Detecting flaws in golf swing using common movements of professional players

• Published in: Machine vision and applications Vol. 27; no. 1; pp. 13 - 22
• Main Authors: Sugimura, Daisuke, Tsutsui, Hiroharu, Hamamoto, Takayuki
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-01-2016; Springer Nature B.V
• Subjects: Body size; Chief executive officers; Communications Engineering; Computer Science; Defects; Dependence; Entrepreneurs; Estimators; Flaw detection; Flexibility; Gaussian process; Image Processing and Computer Vision; Movements; Networks; Original Paper; Pattern Recognition; Players; Posture; Professional golf; Professionals; Swing; Vision systems; Sport video analysis; Flaw detection; Golf swing instruction; Professional player; Common movements
• ISSN: 0932-8092; 1432-1769
• DOI: 10.1007/s00138-015-0725-7

We propose a golf swing instruction system for detecting important flaws to facilitate the improvement of a user’s golf swing. Golf players generally differ greatly in terms of their body size and flexibility; these individual differences make it difficult to identify the underlying characteristics of a good swing. In this study, we exploit common movements made by professional players to establish golf swing instruction for diverse users. The common movements of professionals are likely to be similar without dependence on their individual differences because being important for performing professional golf swing. This suggests that the common movements of professionals are helpful components for achieving appropriate golf swing instructions for diverse users. We construct an ideal posture estimator by aggregating the movements of professionals. In our ideal posture estimator, we use a Gaussian process regression to infer the parts of the golf swing that characterize the common or individual movements. Using the estimation results inferred by our ideal posture estimator, we estimate the important joints to improve the golf swing of each user. Our experiments demonstrate that the use of the common movements made by professionals significantly improves the detection of flaws in the swing of individual users.