A sensor-based golfer-swing signature recognition method using linear support vector machine

Golf performance varies from person to person because of the differences in physical features of golfer’s body and skill level. Recognizing golf swings of an individual golf player is essential to help improving the golf skill level. This can be done through feedback information provided by a specialized equipment or by a personal coach. Based on the classification of the golfer-swing shapes, this work analyses golf-swings. A sensorbased golfer-swing signature-recognition method is performed by using linear support vector machine (LSVM). Golf-swing signals are acquired by a strain-gage sensor fitted to the golf club that measures the club bend. To classify each golfer-swing multi-class classifier is built by combining binary LSVM models with an errorcorrecting-output-codes multi-class strategy. The experiment results of the training accuracy, testing accuracy and training time are compared with the results of other models including decision-tree algorithms, discriminantanalysis algorithms, other support vector machine algorithms, k-nearest neighbor (KNN) classifiers, and ensemble classifiers. A comparison shows that by using the strain-gage sensor and multi-class LSVM model, the golfer-swing signature is recognized accurately and effectively.