Neural networks for golf swing analysis from weight-shift

Author

Yoon, Ho Sub ; Bae, Chang Seok ; Min, Byung Woo

Author_Institution

Syst. Eng. Res. Inst., Korea Inst. of Sci. & Technol., Taejon, South Korea

Volume

5

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

3083

Abstract

Weight-shift of human body motion means the continuous change of weights loaded on the left and right foot respectively. In this study a neural network method is employed to identify the golf swing from a continuous weight-shift waveform. We defined eight input features which can classify various shapes of swing pattern. The adopted network is a three-layered error backpropagation model consisting of eight input, ten hidden and two output nodes. According to experimental results, the identifying success rate is 97.75% using 10 hidden nodes and 5000 epochs

Keywords

backpropagation; neural nets; pattern classification; sport; golf swing analysis; human body motion; neural network; sport; swing pattern classification; three-layered error backpropagation model; weight-shift; Artificial intelligence; Feature extraction; Hardware; Humans; Image motion analysis; Motion analysis; Neural networks; Pattern analysis; Shape; Signal analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374725

Filename

374725