Abstract
In the dynamic world of fitness, the quest for tailor-made exercise plans has never been more critical. Expert systems are stepping up to this challenge, offering a promise of personalized routines for both seasoned athletes and newcomers. In this article, we introduce an ingenious fuzzy-Bayesian expert system, armed with fuzzy and Bayesian logic, that tackles the uncertainties lurking within fitness data. It's a system with a dual focus: for fitness veterans, it conducts a meticulous analysis of their training history, past achievements, and current ambitions. Guided by the nuanced power of fuzzy and Bayesian logic, it makes decisions rooted in probabilities. As for beginners, the system builds a strong foundation, considering your present fitness status and your future aspirations. This forward-thinking approach speaks directly to the increasing demand for adaptable fitness solutions. For seasoned athletes, it paves the way to peak performance and injury prevention. Meanwhile, beginners embark on their fitness journey with gradual and secure steps. The Fuzzy-Bayesian Expert System offers a comprehensive answer that embraces data diversity and individual fitness aspirations in both scenarios. Furthermore, the seamless integration of the UPAFuzzySystems library and Twilio elevates the system's functionality and its connection with users, turning it into a personal fitness enhancement tool. Dive into a world where fitness meets technology for a custom-made future of health and well-being.
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Ortega, R.L.E. (2024). Fuzzy-Bayesian Expert System for Suggesting Personalized Training Plans with Exercises and Routines. In: Calvo, H., MartÃnez-Villaseñor, L., Ponce, H., Zatarain Cabada, R., Montes Rivera, M., Mezura-Montes, E. (eds) Advances in Computational Intelligence. MICAI 2023 International Workshops. MICAI 2023. Lecture Notes in Computer Science(), vol 14502. Springer, Cham. https://doi.org/10.1007/978-3-031-51940-6_17
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