Enhanced Adaptive Rough Decision Optimization for Athletic Training Periodization: A Computational Framework
Abstract
Choosing the most effective periodization strategy in athletic training is essential for enhancing performance and reducing the likelihood of overtraining. This paper proposes a novel method, the Enhanced Adaptive Rough Decision Optimization (EARDO) Algorithm, for evaluating and ranking periodization strategies. The EARDO algorithm is designed to accommodate the dynamic, multifactorial nature of athletic training, where training load impact depends on factors such as daily variability, recovery, individual responses, and intensity. The algorithm integrates adaptive rough set theory to handle uncertainty and captures the trade-offs in performance gains and injury risks. The effectiveness of the EARDO approach was evaluated through computational experiments on three periodization strategies—linear, undulating, and block. The results showed that EARDO could accurately determine the optimal training load for each athlete (98.75%), assess overtraining risk (98.5%), and identify overtraining periods (98.35%). Comparisons with existing fuzzy logic and rough set methods revealed a substantial improvement in accuracy (6-8% higher) for selecting optimal periodization strategies and predicting overtraining and injury risks. These findings suggest that the EARDO algorithm offers a more precise, flexible, and adaptable framework for optimizing athletic training.References
J. Smith and E. Taylor, “Athletic training periodization strategies: A comprehensive review,”
Journal of Sports Science and Medicine, vol. 20,
no. 2, pp. 100–110, 2021.
S. Miller and W. Zhang, “Rough set theory applications in athletic performance assessment,”
Journal of Applied Sports Research, vol. 31, no. 4,
pp. 320–332, 2023.
R. Williams and A. Patel, “Bipolar fuzzy sets in decision-making for complex systems,” IEEE
Transactions on Fuzzy Systems, vol. 29, no. 5, pp.675–689, 2021.
A. Karahano˘glu, A. Coskun, D. Postma, B. L.Scheltinga, R. Gouveia, D. Reidsma, and
J. Reenalda, “Is it just a score? understanding training load management practices beyond sports tracking,” in Proceedings of the CHI Conference on Human Factors in Computing Systems,
, pp. 1–18
L. Anderson and S. Thompson, “Decision-making models for athlete performance optimization,”Sports Engineering, vol. 24, no. 1, pp. 12–25, 2022.
D. Lee and M. Garcia, “Bipolar rough sets in personalized athletic training programs,” Journal of Athletic Performance, vol. 36, no. 2, pp. 222–235,2023.
O. Adams and H. Mitchell, “Fuzzy decision-making in athletic training: Challenges and future directions,” International Journal of Sports Medicine, vol. 40, no. 3, pp. 203–214, 2022.
D. Roberts and F. Carter, “Training optimization through hybrid fuzzy-rough models: A case study,” Journal of Sports and Exercise Science,vol. 28, no. 1, pp. 50–65, 2024.
Z. Tian and J. Wang, “Design and implementation of training plan optimization for athletes in track and field competitions using a genetic algorithm,” Journal of Electrical Systems, vol. 20,no. 9s, pp. 754–760, 2024.
M. Johnson and C. Lee, “Fuzzy multi-criteria decision-making approaches in sports science,”
International Journal of Sports Analytics, vol. 12,
no. 3, pp. 45–57, 2022.
O. Davies and J. King, “A review of periodization strategies in modern sports training,” Journal of Athletic Training Science, vol. 18, no. 4, pp. 390–405, 2021.
K. Hayat, M. S. Raja, E. Lughofer, and N. Yaqoob, “New group-based generalized interval-valued q-rung orthopair fuzzy soft aggregation operators and their applications in sports decision-making problems,” Computational and Applied Mathematics, vol. 42, no. 1, p. 4, 2023.
M. Qiyas, M. Naeem, N. Khan, S. Khan, and F. Khan, “Confidence levels bipolar complex fuzzy aggregation operators and their application in decision making problem,” IEEE Access, 2024.
W. Hu, B. Li, C. Li, and T. Zhang, “An integrated intelligent decision systems for physical health evaluation of college students with fuzzy number intuitionistic fuzzy information,” Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp.611–624, 2023.
A. Fahmi, M. Aslam, and R. Ahmed, “Decision-making problem based on generalized interval- valued bipolar neutrosophic einstein fuzzy aggregation operator,” Soft Computing, vol. 27, no. 20,pp. 14 533–14 551, 2023.
A. A. Rahim, S. N. Musa, S. Ramesh, and M. K. Lim, “Development of a fuzzy-topsis multi criteria decision-making model for material selection with the integration of safety, health and environment risk assessment,” Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, vol.235, no. 7, pp. 1532–1550, 2021.
G. Ali, M. Sarwar, and M. Nabeel, “Novel group decision-making method based on interval-valued m-polar fuzzy soft expert information,” Neural Computing and Applications, vol. 35, no. 30, pp.22 313–22 340, 2023.
S. Pant, P. Garg, A. Kumar, M. Ram, A. Kumar, H. K. Sharma, and Y. Klochkov, “Ahp-
based multi-criteria decision-making approach for monitoring health management practices in smart healthcare system,” International Journal of System Assurance Engineering and Management,vol. 15, no. 4, pp. 1444–1455, 2024.
F. P. Cardenas Hernandez, J. Schneider,D. Di Mitri, I. Jivet, and H. Drachsler, “Beyond hard workout: A multimodal framework for personalised running training with immersive technologies,” British Journal of Educational Technology, 2024.
P. M. Holmberg, S. Russell, K. A. O’Brien, L. P. James, and V. G. Kelly, “Exploring strength and
conditioning practitioners’ perceptions about using priming exercise as a pre-competition strategy to improve performance,” International Journal of Sports Science & Coaching, vol. 19, no. 4, pp.1598–1611, 2024.
R. Tipireddy, V. C. Amatya, W. S. Rosenthal,and M. Subramanian, “Sequential decision mak-
ing (sdm) for mesh refinement and model selection in multiscale, multi-physics applications,”Pacific Northwest National Laboratory (PNNL),Richland, WA (United States), Tech. Rep., 2023.
D. Sahoo, P. K. Parida, and B. Pati, “Efficient fuzzy multi-criteria decision-making for optimal college location selection: A comparative study of min–max fuzzy topsis approach,” Results in Control and Optimization, vol. 15, p. 100422, 2024.
K. R. Harrison, S. Elsayed, I. L. Garanovich,T. Weir, S. G. Boswell, and R. A. Sarker, Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling. Springer, 2022.
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