DocumentCode :
572978
Title :
A Kinect-based golf swing classification system using HMM and Neuro-Fuzzy
Author :
Lichao Zhang ; Jui-Chien Hsieh ; Jiangping Wang
Author_Institution :
Dept. Cognitive Sci., Xiamen Univ., Xiamen, China
fYear :
2012
fDate :
24-26 Aug. 2012
Firstpage :
1163
Lastpage :
1166
Abstract :
This study displays a method of scoring time-sequential postures of golf swing. Correct posture of golf swing is the most important skill for golfer training. In this paper, firstly, a game controller, Kinect, is used to capture the 3D skeleton coordination of a golfer while swing is performed. Secondly, the time-sequential posture of golf swing features has been extracted. Thirdly, a HMM-NF model is used for scoring, which combines ability of HMM model for temporal data modeling with that of Fuzzy Neural Network for fuzz rule modeling and fuzzy defined in a fuzzy (I am not sure on this!!!). Results have shown that the proposed methods can be implemented to identify and score the golf swing effectively with up to 80% accuracy rate.
Keywords :
feature extraction; fuzzy neural nets; hidden Markov models; image classification; learning (artificial intelligence); pose estimation; 3D skeleton coordination; HMM-NF model; fuzz rule modeling; fuzzy neural network; game controller; golf swing features time-sequential posture; golfer training; kinect-based golf swing classification system; neurofuzzy system; Artificial intelligence; Hidden Markov models; Fuzzy; HMM; Motion sequential classification; Neural Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science and Information Processing (CSIP), 2012 International Conference on
Conference_Location :
Xi´an, Shaanxi
Print_ISBN :
978-1-4673-1410-7
Type :
conf
DOI :
10.1109/CSIP.2012.6309065
Filename :
6309065
Link To Document :
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