CNN and bi-LSTM based 3D golf swing analysis by frontal swing sequence images

In this paper, the method to overcome the limitations of the existing three-dimensional golf swing analysis system by using deep learning technology, and analyze the three-dimensional quantitative information through sequence images acquired with a single camera is studied. In this paper, CNN was used to extract the appropriate features from the image of the golf frontal swing sequence, and a regression model based on Bi-LSTM was used to predict the correct information in each sequence. This classifies the major swing section, and analyzes the quantitative status of the twisting angles of the upper body, head, shoulder and pelvis for body-sway, head-up and X-factor analysis. For the experiment, in this paper, a total of 520 times swing data were obtained using no. 1 wood club and no. 7 iron club from five subjects. In the major swing section classification experiment, each swing section was classified with an average accuracy of about 95.44%. Quantitative analysis results from each analysis model showed that the upper body motion prediction RMSE averaged 4.23 degrees, the head motion prediction RMSE averaged 5.18 degrees, and the shoulder and pelvis twisting angle prediction RMSE averaged 3.86 degrees. As a result, it was confirmed that a three-dimensional quantitative analysis based on sequence images is possible.