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	A Kinect based Golf Swing Score and Grade System using GMM and SVM
	Zhang, Lich ; Hsieh, Jui-Chien ; Ting, Tsu-Te ; Huang, Yi-Chi ; Ho, Ya-Chih ; Ku, Lin-Kai ; Zhang LC(张立超)
	2012
关键词	Musculoskeletal system Personnel training Signal processing Vector quantization
英文摘要	Conference Name:2012 5th International Congress on Image and Signal Processing, CISP 2012. Conference Address: Chongqing, China. Time:October 16, 2012 - October 18, 2012.; This study displays a method to recognize and segment the time-sequential postures of golf swing. Golf is a kind of sports that has been adopted by most universities for physical education in Taiwan. The correct posture of golf swing is the most important skill while training a golfer. The training of golf swing is tedious, ineffective, and time-consuming in the traditional university course, because the golf instructor has to correct the swing postures of every student one by one in a class with 50 students. It's crucial to develop a system that can effectively recognize the steps of golf swing and facilitate self-learning of correct golf swing. First, a game controller, Kinect, is used to capture the 3D skeleton coordination of a golfer while performing swing. Second, the time-sequential postures of golf swing represented by Kinect's skeleton coordination are then transformed into a symbol sequence through vector quantization. Third, This system use serial correlation GMM model and GMM-KL divergence kernel to score and recognize grade of Golf Swing. Evaluate result show our method achieve a good accuracy of score and recognize grade and significantly improves than the Standard GMM and other SVM kernel. 漏 2012 IEEE.
语种	英语
出处	http://dx.doi.org/10.1109/CISP.2012.6469827
出版者	IEEE Computer Society
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86818]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Zhang, Lich,Hsieh, Jui-Chien,Ting, Tsu-Te,et al. A Kinect based Golf Swing Score and Grade System using GMM and SVM. 2012-01-01.

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