Korean Journal of Applied Biomechanics (한국운동역학회지)

Korean Society of Applied Biomechanics (한국운동역학회)
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Method of Analyzing Important Variables using Machine Learning-based Golf Putting Direction Prediction Model

머신러닝 기반 골프 퍼팅 방향 예측 모델을 활용한 중요 변수 분석 방법론

  • Kim, Yeon Ho (Department of Convergence Kinesiology, Kookmin University) ;
  • Cho, Seung Hyun (Biomechanics & Sports Engineering Lab., Kookmin University) ;
  • Jung, Hae Ryun (Biomechanics & Sports Engineering Lab., Kookmin University) ;
  • Lee, Ki Kwang (College of Physical Education, Kookmin University)
  • Received : 2022.01.21
  • Accepted : 2022.03.29
  • Published : 2022.03.31
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Abstract

Objective: This study proposes a methodology to analyze important variables that have a significant impact on the putting direction prediction using a machine learning-based putting direction prediction model trained with IMU sensor data. Method: Putting data were collected using an IMU sensor measuring 12 variables from 6 adult males in their 20s at K University who had no golf experience. The data was preprocessed so that it could be applied to machine learning, and a model was built using five machine learning algorithms. Finally, by comparing the performance of the built models, the model with the highest performance was selected as the proposed model, and then 12 variables of the IMU sensor were applied one by one to analyze important variables affecting the learning performance. Results: As a result of comparing the performance of five machine learning algorithms (K-NN, Naive Bayes, Decision Tree, Random Forest, and Light GBM), the prediction accuracy of the Light GBM-based prediction model was higher than that of other algorithms. Using the Light GBM algorithm, which had excellent performance, an experiment was performed to rank the importance of variables that affect the direction prediction of the model. Conclusion: Among the five machine learning algorithms, the algorithm that best predicts the putting direction was the Light GBM algorithm. When the model predicted the putting direction, the variable that had the greatest influence was the left-right inclination (Roll).

Keywords

Acknowledgement

This work was supported by the Sports Promoting Fund of Korea Sports Promotion Foundation (KSPO) from the Ministry of Culture, Sports and Tourism.

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