Multi-Objective Biomechanical Optimization of Breaststroke Swimming Using NSGA-II
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Advancements in computational modeling and optimization algorithms have opened new possibilities for analyzing and improving sports biomechanics. This study presents a multi-objective optimization framework based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize breaststroke swimming techniques. The framework integrates a biomechanical model that combines hydrodynamic forces, joint kinematics, and energy expenditure to address three conflicting objectives: maximizing swimming velocity, improving energy efficiency, and minimizing joint load. Experimental validation conducted with professional swimmers demonstrated that the optimized stroke techniques achieved up to a 20% reduction in peak joint loads at the shoulder and knee, significantly reducing the risk of overuse injuries. Additionally, energy consumption per stroke cycle decreased by 15%-20%, while propulsion efficiency was notably enhanced. The framework generates Pareto-optimal solutions, offering a spectrum of trade-offs that can be tailored to individual performance goals and physical constraints. This approach provides a quantitative, data-driven alternative to traditional training methods, enabling personalized and informed decision-making for athletes and coaches. Beyond breaststroke, the methodology can be extended to other swimming techniques and athletic disciplines, addressing the interplay between performance, efficiency, and safety. This study bridges the gap between theoretical modeling and practical application, offering a scalable and robust solution for optimizing sports performance and reducing injury risks.
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[1] Barbosa, T. M., Fernandes, R. J., Keskinen, K. L., & Vilas-Boas, J. P. (2008). The influence of stroke mechanics into energy cost of elite swimmers. European Journal of Applied Physiology, 103(2), 139–149. doi:10.1007/s00421-008-0676-z.
[2] Toussaint, H., & Truijens, M. (2005). Biomechanical aspects of peak performance in human swimming. Animal Biology, 55(1), 17–40. doi:10.1163/1570756053276907.
[3] Chatard, J. C., & Wilson, B. (2003). Drafting distance in swimming. Medicine and Science in Sports and Exercise, 35(7), 1176–1181. doi:10.1249/01.MSS.0000074564.06106.1F.
[4] López-Belmonte, Ó., Ruiz-Navarro, J. J., Gay, A., Cuenca-Fernández, F., Cejuela, R., & Arellano, R. (2023). Determinants of 1500-m Front-Crawl Swimming Performance in Triathletes: Influence of Physiological and Biomechanical Variables. International Journal of Sports Physiology and Performance, 18(11), 1328–1335. doi:10.1123/ijspp.2023-0157.
[5] Psycharakis, S. G., & McCabe, C. (2011). Shoulder and hip roll differences between breathing and non-breathing conditions in front crawl swimming. Journal of Biomechanics, 44(9), 1752–1756. doi:10.1016/j.jbiomech.2011.04.004.
[6] Bixler, B., Pease, D., & Fairhurst, F. (2007). The accuracy of computational fluid dynamics analysis of the passive drag of a male swimmer. Sports Biomechanics, 6(1), 81–98. doi:10.1080/14763140601058581.
[7] Takagi, H., Nakashima, M., Sengoku, Y., Tsunokawa, T., Koga, D., Narita, K., Kudo, S., Sanders, R., & Gonjo, T. (2023). How do swimmers control their front crawl swimming velocity? Current knowledge and gaps from hydrodynamic perspectives. Sports Biomechanics, 22(12), 1552–1571. doi:10.1080/14763141.2021.1959946.
[8] Gatta, G., Cortesi, M., Fantozzi, S., & Zamparo, P. (2015). Planimetric frontal area in the four swimming strokes: Implications for drag, energetics and speed. Human Movement Science, 39, 41–54. doi:10.1016/j.humov.2014.06.010.
[9] Basri, E. I., Basri, A. A., & Ahmad, K. A. (2023). Computational Fluid Dynamics Analysis in Biomimetics Applications: A Review from Aerospace Engineering Perspective. Biomimetics, 8(3), 319. doi:10.3390/biomimetics8030319.
[10] Kolmogorov, S. V., & Duplishcheva, O. A. (1992). Active drag, useful mechanical power output and hydrodynamic force coefficient in different swimming strokes at maximal velocity. Journal of Biomechanics, 25(3), 311–318. doi:10.1016/0021-9290(92)90028-Y.
[11] Silva, A. F., Seifert, L., Fernandes, R. J., Vilas Boas, J. P., & Figueiredo, P. (2025). Front crawl swimming coordination: a systematic review. Sports Biomechanics, 24(2), 127–146. doi:10.1080/14763141.2022.2125428.
