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http://dx.doi.org/10.5103/KJSB.2021.31.2.87

Quantitative Golf Swing Analysis based on Kinematic Mining Approach  

Lee, Kyu Jong (Data Analytics Group, SAMSUNG SDS)
Ryou, Okhyun (Department of Business Administration, Korea Polytechnic University)
Kang, Jihoon (Department of Business Administration, Korea Polytechnic University)
Publication Information
Korean Journal of Applied Biomechanics / v.31, no.2, 2021 , pp. 87-94 More about this Journal
Abstract
Objective: Identification of meaningful patterns and trends in large volumes of unstructured data is an important task in various research areas. In the present study, we gathered golf swing image data and did quantitative analysis of swing image. Method: We collected golf swing images of 30 novice players and 30 professional players in this study. Results: We selected important features of swing posture and employed data mining algorithm to classify whether a player is an expert or a novice. Moreover, our proposed method could offer quantitative advices for golf beginners for correcting their swing. Conclusion: Finally, we found a possibility that our proposed method can be expanded to golf swing correction system
Keywords
Classification algorithms; Optimization; Feature selection; Golf swing image analysis;
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Times Cited By KSCI : 2  (Citation Analysis)
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