Abstract
A golf swing requires full-body coordination and much practice to perform the complex motion precisely and consistently. The force from the golfer’s full-body movement on the club and the trajectory of the swing are the main determinants of swing quality. In this research, we introduce a unique motion analysis method to evaluate the quality of golf swing. The primary goal is to evaluate how close the user’s swing is to a reference ideal swing. We use 17 skeleton points to evaluate the resemblance and report a score ranging from 0 to 10. This evaluation result can be used as real-time feedback to improve player performance. Using this real-time feedback system repeatedly, the player will be able to train their muscle memory to improve their swing consistency. We created our dataset from a professional golf instructor including good and bad swings. Our result demonstrates that such a machine learning-based approach is feasible and has great potential to be adopted as a low-cost but efficient tool to improve swing quality and consistency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
How amateur golfers deliver energy to the driver, Vol. 8, no. 1, 30 April 2020. https://www.golfsciencejournal.org/article/12640-how-amateur-golfers-deliver-energy-to-the-driver
Golf industry facts - national golf foundation. https://www.ngf.org/golf-industry-research/
McNally, W., Vats, K., Pinto, T., Dulhanty, C., McPhee, J., Wong, A.: GolfDB: a video database for golf swing sequencing. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, pp. 2553–2562 (2019). https://doi.org/10.1109/CVPRW.2019.00311
Golf 2020: 2016 golf economy report. Accessed 11 Feb 2019. https://golf2020.com/research/
Kamel, A., Sheng, B., Li, P., Kim, J., Feng, D.D.: Efficient body motion quantification and similarity evaluation using 3-d joints skeleton coordinates. IEEE Trans. Syst. Man Cybern. Syst., 1–15 (2019). https://ieeexplore-ieee-org.erl.lib.byu.edu/document/8727745
Fang, H.S., Xie, S., Tai, Y., Lu, C.: RMPE: Regional multi-person PoseEstimation. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, pp. 2353–2362 (2017). https://ieeexplore.ieee.org/document/8237518. https://doi.org/10.1109/ICCV.2017.256
Cao, Z., Martinez, G.H., Simon, T., Wei, S., Sheikh, Y.A.: Open-pose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. (2017). https://doi.org/10.1109/TPAMI.2019.2929257.0. https://ieeexplore-ieee-org.erl.lib.byu.edu/document/8765346
Toshev, A., Szegedy, C.: Deeppose: human pose estimation via deepneural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014). https://ieeexplore-ieee-org.erl.lib.byu.edu/document/6909610
Ouyang, W., Chu, X., Wang, X.: Multi-source deep learning for humanpose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014. https://ieeexplore-ieee-org.erl.lib.byu.edu/document/6909696
Jain, A., Tompson, J., Andriluka, M., Taylor, G.W., Bregler, C.: Learning human pose estimation features with convolutional networks (2013). https://arxiv.org/abs/1312.7302
Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: Joint training of aconvolutional network and a graphical model for human pose estimation. In: Conference on Neural Information Processing Systems (NIPS), pp. 1799–1807 (2014). https://arxiv.org/abs/1406.2984
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for ImageRecognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90. https://arxiv.org/abs/1512.03385
Kim, Y.J., Kim, K.D., Kim, S.H., Lee, S., Lee, H.S.: Golf swing analysis system with a dual band and motion analysis algorithm. IEEE Trans. Consum. Electron. 63(3), 309–317 (2017)
Kim, M., Park, S.: Golf swing segmentation from a single IMU using machine learning. Sensors 20, 4466 (2020)
Ko, H., Marreiros, G.: Smart media and application. Concurr. Comput. Pract. Exp. (2019). https://doi.org/10.1002/cpe.5491
Severin, A.C., Barnes, S.G., Tackett, S.A., Barnes, C.L., Mannen, E.M.: The required number of trials for biomechanical analysis of a golf swing. Sports Biomech. (2019). https://doi.org/10.1080/14763141.2018.1554085
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, J.J., Newman, J., Lee, DJ. (2020). Body Motion Analysis for Golf Swing Evaluation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12509. Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-64556-4_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64555-7
Online ISBN: 978-3-030-64556-4
eBook Packages: Computer ScienceComputer Science (R0)