Optimization design of biomechanical parameters based on advanced mathematical modelling
Abstract
In recent years the use of biomechanics in athletic training and performance has received a lot of attention, especially in university sports programs. Biomechanics is the study of the mechanical principles that control how biological things move or are constructed. It is critical for understanding the intricate relationships between physical performance, body mechanics, and injury prevention. The objective of this study is to establish how biomechanical variables can be designed and optimized in universities using mathematical modeling. In this study, a novel Emperor Penguin Search-driven Dynamic Feedforward Neural Network (EPSO-DFNN) is proposed to optimize the biomechanical parameters of athletes. Various biomechanical data are utilized from athletes participating in different sports. Biomechanical parameters include muscle activation patterns, joint angles, forces, and movement. The data was preprocessed using Z-score normalization from the obtained data. The Fast Fourier Transform (FFT) using features is extracted from preprocessed data. The proposed method is to identify the optimal configurations for athlete’s movements tailored to their sports and individual biomechanical profiles. The proposed method is the performance of various evaluation metrics such as F1-score (92.76%), precision (91.42%), accuracy (90.02%), and recall (89.69%). The result demonstrated that the proposed method effectively improved the performance in athletic capabilities compared to other traditional algorithms. This study demonstrates how mathematical modeling may be used to optimize biomechanical characteristics, providing insightful information that can be used to improve athletic performance and encourage safer behaviors in athletic settings.
References
1. Yeadon, M.R. and Pain, M.T.G., 2023. Fifty years of performance‐related sports biomechanics research. Journal of Biomechanics, 155, p.111666.
2. Plakias, S., Tsatalas, T., Mina, M.A., Kokkotis, C., Kellis, E. and Giakas, G., 2024. A bibliometric analysis of soccer biomechanics. Applied Sciences, 14(15), p.6430.
3. Shan, G., 2023. Exploring the intersection of equipment design and human physical ability: Leveraging biomechanics, ergonomics/anthropometry, and wearable technology for enhancing human physical performance. Advanced Design Research, 1(1), pp.7-11.
4. Jaitner, T., 2024. Sports Measurement and Information Systems and Biomechanical Feedback Training. In Sports Technology: Technologies, Fields of Application, Sports Equipment and Materials for Sport (pp. 141-149). Berlin, Heidelberg: Springer Berlin Heidelberg.
5. Qiu, Y., Guan, Y. and Liu, S., 2023. The analysis of infrared high-speed motion capture system on motion aesthetics of aerobics athletes under biomechanics analysis. Plos one, 18(5), p.e0286313.
6. Hernández-Beltrán, V., Espada, M.C., Muñoz-Jiménez, J., León, K., Ferreira, C.C., Parraca, J.A. and Gamonales, J.M., 2023. Evolution of Documents Related to Biomechanics Research in Gymnastics. Biomechanics, 3(4), pp.477-492.
7. Zhang, Z., 2024. Quantum sensors in sports biomechanics in revolutionizing injury prevention. Optical and Quantum Electronics, 56(3), p.311.
8. Stewart, H.E., 2024. Identifying Injury Risk, Improving Performance, and Facilitating Learning Using an Integrated Biomechanics Informatics System (IBIS) (Doctoral dissertation, University of Southern California).
9. Lloyd, D.G., Jonkers, I., Delp, S.L. and Modenese, L., 2023. The history and future of neuromusculoskeletal biomechanics. Journal of Applied Biomechanics, 39(5), pp.273-283.
10. Sorgente, V. and Minciacchi, D., 2023. Efficiency in Kinesiology: Innovative Approaches in Enhancing Motor Skills for Athletic Performance. Journal of Functional Morphology and Kinesiology, 8(3), p.111.
11. Carbonaro, D., Gallo, D., Morbiducci, U., Audenino, A. and Chiastra, C., 2021. In silico biomechanical design of the metal frame of transcatheter aortic valves: multi-objective shape and cross-sectional size optimization. Structural and Multidisciplinary Optimization, 64(4), pp.1825-1842.
12. Yan, S., Chen, J. and Huang, H., 2022. Biomechanical Analysis of Martial Arts Movements Based on Improved PSO Optimized Neural Network. Mobile Information Systems, 2022(1), p.8189426.
13. Karnik, N., Bhadri, K., Bora, U., Joshi, S. and Dhatrak, P., 2021. A mathematical model for biomechanical evaluation of micro-motion in dental prosthetics using vibroacoustic RFA. Journal of Medical and Biological Engineering, 41(4), pp.571-580.
14. Galbusera, F., Cina, A., Panico, M., Albano, D. and Messina, C., 2020. Image-based biomechanical models of the musculoskeletal system. European radiology experimental, 4(1), p.49.
15. Peyroteo, M.M.A., Belinha, J. and Jorge, R.N., 2021. A mathematical biomechanical model for bone remodeling integrated with a radial point interpolating meshless method. Computers in Biology and Medicine, 129, p.104170.
16. Regazzoni, F., Dedè, L. and Quarteroni, A., 2020. Biophysically detailed mathematical models of multiscale cardiac active mechanics. PLoS Computational Biology, 16(10), p.e1008294.
17. Olivas-Padilla, B.E., Manitsaris, S., Menychtas, D. and Glushkova, A., 2021. Stochastic-biomechanic modeling and recognition of human movement primitives, in industry, using wearables. Sensors, 21(7), p.2497.
18. Fahmy, M.A., 2022. A computational model for nonlinear biomechanics problems of FGA biological soft tissues. Applied Sciences, 12(14), p.7174.
19. Harato, K., Morishige, Y., Kobayashi, S., Niki, Y. and Nagura, T., 2022. Biomechanical features of drop vertical jump are different among various sporting activities. BMC Musculoskeletal Disorders, 23(1), p.331.
20. Rozhkov, V., Pavlenko, V., Okun, D., Shutieiev, V., Shutieieva, T. and Olga, L., 2020. Relationship between the biomechanical parameters technique for preliminary swings among elite hammer throwers. Journal of Physical Education and Sport, 20, pp.2258-2262.
21. Prigent, G., Apte, S., Paraschiv-Ionescu, A., Besson, C., Gremeaux, V. and Aminian, K., 2022. Concurrent evolution of biomechanical and physiological parameters with running-induced acute fatigue. Frontiers in physiology, 13, p.814172.
22. Hilhorst, P.L., Quicken, S., van de Vosse, F.N. and Huberts, W., 2024. Efficient sensitivity analysis for biomechanical models with correlated inputs. International Journal for Numerical Methods in Biomedical Engineering, 40(2), p.e3797.
23. Bhardawaj, S., Sharma, R.C., Sharma, S.K., Gopala Rao, L.V.V. and Vashist, A., 2023. Modeling of biomechanical human body model for seat-to-head transmissibility analysis. Noise & Vibration Worldwide, 54(2-3), pp.62-74.
24. Kou, W., Liang, Y., Wang, Z., Liang, Q., Sun, L. and Kuang, S., 2023. An integrated method of biomechanics modeling for pelvic bone and surrounding soft tissues. Bioengineering, 10(6), p.736.
25. Bose, D., Arora, B., Srivastava, A.K. and Garg, P., 2024, May. A Computer Vision Based Framework for Posture Analysis and Performance Prediction in Athletes. In 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE) (pp. 942-947). IEEE.
Copyright (c) 2024 Yuan Wen
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