Fuzzy Decision Support Systems to Improve the Effectiveness of Training Programs in the Field of Sports Fitness

• Published in: International journal of computational intelligence systems Vol. 17; no. 1; pp. 1 - 14
• Main Author: An, Peng
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 27-06-2024; Springer
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Decision support systems; Engineering; Fuzzy logic; Mamdani inference; Mathematical Logic and Foundations; Mechatronics; Research Article; Robotics; Sports fitness; Training programs; Decision support systems; Fuzzy logic; Sports fitness; Mamdani inference; Training programs
• ISSN: 1875-6883; 1875-6883
• DOI: 10.1007/s44196-024-00555-z

To maximize training programs and improve individual performance, the sports fitness profession is always looking for new and innovative solutions. Fuzzy decision support systems provide a strong basis for improving training regimen efficacy through flexible and adaptive decision-making. In sports fitness, doing things differently might affect the way one can train and even lead to injury. Particularly if exercises call for expert training regimens, determining if fitness requirements are sufficiently satisfied is a difficult task. Athletes participate in training programs focusing on strengthening their bodies and minds to perform better. Each sport has unique needs, and athletes should prepare to meet those demands while still meeting their sport’s general fitness activity requirements. A novel strategy for assisting athletes with fitness-related decision-making is presented in this study as the Fitness Mamdani Decision System. At the outset, the system provides an adaptive decision framework to improve the effectiveness of training programs by applying the principles of intuitionistic fuzzy numbers and fuzzy logic. The study wants to know how to train effectively, and it takes important factors like your mood, degree of preparation, sleep quality, and stress levels into account. Using language-specific terminology and triangle membership functions, the Mamdani fuzzy inference system generates rules based on analyzing these crucial elements. Regarding measures like adaptability index, training load capacity, long-term program efficiency, and participation ratio among sports fitness individuals, the system is guided by fuzzy rules that infer decisions.