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Frontiers in Sport Research, 2022, 4(3); doi: 10.25236/FSR.2022.040301.

Research on Apparent Strain Measurement of Ankle Joint in Taekwondo Sports Using Data Mining Algorithm

Author(s)

Xiaodong Wang

Corresponding Author:
Xiaodong Wang
Affiliation(s)

School of Physical Education, Shaoguan University, Shaoguan, 512000, China

Abstract

As a sport, Taekwondo has always attracted everyone’s attention and love, but it is a very common phenomenon to have ankle injuries during sports. Therefore, the detection of ankle injuries has become a concern theme of scholars. It is of great significance to realize the ankle apparent strain measurement quickly and in colleges and universities. In this paper, a data mining algorithm-based approach to measuring the apparent strain of the ankle joint in Taekwondo sports is proposed. This paper first analyzes and integrates the research results of scholars, and then expounds from the two aspects of data mining and ankle apparent strain measurement. The method is introduced in detail, and the principle of apparent strain measurement is explained by the formula. The feasibility and robustness of the research scheme are then demonstrated through specific experimental data. The data showed that there was a difference between the boy group and the girl group in the time of jumping, kicking, and reaching the ground.

Keywords

data mining algorithms, Taekwondo sports, apparent strain measurement

Cite This Paper

Xiaodong Wang. Research on Apparent Strain Measurement of Ankle Joint in Taekwondo Sports Using Data Mining Algorithm. Frontiers in Sport Research (2022) Vol. 4, Issue 3: 1-7. https://doi.org/10.25236/FSR.2022.040301.

References

[1] Bonidia, R P, Brancher, J D andBusto, R M. Data Mining in Sports: A Systematic Review [J]. IEEE Latin America Transactions, 2018, 16(1):232-239.

[2] Meehan C L, Talebi M. A method for correcting field strain measurements to account for temperature effects [J]. Geotextiles and Geomembranes, 2017, 45(4):250-260.

[3] Zubaidi S L, Abdulkareem I H, Hashim K S, et al. Hybridised Artificial Neural Network Model with Slime Mould Algorithm: A Novel Methodology for Prediction of Urban Stochastic Water Demand [J]. Water, 2020, 12(10):1-18.

[4] Zcan E, Danan T, Yumuak R, et al. An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants [J]. Eksploatacja i Niezawodnosc - Maintenance and Reliability, 2020, 22(3):400-418.

[5] Raad N G, Isfahani N M. Ranking of building maintenance contractors using multi-criteria decision making methods and an artificial neural network model [J]. International Journal of Data and Network Science, 2020, 4(2):245-254.

[6] Yildiz O. Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes [J]. Teknik Dergi, 2020, 31(4):1-20.

[7] Yu B, Kim D, Cho H, et al. A Nonlinear Autoregressive With Exogenous Inputs Artificial Neural Network Model for Building Thermal Load Prediction [J]. Journal of Energy Resources Technology, 2020, 142(5):050902.1-050902.9.

[8] Kfer P S ,  Rocha N ,  Diaz L R , et al. Artificial neural networks model based on remote sensing to retrieve evapotranspiration over the Brazilian Pampa[J]. Journal of Applied Remote Sensing, 2020, 14(3):38504-38501.