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Analysis of lower limb high-risk injury factors of patellar tendon enthesis of basketball players based on deep learning and big data

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Abstract

The research purposes were to explore the high-risk injury factors of basketball players’ lower limb patellar tendon enthesis based on medical big data and accurately recognize players’ lower limb injuries. Middle school students in Nanchang, the capital of Jiangxi Province, were included as the research samples. Innovatively, deep learning and artificial intelligence were applied for statistics and analysis of the collected data. The training of included samples was tracked to collect kinetic and biological data and then to analyze the high-risk injury factors of lower limbs. The analysis of basic information of the participants indicated no significant differences between the two groups. Deep learning algorithm analysis suggests that the accuracy of high-risk group and non-high-risk group is 66.82% and 66.20%, respectively. The analysis of the mechanical characteristics of patellar tendon ends of participants indicated that when the maximum flexion angles of the knee joints of the high-risk group were significantly greater than that of the non-high-risk lower limb group, there were statistically significant differences (P < 0.01). The analysis of the dynamic characteristics of the lower limbs revealed that in different action phases, the impulses of the high-risk lower limbs in the stretching and extension phase of drop landing and jump were significantly greater than that of the non-high-risk lower limbs group. In addition, in the buffer phase and the stretching and extension phase of stop jump, the impulses of lower limbs before the injury were smaller than that after the injury in the same action phases, and the differences between the impulses of stretching and extension were statistically significant (P < 0.01). The research results have processed the data through the deep learning algorithm and the parallel computing to find a joint angle that is most appropriate to make the concentration capacities of muscles reach the maximum value during the movements, thereby the damages and injuries of the body would be the lowest. The results provide a new idea for the selection of basketball court material coefficient.

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Acknowledgements

This work was supported by Jiangxi Education Bureau (Title: Research on the Cultivation of Core Literacy in P.E. of Senior High School Students from Moral Education Perspective) and (Title: The Influencing Factors and Path Selection of the Inheritance of Red Sports Culture in the Central Soviet Area-Based on Grounded Theory).

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  1. School of Physical Education, East China University of Technology, Nanchang, 330013, China

    Hongyu Wu & Lili Wang

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  1. Hongyu Wu
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  2. Lili Wang
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Correspondence to Hongyu Wu.

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Wu, H., Wang, L. Analysis of lower limb high-risk injury factors of patellar tendon enthesis of basketball players based on deep learning and big data. J Supercomput 78, 4467–4486 (2022). https://doi.org/10.1007/s11227-021-04029-3

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