Abstract
Clean and jerk is one category of Olympic weightlifting. The barbell trajectory including much kinematic parameters such as displacement, velocity and acceleration which provide coach and athletes to read and obtain athletes’ performance. However, kinematic parameters in barbell trajectory is difficult to understand. Hence, in this paper, we propose a weightlifting Clean&Jerk performance evaluation model by utilizing neural network architecture. Considering barbell trajectory characteristics, the all kinematic parameters on trajectory are dependent and time-sequential, hence, long-short-term memory (LSTM) architecture is considered. We gather the domestic adult competitions from 2019–2021 in Taiwan and utilize video tracking scheme to obtain the barbell trajectory from Clean&Jerk competitions. From the results, our inference model archives 71% identify accuracy to indicate the performance of Clean&Jerk of the lifter. Our proposed model not only helps coaches and athletes evaluating their performance, it also shows neural network can assist sport science.
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
Sato, K., Sands, W.A., Stone, M.H.: The reliability of accelerometry to measure weightlifting performance. Sports Biomech. 11(4), 524–531 (2012)
Chiu, H., Liang, J.: BCH angles of young female weightlifters during snatch movement. In: ISBS-Conference Proceedings Archive (2010)
Nagao, H., Kubo, Y., Tsuno, T., Kurosaka, S., Muto, M.: A biomechanical comparison of successful and unsuccessful snatch attempts among elite male weightlifters. Sports 7(6), 151 (2019)
Fialho, G., Manhães, A., Teixeira, J.P.: Predicting sports results with artificial intelligence–a proposal framework for soccer games. Procedia Comput. Sci. 164, 131–136 (2019)
Oluebube, N.L., Chijioke, E.G., Oghenekevwe, E.H., Ifeanyichukwu, U.C.: English premier league scoreline analysis: a stochastic and game theory approach. Am. J. Theor. Appl. Stat. 10(3), 136 (2021)
Huang, M.L., Lin, Y.J.: Regression tree model for predicting game scores for the golden state warriors in the national basketball association. Symmetry 12(5), 835 (2020)
Chen, W.J., Jhou, M.J., Lee, T.S., Lu, C.J.: Hybrid basketball game outcome prediction model by integrating data mining methods for the national basketball association. Entropy 23(4), 477 (2021)
Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D 404, 132306 (2020)
Garhammer, J.O.H.N.: Power production by Olympic weightlifters. Med. Sci. Sports Exerc. 12(1), 54–60 (1980)
https://iwf.sport/wp-content/uploads/downloads/2020/01/IWF_TCRR_2020.pdf
Storey, A., Smith, H.K.: Unique aspects of competitive weightlifting: performance, training and physiology. Sports Med. 42, 769–790 (2012)
Kipp, K., Meinerz, C.: A biomechanical comparison of successful and unsuccessful power clean attempts. Sports Biomech. 16(2), 272–282 (2017)
Harbili, E., Alptekin, A.: Comparative kinematic analysis of the snatch lifts in elite male adolescent weightlifters. J. Sports Sci. Med. 13(2), 417 (2014)
Al-Khleifat, A.I., Al-Kilani, M., Kilani, H.A.: Biomechanics of the clean and jerk in weightlifting national Jordanian team (2019)
Hsu, C.-T., Ho, W.-H., Chen, J.-S.: High efficient weightlifting barbell tracking algorithm based on diamond search strategy. In: Arkusz, K., Będziński, R., Klekiel, T., Piszczatowski, S. (eds.) Biomechanics in Medicine and Biology, pp. 252–262. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-97286-2_23
Garhammer, J.: Weight lifting and training. Biomech. Sport, 169–211 (2020)
Grabe, S.A., Widule, C.J.: Comparative biomechanics of the jerk in Olympic weightlifting. Res. Q. Exerc. Sport 59(1), 1–8 (1988)
Hadi, G., Akkus, H., Harbili, E.: Three-dimensional kinematic analysis of the snatch technique for lifting different barbell weights. J. Strength Conditioning Res. 26(6), 1568–1576 (2012)
Acknowledgements
This research is supported by National Science and Technology Council, Taiwan, R.O.C. The project number is NSTC 112-2425-H-845-002.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, JY., Ban, YR., Hsu, CT., Ho, WH., Chung, PH. (2023). A Weightlifting Clean and Jerk Team Formation Model by Considering Barbell Trajectory and LSTM Neural Network. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2023. Lecture Notes in Computer Science(), vol 14051. Springer, Cham. https://doi.org/10.1007/978-3-031-35894-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-031-35894-4_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35893-7
Online ISBN: 978-3-031-35894-4
eBook Packages: Computer ScienceComputer Science (R0)