rapidly rising because of the intense competition among athletes and the increased physical load caused by players' long-term training. The difficulty in the sports’ world is the identification and evaluation of risk factors associated with athletes. To address these issues, this paper develops a cognitive evaluation model for risk assessment of injuries in sports using big data to understand and determine the likelihood of injuries. The model is used to prevent the issues faced by running athlete’s injury and thereby lowers the risk of overall risk. This paper also analyzes the injury factors while running by establishing an estimation model based on the radial basis function (RBF) neural network and explains how to compare RBF neural networks and risk level evaluation using certain criteria. The estimation model helps to identify and judge the risk factors of athletes. It designs an algorithm for Gaussian distribution, which predicts the output of the model. It discusses how the error may be calculated, which measures the neural network model's overall performance in forecasting sports’ injury outcomes using a particular dataset. Finally, it designs the RBF Neural Network Learning algorithm, which generates the leaned network parameters to evaluate the reliability of risk cognitive model for athletes based on big data. The Lagrange and Bayesian models are selected for comparison. The results indicate that this model has a higher evaluation effect and accuracy as compared to other models. For this purpose, the comparison is made using 25 athletes. The proposed model's evaluation time is the lowest in comparison to the existing approaches. After examining the impact of cognitive evaluation, it has been reflected that the proposed model can be utilized to assess the different injury types for different parts of athletes. The highest single injury degree is 38.76%, while the highest composite injury degree is 40.05%.

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
Fig. 11
Fig. 12

Similar content being viewed by others

Data and material availability

The corresponding author can be reached for a reasonable request for the datasets used and/or analyzed in the current work.

References

  • Ali M, Yin B, Kunar A, Sheikh AM et al. (2020) Reduction of Multiplications in Convolutional Neural Networks. In 2020 39th Chinese Control Conference (CCC) (pp. 7406–7411). IEEE. DOI: https://doi.org/10.23919/CCC50068.2020.9188843.

  • Aslam MS (2021) L2-L∞ control for delayed singular Markov switch system with nonlinear actuator faults. Int J Fuzzy Syst 23:2297–2308. https://doi.org/10.1007/s40815-021-01102-0

    Article  Google Scholar 

  • Aslam MS, Qaisar I (2023) Sensor networks with distributed event-triggered scheme for T-S fuzzy system with dissipativity analysis. Eur. J. Control 71:100800. https://doi.org/10.1016/j.ejcon.2023.100800

    Article  MathSciNet  MATH  Google Scholar 

  • Aslam MS, Li Q, Hou J (2021) Fault detection for asynchronous T-S fuzzy networked Markov jump systems with new event-triggered scheme. IET Control Theory Appl 15(11):1461–1473

    Article  MathSciNet  Google Scholar 

  • Aslam MS, Tiwari P, Pandey HM, Band SS (2023) Robust stability analysis for class of Takagi-Sugeno (T-S) fuzzy with stochastic process for sustainable hypersonic vehicles. Inf Sci 641:119044. https://doi.org/10.1016/j.ins.2023.119044

    Article  Google Scholar 

  • Beyer EB, Hale RF, Hellem AR, Mumbleau AM, Schilaty ND, Hewett TE (2020) Inter and intra-rater reliability of the drop vertical jump (DVJ) assessment. Int J Sports Phys Ther 15(5):770

    Article  Google Scholar 

  • Bilal H, Yin B, Kumar A, Ali M, Zhang J, Yao J (2023) Jerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approach. Soft Comput 27(7):4029–4039. https://doi.org/10.1007/s00500-023-07923-5

    Article  Google Scholar 

  • Chang W-D, Chou L-W, Chang N-J, Chen S (2020) Comparison of functional movement screen, star excursion balance test, and physical fitness in junior athletes with different sports injury risk. Biomed Res Int 2020:1–8. https://doi.org/10.1155/2020/8690540

    Article  Google Scholar 

  • Chun BJ, Furutani T, Oshiro R, Young C, Prentiss G, Murata N (2021) Concussion epidemiology in youth sports: sports study of a statewide high school sports program. Sports Health 13(1):18–24

    Article  Google Scholar 

  • Dongli W (2019) Research on middle school students’ physical exercise behavior based on injury risk cognitive model. Sports Sci Technol Lit Bull 27(004):145–147

    Google Scholar 

  • Faleide AGH, Magnussen LH, Strand T, Bogen BE, Moe-Nilssen R, Mo IF, Vervaat W, Inderhaug E (2021) The role of psychological readiness in return to sport assessment after anterior cruciate ligament reconstruction. Am J Sports Med 49(5):1236–1243

    Article  Google Scholar 

  • Hannah TC, Li AY, Spiera Z, Kuohn L, Dai J, McAuley F, Ali M, Durbin JR, Dreher N, Marayati NF, Gometz A (2021) Sex-related differences in the incidence, severity, and recovery of concussion in adolescent student-athletes between 2009 and 2019. Am J Sports Med 49(7):1929–1937

    Article  Google Scholar 

  • Hu D, Zhang L, Zhang W (2022) Analysis of methods to reduce jogging injuries from the perspective of risk cognition. Am J Health Behav 46(2):114–123

