Chinese Journal of Tissue Engineering Research ›› 2023, Vol. 27 ›› Issue (5): 738-744.doi: 10.12307/2023.096

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Screening key technical indicators for speed climbing based on machine learning

You Guopeng1, 2, Wang Jianqing3, Liu Fei1, Yuan Qiang1, Liu Haoyan1, Wu Ying1   

  1. 1School of Physical Education and Training, 3School of Leisure Sport, Shanghai University of Sport, Shanghai 200438, China; 2Department of Physical Education, Changzhi Medical College, Changzhi 046000, Shanxi Province, China
  • Received:2022-01-24 Accepted:2022-04-23 Online:2023-02-18 Published:2022-07-23
  • Contact: Wu Ying, PhD, Professor, Doctoral supervisor, School of Physical Education and Training, Shanghai University of Sport, Shanghai 200438, China
  • About author:You Guopeng, PhD candidate, Associate professor, School of Physical Education and Training, Shanghai University of Sport, Shanghai 200438, China; Department of Physical Education, Changzhi Medical College, Changzhi 046000, Shanxi Province, China

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Abstract: BACKGROUND: Selecting key technical indicators to guide special training or competition is the key to improving competition performance. There are obvious differences in the techniques of male and female athletes and the key technical indicators of athletes of different sexes have not yet been known.
OBJECTIVE: To select key technical indicators that affect the competition performance of elite speed climbing athletes, thereby providing direction and theoretical support for the scientific training of speed climbing.
METHODS: The videos of speed climbing finals in the 2017-2021 World Championships, World Cup and China Climbing League were selected to obtain the competition samples of the top four male and female athletes (109 men and 117 women). Reaction time, contact time and segmental speed were collected and then random forest and XGBoost models of male and female speed climbing athletes were established using two-dimensional kinematic analysis. Accordingly, the influence of each technical indicator on the competition performance was calculated.
RESULTS AND CONCLUSION: For male athletes, the root-mean-square error values of random forest and XGBoost models were 0.224 and 0.265, respectively, and r2 was 0.765 and 0.686, respectively. The key technical indicators were total contact time of the left hand, motion frequency of the right hand, total contact time of the right hand, speed of holds 3-6, motion frequency of the left foot, total contact time of the right foot, speed of holds 11-13, speed of holds 13-18, speed of holds 8-10, total contact time of the left foot, number of total holds of the left hand and motion frequency of the right foot. For female athletes, the root-mean-square error indicator of random forest and XGBoost models were 0.066 and 0.055, respectively, and r2 were 0.846 and 0.887, respectively. The key technical indicators were total contact time of the left hand, total contact time of the right hand, motion frequency of the right hand, motion frequency of the left foot, total contact time of the left foot, speed of holds 8-10, total contact time of the right foot, and speed of holds 10-11 and 13-18. To conclude, the technical indicators of the upper limbs (contact time sum and action frequency) on speed rock climbing competition are more influential than those of the lower limbs. The number of holds has no high influence on the competition results. The key segments for winning in male and female elite athletes are holds 3-6 and 8-11 respectively. Athletes should focus on optimizing the upper limb contact time of the key segments for winning and then optimize the lower limb contact time, so as to improve the competition performance.

Key words: two-dimensional kinematics analysis, speed climbing, motion technique, random forest, XGBoost, scientific training

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