[12] Nicol, E., Pearson, S., Saxby, D., Minahan, C., & Tor, E. (2022). Stroke Kinematics, Temporal Patterns, Neuromuscular Activity, Pacing and Kinetics in Elite Breaststroke Swimming: A Systematic Review. Sports Medicine - Open, 8(1), 75. doi:10.1186/s40798-022-00467-2.
[13] Barbosa, T. M., Barbosa, A. C., Simbaña Escobar, D., Mullen, G. J., Cossor, J. M., Hodierne, R., Arellano, R., & Mason, B. R. (2021). The role of the biomechanics analyst in swimming training and competition analysis. Sports Biomechanics, 22(12), 1734–1751. doi:10.1080/14763141.2021.1960417.
[14] Pais, A. I., Belinha, J., & Alves, J. L. (2023). Advances in Computational Techniques for Bio-Inspired Cellular Materials in the Field of Biomechanics: Current Trends and Prospects. Materials, 16(11), 3946. doi:10.3390/ma16113946.
[15] Dong, D., Butler, R., & Herbert, J. (2022). Evaluation of the effectiveness of developing real-world software projects as a motivational device for bridging theory and practice. Journal of Further and Higher Education, 46(9), 1275–1289. doi:10.1080/0309877X.2022.2070727.
[16] Nicol, E., Adani, N., Lin, B., & Tor, E. (2024). The temporal analysis of elite breaststroke swimming during competition. Sports Biomechanics, 23(10), 1692–1704. doi:10.1080/14763141.2021.1975810.
[17] Hollander, A. P., De Groot, G., Van Ingen Schenau, G. J., Toussaint, H. M., De Best, H., Peeters, W., Meulemans, A., & Schreurs, A. W. (1986). Measurement of active drag during crawl arm stroke swimming. Journal of Sports Sciences, 4(1), 21–30. doi:10.1080/02640418608732094.
[18] Lopes, T. J., Morais, J. E., Pinto, M. P., & Marinho, D. A. (2022). Numerical and experimental methods used to evaluate active drag in swimming: A systematic narrative review. Frontiers in Physiology, 13, 938658. doi:10.3389/fphys.2022.938658.
[19] Bilinauskaite, M., Mantha, V. R., Rouboa, A. I., Ziliukas, P., & Silva, A. J. (2013). Computational fluid dynamics study of swimmer’s hand velocity, orientation, and shape: Contributions to hydrodynamics. BioMed Research International, 2013, 140487. doi:10.1155/2013/140487.
[20] Green, M. H., Curet, O. M., Patankar, N. A., & Hale, M. E. (2013). Fluid dynamics of the larval zebrafish pectoral fin and the role of fin bending in fluid transport. Bioinspiration & Biomimetics, 8(1), 16002. doi:10.1088/1748-3182/8/1/016002.
[21] Li, M., Yang, S., Zheng, J., & Liu, X. (2014). ETEA: A Euclidean Minimum Spanning Tree-Based Evolutionary Algorithm for Multi-Objective Optimization. Evolutionary Computation, 22(2), 189–230. doi:10.1162/EVCO_a_00106.
[22] Vennell, R., Pease, D., & Wilson, B. (2006). Wave drag on human swimmers. Journal of Biomechanics, 39(4), 664–671. doi:10.1016/j.jbiomech.2005.01.023.
[23] Zaïdi, H., Taïar, R., Fohanno, S., & Polidori, G. (2008). Analysis of the effect of swimmer’s head position on swimming performance using computational fluid dynamics. Journal of Biomechanics, 41(6), 1350-1358. doi:10.1016/j.jbiomech.2008.02.005.
[24] Do, T. A., & Nguyen, T. H. (2024). Assessment of Fluid Forces on Flooded Bridge Superstructures Using the SPH Method. Civil Engineering Journal, 10(12), 4104–4116. doi:10.28991/CEJ-2024-010-12-019
[25] Novais, M., Silva, A., Mantha, V., Ramos, R., Rouboa, A., Vilas-Boas, J., Luís, S., & Marinho, D. (2012). The effect of depth on drag during the streamlined glide: A three-dimensional CFD analysis. Journal of Human Kinetics, 33(1), 55–62. doi:10.2478/v10078-012-0044-2.
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