Biomechanical Characteristics of Taekwondo Athletes' Horizontal Kick Technique Based on Machine Learning Algorithm_Vol. 1 No. 3 (JMPE 2024)_Journal of Medicine and Physical Education (ISSN: 3005-5806)

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Biomechanical Characteristics of Taekwondo Athletes' Horizontal Kick Technique Based on Machine Learning Algorithm

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DOI: https://doi.org/10.62517/jmpe.202418318

Author(s)

Manman Shi

Affiliation(s)

Leshan Normal University, Leshan, Sichuan, China

Abstract

Taekwondo competitors frequently use the horizontal kick, because it is quick and simple to demonstrate, and it helps them place higher in the competition. This paper investigates artificial neural networks in machine learning algorithms to extract reliable information from the complex competition training process. This essay examines the biomechanical aspects of taekwondo practitioners' horizontal kicks and offers insightful conclusions. A general introduction to machine learning algorithms is given in this article. It combines machine learning algorithms with the biomechanical characteristics analysis of taekwondo athletes' horizontal kick technical movements to analyze the biomechanical aspects of the sport's athletes' horizontal kick technical movements. According to this paper's experimental methodology, the accuracy recognition rates of using neural networks to distinguish between the three stages of taekwondo horizontal kick technical movements are all above 75%, which is within the acceptable range. In contrast to the experimental procedure and measurement data using traditional biomechanical characteristics analysis, the experimental results in this paper demonstrate that the biomechanical characteristics of taekwondo athletes' horizontal kick technical movements based on machine learning algorithms are more abundant.

Keywords

Horizontal Kick Technique, Biomechanical Features, Machine Learning, Artificial Neural Network (ANN)

References

[1]Alp M , Citchkas, Fatih, Kurt C . Acute effects of static and dynamic stretching exercises on lower extremity isokinetic strength in taekwondo athletes[J]. Isokinetics & Exercise Science, 2018, 26(4):307-311. [2]Simunic, Bostjan, Kokol,Peter, Pisot, Rado, et al. Biomechanical characteristics of skeletal muscles and associations between running speed and contraction time in 8-to 13-year-old children[J]. The Journal of international medical research, 2017, 45(1):231-245. [3]Lampen N , Kim D , Fang X , Xuanang K, Tianshu D, Hannah H.et al. Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning[J]. International Journal of Computer Assisted Radiology and Surgery, 2022, 17(5):945-952. [4] Iryna R . PSYCHOLOGICAL TRAINING SUPPORT FOR TAEKWONDO ATHLETES IN FOUR-YEAR OLYMPIC CYCLES[J]. Sport Science and Human Health, 2020, 4(2):114-129. [5]Busco K, Nikolaidis P T . Biomechanical characteristics of Taekwondo athletes: Kicks and punches vs. Laboratory tests[J]. Biomedical Human Kinetics, 2018, 10(1):81-88. [6]Aktan S . Application of machine learning algorithms for business failure prediction[J]. Investment Management & Financial Innovations, 2017, 8(2):52-65. [7]Yedukondalay RV. Nagarajan G. Ramakrishnan S. Electrodermal Activity Based Emotion Recognition using Time-Frequency Methods and Machine Learning Algorithms[J]. Current Directions in Biomedical Engineering, 2021, 7(2):863-866. [8] Whyte A , Ferentinos K P , Petropoulos G P . A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms[J]. Environmental Modelling and Software, 2018, 104(JUN.):40-54. [9]Alanis, Alma Y . Electricity Prices Forecasting using Artificial Neural Networks[J]. IEEE Latin America Transactions, 2018, 16(1):105-111. [10]Isik E , Inalli M . Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey[J]. Energy, 2018, 154(JUL.1):7-16. [11]Koh J O . Incidence Rates of Head Kicks and Concussions among Olympic Style-Taekwondo Athletes[J]. Journal of Korean Association of Physical Education and Sport for Girls and Women, 2018, 32(4):171-187. [12]Jeong-il, Choi, Myung-Kyu, Jung, Shin-Ja, Lim. Structural Relationships among Sports Confidence, Self-Leadership and Sports Satisfaction of Taekwondo Poomsae Athletes[J]. Journal of Korean Association of Physical Education and Sport for Girls and Women, 2017, 31(1):37-51. [13]Kim J S , Chun G S , Lee S J . The Effect on the Change of Psychological State of Taekwondo Athletes Who Are Watching Simulating Game and Wearing Mouth Guard[J]. The Korean Journal of Oral and Maxillofacial Pathology, 2017, 41(3):131-139. [14]Wahid M F , Tafreshi R , Al-Sowaidi M , Langari R. Subject-Independent Hand Gesture Recognition using Normalization and Machine Learning Algorithms[J]. Journal of Computational Science, 2018, 27(JUL.):69-76. [15]Khosravi A , Machado L , Nunes R O . Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil[J]. Applied Energy, 2018, 224(AUG.15):550-566. [16]Raghu S , Sriraam N . Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms[J]. Expert Systems with Applications, 2018, 113(DEC.):18-32. [17]Kotkar E . An automatic pesticide sprayer to detect the crop disease using machine learning algorithms and spraying pesticide on affected crops[J]. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, 12(1S):65-72. [18]Saglam C , Cetin N . Prediction of Pistachio (Pistacia vera L.) Mass Based on Shape and Size Attributes by Using Machine Learning Algorithms[J]. Food Analytical Methods, 2022, 15(3):739-750. [19]Meliho M , Khattabi A , Asinyo J . Spatial modeling offlood susceptibility using machine learning algorithms[J]. Arabian Journal of Geosciences, 2021, 14(21):1-18. [20]Saved M . Biometric Gait Recognition Based on Machine Learning Algorithms[J]. Journal of Computer Science, 2018, 14(7):1064-1073. [21]Tsao H Y , Chan P Y , Su C Y . Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms[J]. BMC Bioinformatics, 2018, 19(9):111-121. [22]Jiajia Liu, Yudong Ye, Chenglong Shen, Yuming Wang, Robert, Transylvanian. A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA[J]. The Astrophysical Journal, 2018, 855(2):109-109. [23]Sharmila S L , C D haruman, Venkatesan P . Disease Classification Using Machine Learning Algorithms-A Comparative Study[J]. International Journal of Pure and Applied Mathematics, 2017, 114(6):1-10. [24]Al-Moqri T , Xiao H , Namahoro J P , Alfalahi EN, Alwesabi I. Exploiting Machine Learning Algorithms for Predicting Crash Injury Severity in Yemen: Hospital Case Study[J]. Applied and Computational Mathematics, 2020, 9(5):155-164.