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

Kim, Yeon Ho; Cho, Seung Hyun; Jung, Hae Ryun; Lee, Ki Kwang

doi:10.5103/kjsb.2022.32.1.1

머신러닝 기반 골프 퍼팅 방향 예측 모델을 활용한 중요 변수 분석 방법론
Method of Analyzing Important Variables using Machine Learning-based Golf Putting Direction Prediction Model 원문보기

한국운동역학회지 = Korean journal of sport biomechanics, v.32 no.1, 2022년, pp.1 - 8

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)

Abstract ▼ AI-Helper

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).

주제어

표/그림 (14)

표 Table 1. Participants' information (Mean ± SD)
표 Table 2. Laboratory equipment and data measurement tools
그림 Figure 1. Description of the variables of the IMU sensor
그림 Figure 2. Putting data collection environment.
그림 Figure 3. Data preprocessing process
그림 Figure 4. Methodology for building machine learning-based models and analyzing important variables
그림 Figure 5. Important variable analysis methodology
표 Table 3. Specification of the putting dataset
표 Table 4. Experimental environment
그림 Figure 6. Comparison of training and testing accuracy of the all model
그림 Figure 7. Comparison of accuracy of models by all variables
표 Table 5. Comparison of training accuracy and test accuracy by prediction model
표 Table 6. Comparison of training and testing accuracy of Light GBM models by all variables
표 Table 7. Ranking of important variables in predicting putting direction

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상세보기
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