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