    Article  Google Scholar 

  • Kuenze C, Collins K, Pfeiffer KA, Lisee C (2022) Assessing physical activity after ACL injury: moving beyond return to sport. Sports Health 14(2):197–204

    Article  Google Scholar 

  • Kumar A, Shaikh AM, Li Y et al (2021) Pruning filters with L1-norm and capped L1-norm for CNN compression. Appl Intell 51:1152–1160. https://doi.org/10.1007/s10489-020-01894-y

    Article  Google Scholar 

  • Lee JH, Ko SJ, Lee JH, Kim MJ (2022) The Decision-making process of sports center consumer based on COVID-19 risk perception: applying an extended goal-directed behavior model. Korean J Sports Sci 33(1):96–110

    Article  Google Scholar 

  • Liu A, Zhai Y, Xu N, Nie W, Li W, Zhang Y (2022) Region-aware image captioning via interaction learning. IEEE Trans Circ Syst Video Technol 32(6):3685–3696. https://doi.org/10.1109/TCSVT.2021.3107035

    Article  Google Scholar 

  • Luedke LE, Geisthardt TW, Rauh MJ (2020) Y-balance test performance does not determine non-contact lower quadrant injury in collegiate american football players. Sports 8(3):27

    Article  Google Scholar 

  • Montalvo AM, Schneider DK, Webster KE, Yut L, Galloway MT, Heidt RS Jr, Kaeding CC, Kremcheck TE, Magnussen RA, Parikh SN, Stanfield DT (2019) Anterior cruciate ligament injury risk in sport: a systematic review and meta-analysis of injury incidence by sex and sport classification. J Athl Train 54(5):472–482

    Article  Google Scholar 

  • Parish MR (2020) On determining factors affecting injury and recovery in athletes. Health Sport Rehabil 6(3):26–33

    Article  Google Scholar 

  • Perera NKP, Åkerlund I, Hägglund M (2019) Motivation for sports participation, injury prevention expectations, injury risk perceptions and health problems in youth floorball players. Knee Surg Sports Traumatol Arthrosc 27(11):3722–3732

    Article  Google Scholar 

  • Shi Y, Huo XY (2017) The mapping knowledge analysis of sport risk domain research. China Sport Sci 37:76–86

    Google Scholar 

  • Shrivastava SR, Shrivastava PS (2020) COVID-19 and conduction of sports event: risk assessment and risk mitigation. Saudi J Sports Med 20(1):27

    Article  Google Scholar 

  • Tian H and Luo Y (2020) Research on sports risk identification and evaluation of college students based on data mining. In: International Symposium on Education and Social Sciences (pp. 180–186).

  • Vacca L, Rosso V, Gastaldi L (2020) Risk assessment in different judo techniques for children and adolescent athletes. Proc Inst Mech Eng [h] 234(7):686–696

    Article  Google Scholar 

  • Luyang W, Qiang Z, Baoqun Y, et al. (2019) Second-order convolutional network for crowd counting. In: Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980T.Doi: https://doi.org/10.1117/12.2540362.

  • Yao W, Guo Y, Wu Y and Guo J, (2017) Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties. In: 2017 36th Chinese Control Conference (CCC) (pp. 4192–4197). IEEE. DOI: https://doi.org/10.23919/ChiCC.2017.8028015.

  • Yin B, Aslam MS et al (2023) A practical study of active disturbance rejection control for rotary flexible joint robot manipulator. Soft Comput 27:4987–5001. https://doi.org/10.1007/s00500-023-08026-x

    Article  Google Scholar 

  • Yin B, Khan J, Wang L, Zhang J and Kumar A (2019) Real-time lane detection and tracking for advanced driver assistance systems. In 2019 Chinese Control Conference (CCC) (pp. 6772–6777). IEEE. DOI: https://doi.org/10.23919/ChiCC.2019.8866334.

Download references

Funding

This study was funded by the 2021 Science and Technology Activity Program for Returned Overseas Students in Sichuan Province is an excellent funding project, which uses Big data to analyze the smart teaching strategy of students' physical health (ZY20210615).

Author information

Authors and Affiliations

  1. College of Physical Education, Southwest University, Chongqing, 400715, China

    Sheng Chen & Liya Guo

  2. Faculty of Education, Southwest University, Chongqing, 400715, China

    Rui Xiao

  3. Chongqing No.11 Middle School, Chongqing, 400061, China

    Jingfa Ran

  4. Nanshan Qianhai School, Shenzhen, 518000, Guangdong, China

    Haidan Li

  5. Graduate School, Emilio Aguinaldo Educational, 1007, Manila, Philippines

    Lino C. Reynoso

Authors
  1. Sheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liya Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rui Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jingfa Ran
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haidan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lino C. Reynoso
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liya Guo.

Ethics declarations

Conflict of interest

We certify that the publishing of this material does not involve any conflicts of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, S., Guo, L., Xiao, R. et al. Establishing a cognitive evaluation model for injury risk assessment in athletes using RBF neural networks. Soft Comput 27, 12637–12652 (2023). https://doi.org/10.1007/s00500-023-08789-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-08789-3

Keywords

Search

Navigation